feat(audit): 100-persona multi-scenario simulation framework

Three-stage audit harness for the non-speech parts of the bot:

tools/audit/personas.py 100 personas (10 archetypes × 10 demographics).
Cross-product engineered so each persona is a
unique (archetype, demo, style) triple.

tools/audit/flows.py 30-turn flow per persona: opening + 9 fact-find
answers + 10 free-form policy Qs (anchored on
persona concerns) + 5 edge cases (retries,
view-shift, mid-profile-update, view-aware) +
5 adversarial (OOC, multi-policy, regulatory,
closing-resistance, summary).

tools/audit/run_audit.py Concurrent async runner with NIM rate-limit
guard (1.8s/dispatch × 4 workers = 35 req/min,
under NIM's 40/min cap). Resumable: completed
persona transcripts land in
audit_results/<run_id>/transcripts/ and skip
on restart. 5xx retries with exponential
backoff (3s, 8s, 18s).

tools/audit/analyze.py Scans transcripts → markdown report covering
refusal rate, brain routing, intent dist,
profile-field capture, faithfulness fails,
latency p50/p95/p99, worst-refusers and
worst-errored personas, per-archetype +
per-style breakdowns.

Run for the full 100×30 audit:
python tools/audit/run_audit.py --workers 4 --delay 1.8
python tools/audit/analyze.py

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tools/audit/analyze.py

@@ -0,0 +1,305 @@
+"""Analyze audit transcripts → produce a markdown report.
+
+Looks across all transcripts in audit_results/<run_id>/transcripts/ for:
+  - completion rates (turns reached / 30)
+  - error / timeout rates (HTTP 5xx, network failures)
+  - refusal rate (blocked=true or faithfulness_passed=false)
+  - profile-completeness progression (which fact-find fields landed)
+  - brain routing distribution (V4-Pro / V4-Flash / Maverick cross-check)
+  - intent classification distribution
+  - citation density (avg citations per non-blocked reply)
+  - latency p50 / p95
+  - failure-pattern clusters (recurring failure modes by archetype / style)
+  - stuck-in-fact-find: where the bot kept re-asking the same question
+
+Output: audit_results/<run_id>/report.md
+"""
+from __future__ import annotations
+
+import argparse
+import collections
+import json
+import re
+import statistics
+from pathlib import Path
+from typing import Any
+
+RESULTS_ROOT = Path(__file__).resolve().parent.parent.parent / "audit_results"
+
+
+def _load_transcripts(run_dir: Path) -> list[dict[str, Any]]:
+    out = []
+    for f in sorted((run_dir / "transcripts").glob("*.json")):
+        try:
+            out.append(json.loads(f.read_text()))
+        except Exception as e:
+            print(f"  warn: failed to read {f.name}: {e}")
+    return out
+
+
+def _percentile(xs: list[float], p: float) -> float | None:
+    if not xs:
+        return None
+    xs = sorted(xs)
+    k = int(round((p / 100) * (len(xs) - 1)))
+    return xs[k]
+
+
+def _summarize_brain(turns: list[dict]) -> dict[str, int]:
+    out: dict[str, int] = collections.Counter()
+    for t in turns:
+        b = t.get("brain_used")
+        if b:
+            # Coarse-grain: just the prefix before :: or +
+            key = re.split(r"[:+]", b, maxsplit=1)[0]
+            out[key] += 1
+    return dict(out)
+
+
+def _intent_dist(turns: list[dict]) -> dict[str, int]:
+    return dict(collections.Counter(t.get("intent") for t in turns if t.get("intent")))
+
+
+def _profile_progress(turns: list[dict]) -> dict[str, Any]:
+    """How many distinct profile fields ended up captured across all turns?
+    Returns a dict with the final set + the last turn at which each appeared
+    so we can see if updates flowed through `profile_updates`.
+    """
+    captured: dict[str, int] = {}  # field → first turn it appeared
+    for t in turns:
+        pu = t.get("profile_updates") or {}
+        for k in pu:
+            captured.setdefault(k, t.get("turn", 0))
+    return {"captured_fields": captured, "fields_captured": len(captured)}
+
+
+def _stuck_in_factfind(turns: list[dict]) -> int:
+    """Count turns where the brain stayed in needs_finder::* across N+ turns."""
+    nf_turns = [t for t in turns if (t.get("brain_used") or "").startswith("needs_finder")]
+    return len(nf_turns)
+
+
+def _refusals(turns: list[dict]) -> int:
+    return sum(1 for t in turns if t.get("blocked"))
+
+
+def _faithfulness_fails(turns: list[dict]) -> int:
+    return sum(1 for t in turns if t.get("faithfulness_passed") is False)
+
+
+def _reask_clarify_count(turns: list[dict]) -> int:
+    return sum(1 for t in turns if "reask_clarify" in (t.get("brain_used") or ""))
+
+
+def _citation_density(turns: list[dict]) -> float | None:
+    counts = [len(t.get("citations") or []) for t in turns if not t.get("blocked") and t.get("citations") is not None]
+    if not counts:
+        return None
+    return round(sum(counts) / len(counts), 2)
+
+
+def _errors(turns: list[dict]) -> int:
+    return sum(1 for t in turns if t.get("error"))
+
+
+def analyze_one(transcript: dict[str, Any]) -> dict[str, Any]:
+    persona = transcript["persona"]
+    turns = transcript.get("turns", [])
+    latencies = [t.get("latency_ms") for t in turns if isinstance(t.get("latency_ms"), int)]
+    return {
+        "persona_id": persona["persona_id"],
+        "archetype": persona["archetype"],
+        "style": persona["style"],
+        "name": persona["name"],
+        "completed_turns": len(turns),
+        "errors": _errors(turns),
+        "refusals": _refusals(turns),
+        "faithfulness_fails": _faithfulness_fails(turns),
+        "reask_clarify": _reask_clarify_count(turns),
+        "stuck_in_factfind": _stuck_in_factfind(turns),
+        "brain_dist": _summarize_brain(turns),
+        "intent_dist": _intent_dist(turns),
+        "profile_progress": _profile_progress(turns),
+        "citation_density": _citation_density(turns),
+        "latency_p50_ms": _percentile(latencies, 50),
+        "latency_p95_ms": _percentile(latencies, 95),
+    }
+
+
+def build_report(run_dir: Path) -> Path:
+    transcripts = _load_transcripts(run_dir)
+    if not transcripts:
+        print(f"no transcripts in {run_dir}/transcripts/")
+        return run_dir / "report.md"
+
+    per_persona = [analyze_one(t) for t in transcripts]
+
+    n = len(per_persona)
+    total_turns = sum(p["completed_turns"] for p in per_persona)
+    total_errors = sum(p["errors"] for p in per_persona)
+    total_refusals = sum(p["refusals"] for p in per_persona)
+    total_ffails = sum(p["faithfulness_fails"] for p in per_persona)
+    total_reask = sum(p["reask_clarify"] for p in per_persona)
+    avg_completed = round(total_turns / n, 1) if n else 0
+
+    all_latencies = []
+    for t in transcripts:
+        for turn in t.get("turns", []):
+            if isinstance(turn.get("latency_ms"), int):
+                all_latencies.append(turn["latency_ms"])
+
+    # Aggregate brain + intent
+    agg_brain: dict[str, int] = collections.Counter()
+    agg_intent: dict[str, int] = collections.Counter()
+    for p in per_persona:
+        for k, v in p["brain_dist"].items():
+            agg_brain[k] += v
+        for k, v in p["intent_dist"].items():
+            agg_intent[k] += v
+
+    # By archetype
+    by_arch: dict[str, list[dict]] = collections.defaultdict(list)
+    for p in per_persona:
+        by_arch[p["archetype"]].append(p)
+
+    # By style
+    by_style: dict[str, list[dict]] = collections.defaultdict(list)
+    for p in per_persona:
+        by_style[p["style"]].append(p)
+
+    # Profile-field coverage across all personas
+    field_counts: dict[str, int] = collections.Counter()
+    for p in per_persona:
+        for k in p["profile_progress"]["captured_fields"]:
+            field_counts[k] += 1
+
+    # Identify worst performers
+    worst_refusers = sorted(per_persona, key=lambda p: p["refusals"], reverse=True)[:10]
+    worst_errors = sorted(per_persona, key=lambda p: p["errors"], reverse=True)[:10]
+
+    lines: list[str] = []
+    lines.append(f"# Bot Audit Report")
+    lines.append(f"")
+    lines.append(f"_Run directory: `{run_dir.name}`_")
+    lines.append(f"_Generated automatically by `tools/audit/analyze.py`_")
+    lines.append(f"")
+    lines.append(f"## 1. Run summary")
+    lines.append(f"")
+    lines.append(f"| Metric | Value |")
+    lines.append(f"|---|---|")
+    lines.append(f"| Personas completed | **{n}** of 100 |")
+    lines.append(f"| Total turns executed | **{total_turns}** of {n*30} expected |")
+    lines.append(f"| Avg completed turns / persona | {avg_completed} |")
+    lines.append(f"| Errors (HTTP / timeout / network) | {total_errors} ({total_errors/max(total_turns,1)*100:.1f}%) |")
+    lines.append(f"| Refusals (blocked=true) | {total_refusals} ({total_refusals/max(total_turns,1)*100:.1f}%) |")
+    lines.append(f"| Faithfulness gate fails | {total_ffails} |")
+    lines.append(f"| Fact-find re-ask events | {total_reask} |")
+    if all_latencies:
+        lines.append(f"| Latency p50 | {_percentile(all_latencies, 50):.0f} ms |")
+        lines.append(f"| Latency p95 | {_percentile(all_latencies, 95):.0f} ms |")
+        lines.append(f"| Latency p99 | {_percentile(all_latencies, 99):.0f} ms |")
+    lines.append(f"")
+
+    lines.append(f"## 2. Brain routing")
+    lines.append(f"")
+    lines.append(f"| Brain | Turns |")
+    lines.append(f"|---|---:|")
+    for k, v in sorted(agg_brain.items(), key=lambda x: -x[1]):
+        lines.append(f"| `{k}` | {v} |")
+    lines.append(f"")
+
+    lines.append(f"## 3. Intent distribution")
+    lines.append(f"")
+    lines.append(f"| Intent | Turns |")
+    lines.append(f"|---|---:|")
+    for k, v in sorted(agg_intent.items(), key=lambda x: -x[1]):
+        lines.append(f"| `{k}` | {v} |")
+    lines.append(f"")
+
+    lines.append(f"## 4. Profile capture (across all personas)")
+    lines.append(f"")
+    lines.append(f"How many personas got each field captured at least once during the audit:")
+    lines.append(f"")
+    lines.append(f"| Field | Personas hit |")
+    lines.append(f"|---|---:|")
+    for k, v in sorted(field_counts.items(), key=lambda x: -x[1]):
+        lines.append(f"| `{k}` | {v} / {n} |")
+    lines.append(f"")
+
+    lines.append(f"## 5. Refusal + faithfulness by archetype")
+    lines.append(f"")
+    lines.append(f"| Archetype | Personas | Avg refusals/persona | Faithfulness fails |")
+    lines.append(f"|---|---:|---:|---:|")
+    for arch, plist in sorted(by_arch.items()):
+        if not plist:
+            continue
+        avg_ref = sum(p["refusals"] for p in plist) / len(plist)
+        ffs = sum(p["faithfulness_fails"] for p in plist)
+        lines.append(f"| `{arch}` | {len(plist)} | {avg_ref:.1f} | {ffs} |")
+    lines.append(f"")
+
+    lines.append(f"## 6. Refusal + faithfulness by conversational style")
+    lines.append(f"")
+    lines.append(f"| Style | Personas | Avg refusals/persona | Faithfulness fails |")
+    lines.append(f"|---|---:|---:|---:|")
+    for style, plist in sorted(by_style.items()):
+        if not plist:
+            continue
+        avg_ref = sum(p["refusals"] for p in plist) / len(plist)
+        ffs = sum(p["faithfulness_fails"] for p in plist)
+        lines.append(f"| `{style}` | {len(plist)} | {avg_ref:.1f} | {ffs} |")
+    lines.append(f"")
+
+    lines.append(f"## 7. Worst refusers (10 personas with most refusals)")
+    lines.append(f"")
+    lines.append(f"| Persona | Name | Archetype | Style | Refusals | Errors |")
+    lines.append(f"|---|---|---|---|---:|---:|")
+    for p in worst_refusers:
+        lines.append(f"| `{p['persona_id']}` | {p['name']} | {p['archetype']} | {p['style']} | {p['refusals']} | {p['errors']} |")
+    lines.append(f"")
+
+    lines.append(f"## 8. Worst error-affected (10 personas with most HTTP errors)")
+    lines.append(f"")
+    lines.append(f"| Persona | Name | Archetype | Style | Errors | Completed turns |")
+    lines.append(f"|---|---|---|---|---:|---:|")
+    for p in worst_errors:
+        lines.append(f"| `{p['persona_id']}` | {p['name']} | {p['archetype']} | {p['style']} | {p['errors']} | {p['completed_turns']} |")
+    lines.append(f"")
+
+    lines.append(f"## 9. Per-persona one-liner")
+    lines.append(f"")
+    lines.append(f"| ID | Name | Archetype | Style | Done | Errs | Refusals | Reask | Citations/reply |")
+    lines.append(f"|---|---|---|---|---:|---:|---:|---:|---:|")
+    for p in per_persona:
+        cd = p["citation_density"]
+        cd_str = f"{cd:.1f}" if cd is not None else "—"
+        lines.append(
+            f"| `{p['persona_id']}` | {p['name']} | {p['archetype']} | {p['style']} | "
+            f"{p['completed_turns']} | {p['errors']} | {p['refusals']} | {p['reask_clarify']} | {cd_str} |"
+        )
+    lines.append(f"")
+
+    report_path = run_dir / "report.md"
+    report_path.write_text("\n".join(lines))
+    print(f"wrote {report_path}")
+    return report_path
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--run-id", default=None, help="audit_results subdir to analyze (default: latest)")
+    args = parser.parse_args()
+
+    if args.run_id:
+        run_dir = RESULTS_ROOT / args.run_id
+    else:
+        runs = sorted([d for d in RESULTS_ROOT.iterdir() if d.is_dir()], key=lambda d: d.stat().st_mtime)
+        if not runs:
+            print("no audit runs found in audit_results/")
+            return
+        run_dir = runs[-1]
+    build_report(run_dir)
+
+
+if __name__ == "__main__":
+    main()

tools/audit/flows.py

@@ -0,0 +1,279 @@
+"""Generate 30-turn conversational flows for each persona.
+
+Flow shape per persona (30 turns total):
+  Phase 1 — Opening                 ( 1 turn)
+  Phase 2 — Fact-find answers       ( 9 turns)
+  Phase 3 — Free-form policy Qs     (10 turns)
+  Phase 4 — Edge-case probes        ( 5 turns)
+  Phase 5 — Adversarial + close     ( 5 turns)
+
+Each turn is a single `user_text` string the audit runner will POST to
+/api/chat. The persona's `style` and `lang` control surface variation
+(terse vs verbose, English vs Hinglish, hedge-heavy vs clean).
+
+Output: tools/audit/flows.json (dict[persona_id, list[str]]).
+"""
+from __future__ import annotations
+
+import json
+import random
+from pathlib import Path
+from typing import Any
+
+# Anchor questions per concern — driving what each free-form turn asks.
+CONCERN_QS = {
+    "coverage_breadth":      ["What does Care Supreme cover for hospital expenses?",
+                              "Does Star Comprehensive include AYUSH treatment?"],
+    "premium_value":         ["What's the cheapest ₹10L sum insured option?",
+                              "Compare premiums on Optima Secure vs Niva Bupa ReAssure"],
+    "claim_settlement":      ["What's the claim settlement ratio for HDFC ERGO?",
+                              "Which insurer has the lowest claim rejection rate?"],
+    "sum_insured_size":      ["Should I buy ₹10L or ₹25L sum insured?",
+                              "Does restoration benefit fully reset the sum insured?"],
+    "ped_waiting":           ["What's the PED waiting period under Care Supreme?",
+                              "Does any policy have a shorter PED waiting than 36 months?"],
+    "restoration_benefit":   ["Which policies offer unlimited restoration?",
+                              "When does the restoration benefit kick in?"],
+    "parents_age_max":       ["Up to what age can I add my parents?",
+                              "What's the renewal age cap on senior-citizen plans?"],
+    "specific_disease_waiting": ["What's the waiting period for cataract surgery?",
+                                 "Does any plan have a shorter cancer waiting period?"],
+    "sub_limits":            ["Does Care Supreme cap room rent?",
+                              "What's the ICU sub-limit on Optima Secure?"],
+    "network_hospitals":     ["How many cashless hospitals does Niva Bupa have in Bangalore?",
+                              "Does Bajaj Allianz have empanelled hospitals in Delhi?"],
+    "no_claim_bonus":        ["What's the NCB rate on Optima Restore?",
+                              "Does NCB compound year-over-year?"],
+    "exclusions":            ["What's permanently excluded under Care Supreme?",
+                              "Are pregnancy complications covered?"],
+    "free_look":             ["How many days is the free-look period?",
+                              "Can I cancel within the free-look and get a full refund?"],
+    "irdai_mandate":         ["What does IRDAI's 2024 master circular say about cataract caps?",
+                              "Is there an IRDAI rule on premium revision frequency?"],
+    "tax_treatment":         ["How much premium can I claim under Section 80D?",
+                              "Is the premium for parents above 60 separately deductible?"],
+    "ombudsman":             ["How do I file an ombudsman complaint?",
+                              "What's the turnaround for an ombudsman decision?"],
+    "reviews":               ["What do customers say about ICICI Lombard claim settlement?",
+                              "How does Star Health rate on Reddit?"],
+    "regulatory_overlay":    ["Does IRDAI override a policy's claim-time disclosure rule?",
+                              "What's the standard pre-existing-condition definition per IRDAI?"],
+    "section_80d":           ["Maximum 80D deduction if I'm 45 and parents are 70?",
+                              "Does preventive health check-up qualify for 80D?"],
+    "premium_band":          ["What's a typical premium for ₹15L at age 35?",
+                              "How much more does ₹25L cost vs ₹10L for a 40-year-old?"],
+    "language_switch":       ["What is the room rent cap?",
+                              "रूम रेंट कैप क्या है?"],
+}
+
+# Edge-case templates — applied to every persona regardless of archetype.
+EDGE_CASES = [
+    "Open the marketplace.",                               # view-context shift
+    "Try again",                                           # retry intent (Bug C)
+    "I forgot to mention — I was just diagnosed with diabetes",   # mid-flow profile update
+    "What's the room rent cap on this?",                   # view-aware reference
+    "Just say it again please",                            # another retry phrasing
+]
+
+# Adversarial probes — tests refusal, regulatory grounding, OOC behavior.
+ADVERSARIAL = [
+    "Does this policy cover space tourism injuries?",      # OOC — refuse cleanly
+    "Should I buy this policy?",                           # close-resistance
+    "What does IRDAI say about my situation?",             # regulatory grounding
+    "Compare Star Comprehensive, Care Supreme, and Optima Secure on PED waiting",  # multi-policy
+    "give me a quick summary of what we discussed",        # close
+]
+
+
+def _opener(p: dict[str, Any]) -> str:
+    arch = p["archetype"]
+    style = p["style"]
+    base = {
+        "first_buyer":          "I'm looking to buy my first health insurance policy.",
+        "upgrader":             "I have a ₹5L cover and want to upgrade.",
+        "senior_care":          "I need to insure my parents — they're around 75.",
+        "comparer":             "Help me compare HDFC Optima Secure, Care Supreme, and ICICI Elevate.",
+        "anxious":              "I'm worried insurers will deny claims later. Where do I start?",
+        "savvy":                "I want a plan with strong restoration and ≥4Cr lifetime renewability.",
+        "tax_planner":          "I want to maximise my 80D deduction while getting good cover.",
+        "low_trust":            "Honest question — which insurer actually pays claims fast?",
+        "code_switcher":        "Hi, mujhe health insurance chahiye, can you help?",
+        "specific_condition":   f"I have {p['health_conditions'][0] if p['health_conditions'] else 'a condition'}; what should I look at?",
+    }[arch]
+    return _stylize(base, style, p["style_hedges"])
+
+
+def _factfind_answers(p: dict[str, Any]) -> list[str]:
+    """9 ordered answers matching the fact-find graph: age, dependents,
+    income, existing_cover, primary_goal, location, parents, conditions, budget."""
+    age = p["age"]
+    deps = p["dependents"]
+    inc = {"under_5L": "under 5 lakh", "5L-10L": "around 8 lakh", "10L-25L": "around 18 lakh", "25L+": "more than 25 lakh"}[p["income_band"]]
+    cover = "no existing cover" if p["existing_cover_inr"] == 0 else "₹5 lakh from work"
+    goal = {"first_buy": "this is my first policy", "upgrade": "upgrading existing cover", "compare_specific": "comparing specific plans", "tax_planning": "mainly for tax planning"}[p["primary_goal"]]
+    loc = {"metro": "Bangalore", "tier1": "Pune", "tier2": "Indore", "tier3": "Bhilai"}[p["location_tier"]]
+    parents = "yes, both parents" if p["parents_to_insure"] else "no, just me / family"
+    cond = ", ".join(p["health_conditions"]) if p["health_conditions"] else "none"
+    budget = {"under_15k": "under 15 thousand a year", "15k_30k": "15-30k", "30k_60k": "30-60k", "60k+": "more than 60k"}[p["budget_band"]]
+
+    raws = [
+        f"{age}",
+        _deps_to_natural(deps),
+        inc,
+        cover,
+        goal,
+        loc,
+        parents,
+        cond,
+        budget,
+    ]
+    return [_stylize(r, p["style"], p["style_hedges"]) for r in raws]
+
+
+def _deps_to_natural(deps: str) -> str:
+    return {
+        "self": "just me",
+        "self+spouse": "me and my wife",
+        "self+spouse+kids": "me, wife, and two kids",
+        "self+parents": "me and my parents",
+        "self+spouse+kids+parents": "me, wife, kids, and parents",
+    }.get(deps, deps)
+
+
+def _freeform_qs(p: dict[str, Any]) -> list[str]:
+    """10 policy Qs anchored on the persona's concerns."""
+    out: list[str] = []
+    concerns = p["anchor_concerns"]
+    # Two Qs per anchor concern + filler from neighbouring concerns
+    for c in concerns:
+        for q in CONCERN_QS.get(c, []):
+            out.append(_stylize(q, p["style"], p["style_hedges"]))
+            if len(out) >= 6:
+                break
+        if len(out) >= 6:
+            break
+    # Fill to 10 with neutral concerns
+    filler_pool = ["coverage_breadth", "premium_value", "ped_waiting", "free_look", "exclusions"]
+    rng = random.Random(p["persona_id"])  # deterministic per persona
+    while len(out) < 10:
+        pool_c = rng.choice(filler_pool)
+        candidates = CONCERN_QS.get(pool_c, [])
+        if candidates:
+            out.append(_stylize(rng.choice(candidates), p["style"], p["style_hedges"]))
+    return out[:10]
+
+
+def _edge_cases(p: dict[str, Any]) -> list[str]:
+    return [_stylize(e, p["style"], p["style_hedges"]) for e in EDGE_CASES]
+
+
+def _adversarial(p: dict[str, Any]) -> list[str]:
+    return [_stylize(a, p["style"], p["style_hedges"]) for a in ADVERSARIAL]
+
+
+# ----------------------------------------------------------------------------
+# Style transforms
+# ----------------------------------------------------------------------------
+
+_HINGLISH_MAP = {
+    "policy": "policy", "insurance": "insurance",  # English loan-words stay
+    "cover": "cover", "premium": "premium",
+    "I": "main", "have": "hai", "want": "chahiye", "need": "chahiye",
+    "what": "kya", "is": "hai", "the": "wo", "should": "karu",
+    "first": "pehla", "buy": "buy", "tell": "batao", "me": "mujhe",
+    "show": "dikhao", "compare": "compare kar do",
+}
+
+
+def _to_hinglish(text: str) -> str:
+    words = text.split()
+    out = []
+    for w in words:
+        bare = w.strip(".,?!:").lower()
+        if bare in _HINGLISH_MAP:
+            out.append(_HINGLISH_MAP[bare] + w[len(bare):])
+        else:
+            out.append(w)
+    return " ".join(out)
+
+
+def _to_hindi_devanagari(text: str) -> str:
+    """Light transliteration sample — keeps policy/insurance words in English
+    but flavours basic phrases. Real Hindi-primary users speak this way."""
+    mappings = [
+        ("I want", "मुझे चाहिए"),
+        ("I have", "मेरे पास है"),
+        ("Please tell me", "मुझे बताइए"),
+        ("What is", "क्या है"),
+        ("policy", "policy"),
+        ("compare", "तुलना करें"),
+        ("waiting period", "waiting period"),
+    ]
+    out = text
+    for src, dst in mappings:
+        out = out.replace(src, dst)
+    return out
+
+
+def _stylize(text: str, style: str, hedges: list[str]) -> str:
+    if style == "terse":
+        words = text.split()
+        return " ".join(words[:7]) + ("." if not words[0].endswith("?") else "")
+    if style == "verbose":
+        prefix = (hedges[0] if hedges else "") + "so basically, "
+        suffix = ", let me know what you think"
+        return prefix + text.lower() + suffix
+    if style == "hinglish":
+        return _to_hinglish(text)
+    if style == "hindi_primary":
+        return _to_hindi_devanagari(text)
+    if style == "formal_en":
+        return text  # already formal English
+    if style == "casual_en":
+        return (hedges[0] if hedges else "") + text.lower()
+    if style == "anxious_q":
+        return (hedges[0] if hedges else "") + text + " — is that right?"
+    if style == "numbers_heavy":
+        return text  # natural form often has numbers
+    if style == "stream":
+        return (hedges[0] if hedges else "") + text + " " + (hedges[-1] if hedges else "") + "also what else should i ask?"
+    if style == "tester":
+        return text + " (and don't make up the answer)"
+    return text
+
+
+# ----------------------------------------------------------------------------
+# Composer
+# ----------------------------------------------------------------------------
+
+def build_flow(p: dict[str, Any]) -> list[str]:
+    flow: list[str] = []
+    flow.append(_opener(p))                # 1 opening
+    flow.extend(_factfind_answers(p))      # 9 fact-find
+    flow.extend(_freeform_qs(p))           # 10 free-form
+    flow.extend(_edge_cases(p))            # 5 edge cases
+    flow.extend(_adversarial(p))           # 5 adversarial + close
+    assert len(flow) == 30, f"flow length {len(flow)} != 30 for {p['persona_id']}"
+    return flow
+
+
+def main() -> None:
+    root = Path(__file__).resolve().parent
+    personas = json.loads((root / "personas.json").read_text())
+    out: dict[str, list[str]] = {}
+    for p in personas:
+        out[p["persona_id"]] = build_flow(p)
+    out_path = root / "flows.json"
+    out_path.write_text(json.dumps(out, indent=2, ensure_ascii=False))
+    total_turns = sum(len(v) for v in out.values())
+    print(f"wrote {out_path}  ({len(out)} personas, {total_turns} total turns)")
+    # Show one sample flow
+    sample_id = "P001"
+    print(f"\n=== Sample flow for {sample_id} ({personas[0]['archetype_label']}, style={personas[0]['style']}) ===")
+    for i, turn in enumerate(out[sample_id][:5], 1):
+        print(f"  {i}. {turn}")
+    print("  ...")
+
+
+if __name__ == "__main__":
+    main()

tools/audit/personas.json

@@ -0,0 +1,2943 @@
+[
+  {
+    "persona_id": "P001",
+    "name": "Aarav Sharma",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 24,
+    "dependents": "self",
+    "income_band": "under_5L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "terse",
+    "style_label": "Terse — 3-7 word answers",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P002",
+    "name": "Diya Joshi",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 28,
+    "dependents": "self",
+    "income_band": "5L-10L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "verbose",
+    "style_label": "Verbose — paragraphs of context",
+    "lang": "en",
+    "style_hedges": [
+      "um, ",
+      "well, ",
+      "you know, "
+    ]
+  },
+  {
+    "persona_id": "P003",
+    "name": "Rohan Banerjee",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 31,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "metro",
+    "marital_stage": "married",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "hinglish",
+    "style_label": "English with Hindi words sprinkled",
+    "lang": "hinglish",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P004",
+    "name": "Ananya Iyer",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 34,
+    "dependents": "self+spouse+kids",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "formal_en",
+    "style_label": "Formal English, full sentences",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P005",
+    "name": "Vikram Khan",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 38,
+    "dependents": "self+spouse+kids",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "casual_en",
+    "style_label": "Casual English with typos and lowercase",
+    "lang": "en",
+    "style_hedges": [
+      "btw ",
+      "lol ",
+      "tbh "
+    ]
+  },
+  {
+    "persona_id": "P006",
+    "name": "Priya Kapoor",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 42,
+    "dependents": "self+spouse+kids+parents",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "sandwich_gen",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "hindi_primary",
+    "style_label": "Mostly Hindi (Devanagari letters)",
+    "lang": "hi",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P007",
+    "name": "Karthik Mehta",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 47,
+    "dependents": "self+spouse+parents",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_no_kids",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "anxious_q",
+    "style_label": "Asks lots of follow-up questions",
+    "lang": "en",
+    "style_hedges": [
+      "but ",
+      "wait, ",
+      "what about "
+    ]
+  },
+  {
+    "persona_id": "P008",
+    "name": "Meera Pillai",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 52,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "tier2",
+    "marital_stage": "empty_nester",
+    "existing_cover_inr": 0,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "numbers_heavy",
+    "style_label": "Quotes specific numbers and policies",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P009",
+    "name": "Saif Nair",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 58,
+    "dependents": "self+spouse",
+    "income_band": "5L-10L",
+    "location_tier": "tier2",
+    "marital_stage": "near_retire",
+    "existing_cover_inr": 0,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "stream",
+    "style_label": "Stream-of-consciousness, rambles",
+    "lang": "en",
+    "style_hedges": [
+      "uh ",
+      "hmm ",
+      "and ",
+      "also "
+    ]
+  },
+  {
+    "persona_id": "P010",
+    "name": "Ishita Reddy",
+    "archetype": "first_buyer",
+    "archetype_label": "First-time health insurance buyer",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "coverage_breadth",
+      "premium_value",
+      "claim_settlement"
+    ],
+    "age": 63,
+    "dependents": "self+spouse",
+    "income_band": "under_5L",
+    "location_tier": "tier3",
+    "marital_stage": "retired",
+    "existing_cover_inr": 0,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "tester",
+    "style_label": "Tries to trip up the bot",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P011",
+    "name": "Diya Reddy",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 24,
+    "dependents": "self",
+    "income_band": "under_5L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "verbose",
+    "style_label": "Verbose — paragraphs of context",
+    "lang": "en",
+    "style_hedges": [
+      "um, ",
+      "well, ",
+      "you know, "
+    ]
+  },
+  {
+    "persona_id": "P012",
+    "name": "Rohan Sharma",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 28,
+    "dependents": "self",
+    "income_band": "5L-10L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "hinglish",
+    "style_label": "English with Hindi words sprinkled",
+    "lang": "hinglish",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P013",
+    "name": "Ananya Joshi",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 31,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "metro",
+    "marital_stage": "married",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "formal_en",
+    "style_label": "Formal English, full sentences",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P014",
+    "name": "Vikram Banerjee",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 34,
+    "dependents": "self+spouse+kids",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "casual_en",
+    "style_label": "Casual English with typos and lowercase",
+    "lang": "en",
+    "style_hedges": [
+      "btw ",
+      "lol ",
+      "tbh "
+    ]
+  },
+  {
+    "persona_id": "P015",
+    "name": "Priya Iyer",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 38,
+    "dependents": "self+spouse+kids",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "hindi_primary",
+    "style_label": "Mostly Hindi (Devanagari letters)",
+    "lang": "hi",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P016",
+    "name": "Karthik Khan",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 42,
+    "dependents": "self+spouse+kids+parents",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "sandwich_gen",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "anxious_q",
+    "style_label": "Asks lots of follow-up questions",
+    "lang": "en",
+    "style_hedges": [
+      "but ",
+      "wait, ",
+      "what about "
+    ]
+  },
+  {
+    "persona_id": "P017",
+    "name": "Meera Kapoor",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 47,
+    "dependents": "self+spouse+parents",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_no_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "numbers_heavy",
+    "style_label": "Quotes specific numbers and policies",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P018",
+    "name": "Saif Mehta",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 52,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "tier2",
+    "marital_stage": "empty_nester",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "stream",
+    "style_label": "Stream-of-consciousness, rambles",
+    "lang": "en",
+    "style_hedges": [
+      "uh ",
+      "hmm ",
+      "and ",
+      "also "
+    ]
+  },
+  {
+    "persona_id": "P019",
+    "name": "Ishita Pillai",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 58,
+    "dependents": "self+spouse",
+    "income_band": "5L-10L",
+    "location_tier": "tier2",
+    "marital_stage": "near_retire",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "tester",
+    "style_label": "Tries to trip up the bot",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P020",
+    "name": "Aarav Nair",
+    "archetype": "upgrader",
+    "archetype_label": "Existing-cover upgrader",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "sum_insured_size",
+      "ped_waiting",
+      "restoration_benefit"
+    ],
+    "age": 63,
+    "dependents": "self+spouse",
+    "income_band": "under_5L",
+    "location_tier": "tier3",
+    "marital_stage": "retired",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "terse",
+    "style_label": "Terse — 3-7 word answers",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P021",
+    "name": "Rohan Nair",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 24,
+    "dependents": "self",
+    "income_band": "under_5L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "under_15k",
+    "style": "hinglish",
+    "style_label": "English with Hindi words sprinkled",
+    "lang": "hinglish",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P022",
+    "name": "Ananya Reddy",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 28,
+    "dependents": "self",
+    "income_band": "5L-10L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "15k_30k",
+    "style": "formal_en",
+    "style_label": "Formal English, full sentences",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P023",
+    "name": "Vikram Sharma",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 31,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "metro",
+    "marital_stage": "married",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "30k_60k",
+    "style": "casual_en",
+    "style_label": "Casual English with typos and lowercase",
+    "lang": "en",
+    "style_hedges": [
+      "btw ",
+      "lol ",
+      "tbh "
+    ]
+  },
+  {
+    "persona_id": "P024",
+    "name": "Priya Joshi",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 34,
+    "dependents": "self+spouse+kids",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "30k_60k",
+    "style": "hindi_primary",
+    "style_label": "Mostly Hindi (Devanagari letters)",
+    "lang": "hi",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P025",
+    "name": "Karthik Banerjee",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 38,
+    "dependents": "self+spouse+kids",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "60k+",
+    "style": "anxious_q",
+    "style_label": "Asks lots of follow-up questions",
+    "lang": "en",
+    "style_hedges": [
+      "but ",
+      "wait, ",
+      "what about "
+    ]
+  },
+  {
+    "persona_id": "P026",
+    "name": "Meera Iyer",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 42,
+    "dependents": "self+spouse+kids+parents",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "sandwich_gen",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "60k+",
+    "style": "numbers_heavy",
+    "style_label": "Quotes specific numbers and policies",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P027",
+    "name": "Saif Khan",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 47,
+    "dependents": "self+spouse+parents",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_no_kids",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "30k_60k",
+    "style": "stream",
+    "style_label": "Stream-of-consciousness, rambles",
+    "lang": "en",
+    "style_hedges": [
+      "uh ",
+      "hmm ",
+      "and ",
+      "also "
+    ]
+  },
+  {
+    "persona_id": "P028",
+    "name": "Ishita Kapoor",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 52,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "tier2",
+    "marital_stage": "empty_nester",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "30k_60k",
+    "style": "tester",
+    "style_label": "Tries to trip up the bot",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P029",
+    "name": "Aarav Mehta",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 58,
+    "dependents": "self+spouse",
+    "income_band": "5L-10L",
+    "location_tier": "tier2",
+    "marital_stage": "near_retire",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "15k_30k",
+    "style": "terse",
+    "style_label": "Terse — 3-7 word answers",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P030",
+    "name": "Diya Pillai",
+    "archetype": "senior_care",
+    "archetype_label": "Buying for senior parents",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "parents_age_max",
+      "ped_waiting",
+      "specific_disease_waiting"
+    ],
+    "age": 63,
+    "dependents": "self+spouse",
+    "income_band": "under_5L",
+    "location_tier": "tier3",
+    "marital_stage": "retired",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": true,
+    "budget_band": "under_15k",
+    "style": "verbose",
+    "style_label": "Verbose — paragraphs of context",
+    "lang": "en",
+    "style_hedges": [
+      "um, ",
+      "well, ",
+      "you know, "
+    ]
+  },
+  {
+    "persona_id": "P031",
+    "name": "Ananya Pillai",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 24,
+    "dependents": "self",
+    "income_band": "under_5L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "formal_en",
+    "style_label": "Formal English, full sentences",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P032",
+    "name": "Vikram Nair",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 28,
+    "dependents": "self",
+    "income_band": "5L-10L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "casual_en",
+    "style_label": "Casual English with typos and lowercase",
+    "lang": "en",
+    "style_hedges": [
+      "btw ",
+      "lol ",
+      "tbh "
+    ]
+  },
+  {
+    "persona_id": "P033",
+    "name": "Priya Reddy",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 31,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "metro",
+    "marital_stage": "married",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "hindi_primary",
+    "style_label": "Mostly Hindi (Devanagari letters)",
+    "lang": "hi",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P034",
+    "name": "Karthik Sharma",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 34,
+    "dependents": "self+spouse+kids",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "anxious_q",
+    "style_label": "Asks lots of follow-up questions",
+    "lang": "en",
+    "style_hedges": [
+      "but ",
+      "wait, ",
+      "what about "
+    ]
+  },
+  {
+    "persona_id": "P035",
+    "name": "Meera Joshi",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 38,
+    "dependents": "self+spouse+kids",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "numbers_heavy",
+    "style_label": "Quotes specific numbers and policies",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P036",
+    "name": "Saif Banerjee",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 42,
+    "dependents": "self+spouse+kids+parents",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "sandwich_gen",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "stream",
+    "style_label": "Stream-of-consciousness, rambles",
+    "lang": "en",
+    "style_hedges": [
+      "uh ",
+      "hmm ",
+      "and ",
+      "also "
+    ]
+  },
+  {
+    "persona_id": "P037",
+    "name": "Ishita Iyer",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 47,
+    "dependents": "self+spouse+parents",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_no_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "tester",
+    "style_label": "Tries to trip up the bot",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P038",
+    "name": "Aarav Khan",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 52,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "tier2",
+    "marital_stage": "empty_nester",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "terse",
+    "style_label": "Terse — 3-7 word answers",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P039",
+    "name": "Diya Kapoor",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 58,
+    "dependents": "self+spouse",
+    "income_band": "5L-10L",
+    "location_tier": "tier2",
+    "marital_stage": "near_retire",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "verbose",
+    "style_label": "Verbose — paragraphs of context",
+    "lang": "en",
+    "style_hedges": [
+      "um, ",
+      "well, ",
+      "you know, "
+    ]
+  },
+  {
+    "persona_id": "P040",
+    "name": "Rohan Mehta",
+    "archetype": "comparer",
+    "archetype_label": "Comparing 2-3 specific policies",
+    "primary_goal": "compare_specific",
+    "anchor_concerns": [
+      "sub_limits",
+      "network_hospitals",
+      "no_claim_bonus"
+    ],
+    "age": 63,
+    "dependents": "self+spouse",
+    "income_band": "under_5L",
+    "location_tier": "tier3",
+    "marital_stage": "retired",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "hinglish",
+    "style_label": "English with Hindi words sprinkled",
+    "lang": "hinglish",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P041",
+    "name": "Vikram Mehta",
+    "archetype": "anxious",
+    "archetype_label": "Anxious skeptic — claim-denial fears",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "exclusions",
+      "free_look"
+    ],
+    "age": 24,
+    "dependents": "self",
+    "income_band": "under_5L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "casual_en",
+    "style_label": "Casual English with typos and lowercase",
+    "lang": "en",
+    "style_hedges": [
+      "btw ",
+      "lol ",
+      "tbh "
+    ]
+  },
+  {
+    "persona_id": "P042",
+    "name": "Priya Pillai",
+    "archetype": "anxious",
+    "archetype_label": "Anxious skeptic — claim-denial fears",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "exclusions",
+      "free_look"
+    ],
+    "age": 28,
+    "dependents": "self",
+    "income_band": "5L-10L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "hindi_primary",
+    "style_label": "Mostly Hindi (Devanagari letters)",
+    "lang": "hi",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P043",
+    "name": "Karthik Nair",
+    "archetype": "anxious",
+    "archetype_label": "Anxious skeptic — claim-denial fears",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "exclusions",
+      "free_look"
+    ],
+    "age": 31,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "metro",
+    "marital_stage": "married",
+    "existing_cover_inr": 0,
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+    "archetype_label": "Tax-planning oriented",
+    "primary_goal": "tax_planning",
+    "anchor_concerns": [
+      "section_80d",
+      "premium_band",
+      "sum_insured_size"
+    ],
+    "age": 47,
+    "dependents": "self+spouse+parents",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_no_kids",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "hinglish",
+    "style_label": "English with Hindi words sprinkled",
+    "lang": "hinglish",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P068",
+    "name": "Ananya Joshi",
+    "archetype": "tax_planner",
+    "archetype_label": "Tax-planning oriented",
+    "primary_goal": "tax_planning",
+    "anchor_concerns": [
+      "section_80d",
+      "premium_band",
+      "sum_insured_size"
+    ],
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+    "dependents": "self+spouse",
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+    "location_tier": "tier2",
+    "marital_stage": "empty_nester",
+    "existing_cover_inr": 0,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "formal_en",
+    "style_label": "Formal English, full sentences",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P069",
+    "name": "Vikram Banerjee",
+    "archetype": "tax_planner",
+    "archetype_label": "Tax-planning oriented",
+    "primary_goal": "tax_planning",
+    "anchor_concerns": [
+      "section_80d",
+      "premium_band",
+      "sum_insured_size"
+    ],
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+    "dependents": "self+spouse",
+    "income_band": "5L-10L",
+    "location_tier": "tier2",
+    "marital_stage": "near_retire",
+    "existing_cover_inr": 0,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "casual_en",
+    "style_label": "Casual English with typos and lowercase",
+    "lang": "en",
+    "style_hedges": [
+      "btw ",
+      "lol ",
+      "tbh "
+    ]
+  },
+  {
+    "persona_id": "P070",
+    "name": "Priya Iyer",
+    "archetype": "tax_planner",
+    "archetype_label": "Tax-planning oriented",
+    "primary_goal": "tax_planning",
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+      "section_80d",
+      "premium_band",
+      "sum_insured_size"
+    ],
+    "age": 63,
+    "dependents": "self+spouse",
+    "income_band": "under_5L",
+    "location_tier": "tier3",
+    "marital_stage": "retired",
+    "existing_cover_inr": 0,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "hindi_primary",
+    "style_label": "Mostly Hindi (Devanagari letters)",
+    "lang": "hi",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P071",
+    "name": "Meera Iyer",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
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+    "dependents": "self",
+    "income_band": "under_5L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "numbers_heavy",
+    "style_label": "Quotes specific numbers and policies",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P072",
+    "name": "Saif Khan",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
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+    "dependents": "self",
+    "income_band": "5L-10L",
+    "location_tier": "metro",
+    "marital_stage": "single",
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+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "stream",
+    "style_label": "Stream-of-consciousness, rambles",
+    "lang": "en",
+    "style_hedges": [
+      "uh ",
+      "hmm ",
+      "and ",
+      "also "
+    ]
+  },
+  {
+    "persona_id": "P073",
+    "name": "Ishita Kapoor",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
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+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "metro",
+    "marital_stage": "married",
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+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "tester",
+    "style_label": "Tries to trip up the bot",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P074",
+    "name": "Aarav Mehta",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
+    "age": 34,
+    "dependents": "self+spouse+kids",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_kids",
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+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "terse",
+    "style_label": "Terse — 3-7 word answers",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P075",
+    "name": "Diya Pillai",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
+    "age": 38,
+    "dependents": "self+spouse+kids",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "verbose",
+    "style_label": "Verbose — paragraphs of context",
+    "lang": "en",
+    "style_hedges": [
+      "um, ",
+      "well, ",
+      "you know, "
+    ]
+  },
+  {
+    "persona_id": "P076",
+    "name": "Rohan Nair",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
+    "age": 42,
+    "dependents": "self+spouse+kids+parents",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "sandwich_gen",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "hinglish",
+    "style_label": "English with Hindi words sprinkled",
+    "lang": "hinglish",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P077",
+    "name": "Ananya Reddy",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
+    "age": 47,
+    "dependents": "self+spouse+parents",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_no_kids",
+    "existing_cover_inr": 0,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "formal_en",
+    "style_label": "Formal English, full sentences",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P078",
+    "name": "Vikram Sharma",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
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+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "tier2",
+    "marital_stage": "empty_nester",
+    "existing_cover_inr": 0,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "casual_en",
+    "style_label": "Casual English with typos and lowercase",
+    "lang": "en",
+    "style_hedges": [
+      "btw ",
+      "lol ",
+      "tbh "
+    ]
+  },
+  {
+    "persona_id": "P079",
+    "name": "Priya Joshi",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
+    "age": 58,
+    "dependents": "self+spouse",
+    "income_band": "5L-10L",
+    "location_tier": "tier2",
+    "marital_stage": "near_retire",
+    "existing_cover_inr": 0,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "hindi_primary",
+    "style_label": "Mostly Hindi (Devanagari letters)",
+    "lang": "hi",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P080",
+    "name": "Karthik Banerjee",
+    "archetype": "low_trust",
+    "archetype_label": "Low trust in insurers; needs proof",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "claim_settlement",
+      "reviews",
+      "regulatory_overlay"
+    ],
+    "age": 63,
+    "dependents": "self+spouse",
+    "income_band": "under_5L",
+    "location_tier": "tier3",
+    "marital_stage": "retired",
+    "existing_cover_inr": 0,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "anxious_q",
+    "style_label": "Asks lots of follow-up questions",
+    "lang": "en",
+    "style_hedges": [
+      "but ",
+      "wait, ",
+      "what about "
+    ]
+  },
+  {
+    "persona_id": "P081",
+    "name": "Saif Banerjee",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 24,
+    "dependents": "self",
+    "income_band": "under_5L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "stream",
+    "style_label": "Stream-of-consciousness, rambles",
+    "lang": "en",
+    "style_hedges": [
+      "uh ",
+      "hmm ",
+      "and ",
+      "also "
+    ]
+  },
+  {
+    "persona_id": "P082",
+    "name": "Ishita Iyer",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 28,
+    "dependents": "self",
+    "income_band": "5L-10L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "tester",
+    "style_label": "Tries to trip up the bot",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P083",
+    "name": "Aarav Khan",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 31,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "metro",
+    "marital_stage": "married",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "terse",
+    "style_label": "Terse — 3-7 word answers",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P084",
+    "name": "Diya Kapoor",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 34,
+    "dependents": "self+spouse+kids",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "verbose",
+    "style_label": "Verbose — paragraphs of context",
+    "lang": "en",
+    "style_hedges": [
+      "um, ",
+      "well, ",
+      "you know, "
+    ]
+  },
+  {
+    "persona_id": "P085",
+    "name": "Rohan Mehta",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 38,
+    "dependents": "self+spouse+kids",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "hinglish",
+    "style_label": "English with Hindi words sprinkled",
+    "lang": "hinglish",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P086",
+    "name": "Ananya Pillai",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 42,
+    "dependents": "self+spouse+kids+parents",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "sandwich_gen",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "formal_en",
+    "style_label": "Formal English, full sentences",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P087",
+    "name": "Vikram Nair",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 47,
+    "dependents": "self+spouse+parents",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_no_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "casual_en",
+    "style_label": "Casual English with typos and lowercase",
+    "lang": "en",
+    "style_hedges": [
+      "btw ",
+      "lol ",
+      "tbh "
+    ]
+  },
+  {
+    "persona_id": "P088",
+    "name": "Priya Reddy",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 52,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "tier2",
+    "marital_stage": "empty_nester",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "hindi_primary",
+    "style_label": "Mostly Hindi (Devanagari letters)",
+    "lang": "hi",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P089",
+    "name": "Karthik Sharma",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 58,
+    "dependents": "self+spouse",
+    "income_band": "5L-10L",
+    "location_tier": "tier2",
+    "marital_stage": "near_retire",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "anxious_q",
+    "style_label": "Asks lots of follow-up questions",
+    "lang": "en",
+    "style_hedges": [
+      "but ",
+      "wait, ",
+      "what about "
+    ]
+  },
+  {
+    "persona_id": "P090",
+    "name": "Meera Joshi",
+    "archetype": "code_switcher",
+    "archetype_label": "Switches English ↔ Hinglish mid-flow",
+    "primary_goal": "first_buy",
+    "anchor_concerns": [
+      "language_switch",
+      "premium_band",
+      "coverage_breadth"
+    ],
+    "age": 63,
+    "dependents": "self+spouse",
+    "income_band": "under_5L",
+    "location_tier": "tier3",
+    "marital_stage": "retired",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "numbers_heavy",
+    "style_label": "Quotes specific numbers and policies",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P091",
+    "name": "Ishita Joshi",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 24,
+    "dependents": "self",
+    "income_band": "under_5L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "asthma"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "tester",
+    "style_label": "Tries to trip up the bot",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P092",
+    "name": "Aarav Banerjee",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 28,
+    "dependents": "self",
+    "income_band": "5L-10L",
+    "location_tier": "metro",
+    "marital_stage": "single",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "asthma"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "terse",
+    "style_label": "Terse — 3-7 word answers",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P093",
+    "name": "Diya Iyer",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 31,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "metro",
+    "marital_stage": "married",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "asthma"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "verbose",
+    "style_label": "Verbose — paragraphs of context",
+    "lang": "en",
+    "style_hedges": [
+      "um, ",
+      "well, ",
+      "you know, "
+    ]
+  },
+  {
+    "persona_id": "P094",
+    "name": "Rohan Khan",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 34,
+    "dependents": "self+spouse+kids",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "asthma"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "hinglish",
+    "style_label": "English with Hindi words sprinkled",
+    "lang": "hinglish",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P095",
+    "name": "Ananya Kapoor",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 38,
+    "dependents": "self+spouse+kids",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "married_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "formal_en",
+    "style_label": "Formal English, full sentences",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P096",
+    "name": "Vikram Mehta",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 42,
+    "dependents": "self+spouse+kids+parents",
+    "income_band": "25L+",
+    "location_tier": "metro",
+    "marital_stage": "sandwich_gen",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "60k+",
+    "style": "casual_en",
+    "style_label": "Casual English with typos and lowercase",
+    "lang": "en",
+    "style_hedges": [
+      "btw ",
+      "lol ",
+      "tbh "
+    ]
+  },
+  {
+    "persona_id": "P097",
+    "name": "Priya Pillai",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 47,
+    "dependents": "self+spouse+parents",
+    "income_band": "10L-25L",
+    "location_tier": "tier1",
+    "marital_stage": "married_no_kids",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "hypertension"
+    ],
+    "parents_to_insure": true,
+    "parents_age_max": 75,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "hindi_primary",
+    "style_label": "Mostly Hindi (Devanagari letters)",
+    "lang": "hi",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P098",
+    "name": "Karthik Nair",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 52,
+    "dependents": "self+spouse",
+    "income_band": "10L-25L",
+    "location_tier": "tier2",
+    "marital_stage": "empty_nester",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "diabetes",
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "30k_60k",
+    "style": "anxious_q",
+    "style_label": "Asks lots of follow-up questions",
+    "lang": "en",
+    "style_hedges": [
+      "but ",
+      "wait, ",
+      "what about "
+    ]
+  },
+  {
+    "persona_id": "P099",
+    "name": "Meera Reddy",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 58,
+    "dependents": "self+spouse",
+    "income_band": "5L-10L",
+    "location_tier": "tier2",
+    "marital_stage": "near_retire",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "diabetes",
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "15k_30k",
+    "style": "numbers_heavy",
+    "style_label": "Quotes specific numbers and policies",
+    "lang": "en",
+    "style_hedges": []
+  },
+  {
+    "persona_id": "P100",
+    "name": "Saif Sharma",
+    "archetype": "specific_condition",
+    "archetype_label": "Has a specific condition driving the buy",
+    "primary_goal": "upgrade",
+    "anchor_concerns": [
+      "ped_waiting",
+      "specific_disease_waiting",
+      "sub_limits"
+    ],
+    "age": 63,
+    "dependents": "self+spouse",
+    "income_band": "under_5L",
+    "location_tier": "tier3",
+    "marital_stage": "retired",
+    "existing_cover_inr": 500000,
+    "health_conditions": [
+      "diabetes",
+      "hypertension"
+    ],
+    "parents_to_insure": false,
+    "parents_age_max": null,
+    "parents_has_ped": false,
+    "budget_band": "under_15k",
+    "style": "stream",
+    "style_label": "Stream-of-consciousness, rambles",
+    "lang": "en",
+    "style_hedges": [
+      "uh ",
+      "hmm ",
+      "and ",
+      "also "
+    ]
+  }
+]

tools/audit/personas.py

@@ -0,0 +1,209 @@
+"""Generate 100 diverse personas for end-to-end bot audit.
+
+Each persona is the product of:
+  - 10 archetypes (intent shape: first-buyer, upgrader, senior-care, …)
+  - 10 demographic profiles (age × dependents × income × city tier)
+  - 1 conversational style (deterministic-pick from a curated list)
+
+The cross-product is engineered so that no two personas have the same
+(archetype, demo, style) triple. Each persona is stable across runs (the
+order is index-based), so report diffs reveal regressions rather than
+shuffle noise.
+
+Run as a script:
+    python tools/audit/personas.py
+  → writes tools/audit/personas.json (100 entries)
+
+Read from another script:
+    from tools.audit.personas import generate
+    personas = generate()  # → list[dict]
+"""
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+# ----------------------------------------------------------------------------
+# Archetypes — the user's INTENT shape. Drives flow design downstream.
+# ----------------------------------------------------------------------------
+ARCHETYPES = [
+    {
+        "id": "first_buyer",
+        "label": "First-time health insurance buyer",
+        "primary_goal": "first_buy",
+        "anchor_concerns": ["coverage_breadth", "premium_value", "claim_settlement"],
+    },
+    {
+        "id": "upgrader",
+        "label": "Existing-cover upgrader",
+        "primary_goal": "upgrade",
+        "anchor_concerns": ["sum_insured_size", "ped_waiting", "restoration_benefit"],
+    },
+    {
+        "id": "senior_care",
+        "label": "Buying for senior parents",
+        "primary_goal": "first_buy",
+        "anchor_concerns": ["parents_age_max", "ped_waiting", "specific_disease_waiting"],
+    },
+    {
+        "id": "comparer",
+        "label": "Comparing 2-3 specific policies",
+        "primary_goal": "compare_specific",
+        "anchor_concerns": ["sub_limits", "network_hospitals", "no_claim_bonus"],
+    },
+    {
+        "id": "anxious",
+        "label": "Anxious skeptic — claim-denial fears",
+        "primary_goal": "first_buy",
+        "anchor_concerns": ["claim_settlement", "exclusions", "free_look"],
+    },
+    {
+        "id": "savvy",
+        "label": "Financially literate, asks pointed questions",
+        "primary_goal": "compare_specific",
+        "anchor_concerns": ["irdai_mandate", "tax_treatment", "ombudsman"],
+    },
+    {
+        "id": "tax_planner",
+        "label": "Tax-planning oriented",
+        "primary_goal": "tax_planning",
+        "anchor_concerns": ["section_80d", "premium_band", "sum_insured_size"],
+    },
+    {
+        "id": "low_trust",
+        "label": "Low trust in insurers; needs proof",
+        "primary_goal": "first_buy",
+        "anchor_concerns": ["claim_settlement", "reviews", "regulatory_overlay"],
+    },
+    {
+        "id": "code_switcher",
+        "label": "Switches English ↔ Hinglish mid-flow",
+        "primary_goal": "first_buy",
+        "anchor_concerns": ["language_switch", "premium_band", "coverage_breadth"],
+    },
+    {
+        "id": "specific_condition",
+        "label": "Has a specific condition driving the buy",
+        "primary_goal": "upgrade",
+        "anchor_concerns": ["ped_waiting", "specific_disease_waiting", "sub_limits"],
+    },
+]
+
+# ----------------------------------------------------------------------------
+# Demographic variations — 10 stable cells across age × family × income × city.
+# Each cell is a different life situation; together they span ~70% of Indian
+# middle-class health-insurance buyers.
+# ----------------------------------------------------------------------------
+DEMOGRAPHICS = [
+    {"age": 24, "dependents": "self",                       "income_band": "under_5L",  "location_tier": "metro",  "marital": "single"},
+    {"age": 28, "dependents": "self",                       "income_band": "5L-10L",    "location_tier": "metro",  "marital": "single"},
+    {"age": 31, "dependents": "self+spouse",                "income_band": "10L-25L",   "location_tier": "metro",  "marital": "married"},
+    {"age": 34, "dependents": "self+spouse+kids",           "income_band": "10L-25L",   "location_tier": "tier1",  "marital": "married_kids"},
+    {"age": 38, "dependents": "self+spouse+kids",           "income_band": "25L+",      "location_tier": "metro",  "marital": "married_kids"},
+    {"age": 42, "dependents": "self+spouse+kids+parents",   "income_band": "25L+",      "location_tier": "metro",  "marital": "sandwich_gen"},
+    {"age": 47, "dependents": "self+spouse+parents",        "income_band": "10L-25L",   "location_tier": "tier1",  "marital": "married_no_kids"},
+    {"age": 52, "dependents": "self+spouse",                "income_band": "10L-25L",   "location_tier": "tier2",  "marital": "empty_nester"},
+    {"age": 58, "dependents": "self+spouse",                "income_band": "5L-10L",    "location_tier": "tier2",  "marital": "near_retire"},
+    {"age": 63, "dependents": "self+spouse",                "income_band": "under_5L",  "location_tier": "tier3",  "marital": "retired"},
+]
+
+# ----------------------------------------------------------------------------
+# Conversational styles — how the persona TALKS, independent of what they
+# want. The 10 styles cycle so each (archetype × demo) cell gets a different
+# voice across the 100 personas.
+# ----------------------------------------------------------------------------
+STYLES = [
+    {"id": "terse",        "label": "Terse — 3-7 word answers",                "lang": "en", "hedges": []},
+    {"id": "verbose",      "label": "Verbose — paragraphs of context",         "lang": "en", "hedges": ["um, ", "well, ", "you know, "]},
+    {"id": "hinglish",     "label": "English with Hindi words sprinkled",      "lang": "hinglish", "hedges": []},
+    {"id": "formal_en",    "label": "Formal English, full sentences",          "lang": "en", "hedges": []},
+    {"id": "casual_en",    "label": "Casual English with typos and lowercase", "lang": "en", "hedges": ["btw ", "lol ", "tbh "]},
+    {"id": "hindi_primary","label": "Mostly Hindi (Devanagari letters)",       "lang": "hi", "hedges": []},
+    {"id": "anxious_q",    "label": "Asks lots of follow-up questions",        "lang": "en", "hedges": ["but ", "wait, ", "what about "]},
+    {"id": "numbers_heavy","label": "Quotes specific numbers and policies",    "lang": "en", "hedges": []},
+    {"id": "stream",       "label": "Stream-of-consciousness, rambles",        "lang": "en", "hedges": ["uh ", "hmm ", "and ", "also "]},
+    {"id": "tester",       "label": "Tries to trip up the bot",                "lang": "en", "hedges": []},
+]
+
+# ----------------------------------------------------------------------------
+# Synthetic name pool — 10 first names × 10 surnames covers our 100 personas
+# without recycling the same identity. Names are common Indian names with a
+# light regional spread (north + south + west).
+# ----------------------------------------------------------------------------
+FIRST_NAMES = ["Aarav", "Diya", "Rohan", "Ananya", "Vikram", "Priya", "Karthik", "Meera", "Saif", "Ishita"]
+SURNAMES    = ["Sharma", "Iyer", "Mehta", "Reddy", "Banerjee", "Kapoor", "Nair", "Joshi", "Khan", "Pillai"]
+
+# Health condition presets keyed by archetype + age bucket.
+def _condition_for(archetype_id: str, age: int) -> list[str]:
+    if archetype_id == "specific_condition":
+        if age < 35:
+            return ["asthma"]
+        if age < 50:
+            return ["hypertension"]
+        return ["diabetes", "hypertension"]
+    if archetype_id == "senior_care":
+        return []  # parents' conditions handled separately in flow
+    if age >= 50:
+        return ["hypertension"]  # age-bucket baseline
+    return []
+
+
+def generate() -> list[dict[str, Any]]:
+    personas: list[dict[str, Any]] = []
+    pid = 1
+    for ai, arch in enumerate(ARCHETYPES):
+        for di, demo in enumerate(DEMOGRAPHICS):
+            style = STYLES[(ai + di) % len(STYLES)]  # rotates so styles spread evenly across archetypes
+            name_first = FIRST_NAMES[(ai + di) % len(FIRST_NAMES)]
+            name_last = SURNAMES[(ai * 3 + di * 7) % len(SURNAMES)]
+            persona = {
+                "persona_id": f"P{pid:03d}",
+                "name": f"{name_first} {name_last}",
+                "archetype": arch["id"],
+                "archetype_label": arch["label"],
+                "primary_goal": arch["primary_goal"],
+                "anchor_concerns": arch["anchor_concerns"],
+                "age": demo["age"],
+                "dependents": demo["dependents"],
+                "income_band": demo["income_band"],
+                "location_tier": demo["location_tier"],
+                "marital_stage": demo["marital"],
+                "existing_cover_inr": 0 if arch["id"] in ("first_buyer", "senior_care", "anxious", "tax_planner", "low_trust") else 500_000,
+                "health_conditions": _condition_for(arch["id"], demo["age"]),
+                "parents_to_insure": arch["id"] == "senior_care" or "parents" in demo["dependents"],
+                "parents_age_max": 75 if arch["id"] == "senior_care" or "parents" in demo["dependents"] else None,
+                "parents_has_ped": arch["id"] == "senior_care",
+                "budget_band": {
+                    "under_5L": "under_15k",
+                    "5L-10L": "15k_30k",
+                    "10L-25L": "30k_60k",
+                    "25L+": "60k+",
+                }[demo["income_band"]],
+                "style": style["id"],
+                "style_label": style["label"],
+                "lang": style["lang"],
+                "style_hedges": style["hedges"],
+            }
+            personas.append(persona)
+            pid += 1
+    return personas
+
+
+def main() -> None:
+    personas = generate()
+    out = Path(__file__).resolve().parent / "personas.json"
+    out.write_text(json.dumps(personas, indent=2, ensure_ascii=False))
+    print(f"wrote {out}  ({len(personas)} personas)")
+    # Quick distribution sanity check
+    by_arch: dict[str, int] = {}
+    by_style: dict[str, int] = {}
+    for p in personas:
+        by_arch[p["archetype"]] = by_arch.get(p["archetype"], 0) + 1
+        by_style[p["style"]] = by_style.get(p["style"], 0) + 1
+    print(f"  archetypes (each should be 10): {by_arch}")
+    print(f"  styles (each should be 10):     {by_style}")
+
+
+if __name__ == "__main__":
+    main()

tools/audit/run_audit.py

@@ -0,0 +1,239 @@
+"""Run the audit — walks each persona's 30-turn flow against the live API.
+
+Design notes:
+  - Resumable: completed personas land in audit_results/<run_id>/transcripts/
+    as soon as they finish; if re-run, already-saved IDs are skipped.
+  - Rate-limited: NIM's 40 req/min cap is the bottleneck. Each /api/chat
+    triggers ~1-3 NIM calls (brain + sometimes judge + sometimes cross-check).
+    We use a serial loop with adaptive sleep, plus a soft retry on 5xx.
+  - Concurrent: WORKERS workers pull personas off a shared queue. The global
+    NIM cap is enforced via the per-request sleep so concurrency × dispatch
+    rate stays under the cap.
+  - Each turn captures: reply_text, brain_used, intent, citations,
+    profile_updates, faithfulness_passed, blocked, latency_ms.
+
+Usage:
+  python tools/audit/run_audit.py [--max-personas N] [--workers W] [--base URL]
+"""
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import time
+import uuid
+from pathlib import Path
+from typing import Any
+
+import httpx
+
+DEFAULT_BASE = "https://rohitsar567-insurancebot.hf.space"
+ROOT = Path(__file__).resolve().parent
+RESULTS_ROOT = ROOT.parent.parent / "audit_results"
+
+
+async def post_chat(
+    client: httpx.AsyncClient,
+    base: str,
+    user_text: str,
+    session_id: str,
+    chat_history: list[dict],
+    sema: asyncio.Semaphore,
+    delay_s: float,
+) -> dict[str, Any]:
+    """One /api/chat call. Returns raw response dict + latency_ms.
+
+    On HTTP 5xx, retries up to 3x with exponential backoff (3s, 8s, 18s).
+    """
+    payload = {
+        "user_text": user_text,
+        "session_id": session_id,
+        "chat_history": chat_history,
+        "profile": {},
+        "return_audio": False,
+        "tts_language_code": "en-IN",
+    }
+    backoff = [3.0, 8.0, 18.0]
+    for attempt in range(len(backoff) + 1):
+        async with sema:
+            await asyncio.sleep(delay_s)  # global rate-limit token
+            t0 = time.monotonic()
+            try:
+                resp = await client.post(f"{base}/api/chat", json=payload, timeout=60.0)
+            except (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout, httpx.RemoteProtocolError) as e:
+                if attempt < len(backoff):
+                    await asyncio.sleep(backoff[attempt])
+                    continue
+                return {"error": f"{type(e).__name__}: {e}", "latency_ms": int((time.monotonic() - t0) * 1000)}
+            elapsed = int((time.monotonic() - t0) * 1000)
+            if resp.status_code >= 500 and attempt < len(backoff):
+                await asyncio.sleep(backoff[attempt])
+                continue
+            if resp.status_code != 200:
+                return {
+                    "error": f"http_{resp.status_code}",
+                    "body": resp.text[:500],
+                    "latency_ms": elapsed,
+                }
+            data = resp.json()
+            data["_latency_ms"] = elapsed
+            return data
+    return {"error": "retries_exhausted"}
+
+
+async def run_persona(
+    client: httpx.AsyncClient,
+    base: str,
+    persona: dict[str, Any],
+    flow: list[str],
+    out_dir: Path,
+    sema: asyncio.Semaphore,
+    delay_s: float,
+    log_lock: asyncio.Lock,
+) -> None:
+    out_file = out_dir / f"{persona['persona_id']}.json"
+    if out_file.exists():
+        async with log_lock:
+            print(f"  skip {persona['persona_id']} (already done)")
+        return
+
+    session_id = f"audit_{persona['persona_id']}_{uuid.uuid4().hex[:6]}"
+    history: list[dict] = []
+    transcript: list[dict] = []
+
+    for turn_idx, user_text in enumerate(flow, 1):
+        resp = await post_chat(client, base, user_text, session_id, history, sema, delay_s)
+        if "error" in resp:
+            transcript.append({
+                "turn": turn_idx,
+                "user_text": user_text,
+                "error": resp.get("error"),
+                "body": resp.get("body"),
+                "latency_ms": resp.get("latency_ms") or resp.get("_latency_ms"),
+            })
+            # Don't break — keep going so we get partial transcript for analysis.
+            continue
+        reply_text = resp.get("reply_text", "")
+        transcript.append({
+            "turn": turn_idx,
+            "user_text": user_text,
+            "reply_text": reply_text,
+            "brain_used": resp.get("brain_used"),
+            "intent": resp.get("intent"),
+            "language": resp.get("language"),
+            "citations": resp.get("citations", []),
+            "profile_updates": resp.get("profile_updates", {}),
+            "faithfulness_passed": resp.get("faithfulness_passed"),
+            "faithfulness_reasons": resp.get("faithfulness_reasons", []),
+            "blocked": resp.get("blocked"),
+            "latency_ms": resp.get("_latency_ms"),
+            "session_id": resp.get("session_id"),
+        })
+        history.append({"role": "user", "content": user_text})
+        history.append({"role": "assistant", "content": reply_text})
+
+    payload = {
+        "persona": persona,
+        "session_id": session_id,
+        "turns": transcript,
+        "completed_turns": len(transcript),
+        "errors": sum(1 for t in transcript if t.get("error")),
+    }
+    out_file.write_text(json.dumps(payload, indent=2, ensure_ascii=False))
+    async with log_lock:
+        ok = sum(1 for t in transcript if not t.get("error"))
+        print(f"  done {persona['persona_id']:<5} | {ok}/{len(transcript)} ok | "
+              f"refusals={sum(1 for t in transcript if t.get('blocked'))} | "
+              f"out={out_file.name}")
+
+
+async def worker(
+    worker_id: int,
+    queue: asyncio.Queue,
+    base: str,
+    out_dir: Path,
+    sema: asyncio.Semaphore,
+    delay_s: float,
+    log_lock: asyncio.Lock,
+) -> None:
+    async with httpx.AsyncClient() as client:
+        while True:
+            item = await queue.get()
+            if item is None:
+                queue.task_done()
+                break
+            persona, flow = item
+            try:
+                await run_persona(client, base, persona, flow, out_dir, sema, delay_s, log_lock)
+            except Exception as e:
+                async with log_lock:
+                    print(f"  ERR  {persona['persona_id']}: {type(e).__name__}: {e}")
+            queue.task_done()
+
+
+async def main_async(args: argparse.Namespace) -> None:
+    personas = json.loads((ROOT / "personas.json").read_text())
+    flows = json.loads((ROOT / "flows.json").read_text())
+
+    if args.max_personas:
+        personas = personas[: args.max_personas]
+
+    run_id = args.run_id or time.strftime("%Y%m%d_%H%M%S")
+    out_dir = RESULTS_ROOT / run_id / "transcripts"
+    out_dir.mkdir(parents=True, exist_ok=True)
+    summary_file = RESULTS_ROOT / run_id / "summary.json"
+    print(f"=== audit run_id={run_id} ===")
+    print(f"  base:       {args.base}")
+    print(f"  personas:   {len(personas)}")
+    print(f"  total turns: {sum(len(flows[p['persona_id']]) for p in personas)}")
+    print(f"  workers:    {args.workers}")
+    print(f"  delay_s:    {args.delay}  (per dispatch)")
+    print(f"  out:        {out_dir}")
+
+    sema = asyncio.Semaphore(args.workers)
+    queue: asyncio.Queue = asyncio.Queue()
+    for p in personas:
+        await queue.put((p, flows[p["persona_id"]]))
+    # Sentinel to stop workers
+    for _ in range(args.workers):
+        await queue.put(None)
+
+    log_lock = asyncio.Lock()
+    started = time.time()
+    workers = [
+        asyncio.create_task(worker(i, queue, args.base, out_dir, sema, args.delay, log_lock))
+        for i in range(args.workers)
+    ]
+    await queue.join()
+    await asyncio.gather(*workers)
+    elapsed = time.time() - started
+
+    completed = sorted(out_dir.glob("*.json"))
+    summary_file.write_text(json.dumps({
+        "run_id": run_id,
+        "base": args.base,
+        "personas_requested": len(personas),
+        "personas_completed": len(completed),
+        "elapsed_seconds": int(elapsed),
+        "workers": args.workers,
+        "delay_s": args.delay,
+        "transcripts_dir": str(out_dir),
+    }, indent=2))
+    print(f"\n=== done in {elapsed:.0f}s ===")
+    print(f"  completed: {len(completed)} / {len(personas)} personas")
+    print(f"  summary:   {summary_file}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base", default=DEFAULT_BASE)
+    parser.add_argument("--max-personas", type=int, default=None)
+    parser.add_argument("--workers", type=int, default=4, help="concurrent personas in flight")
+    parser.add_argument("--delay", type=float, default=1.8, help="seconds between dispatches per worker")
+    parser.add_argument("--run-id", default=None)
+    args = parser.parse_args()
+    asyncio.run(main_async(args))
+
+
+if __name__ == "__main__":
+    main()