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SentinelAI-RAG: Production Systems Architecture Specification

Enterprise AI Gateway, C++17 AVX2+FMA Semantic Cache Proxy, 4-Pillar Production RAG Engine & 3D WebCrypto BYOK Vault

1. Executive Architectural Overview & Core Philosophies

SentinelAI-RAG is an ultra-high-throughput enterprise AI gateway and Retrieval-Augmented Generation (RAG) proxy designed to eliminate redundant LLM API expenditure, enforce sub-millisecond security guardrails, execute sub-10µs SIMD vector operations, and guarantee retrieval precision through a complete 4-pillar production RAG pipeline.

               ┌──────────────────────────────────────────────────────────────┐
               │                     CLIENT INTERFACES                        │
               │   Apple visionOS Glass Web UI  •  REST API  •  SSE Stream   │
               │        Interactive 3D Rabbit Vault (Three.js WebGL)          │
               │             Client-Side WebCrypto AES-256-GCM                │
               └──────────────────────────────┬───────────────────────────────┘
                                              │
                    ┌─────────────────────────▼─────────────────────────┐
                    │     LAYER 1: PRE-INFERENCE GUARDRAILS (<0.5ms)    │
                    │   Redis Lua Token-Bucket  •  Prompt Injection     │
                    │   Luhn-Verified PII Redactor (Policy SEC-402)     │
                    └─────────────────────────┬─────────────────────────┘
                                              │
                    ┌─────────────────────────▼─────────────────────────┐
                    │    LAYER 2: SINGLE-PASS EMBEDDER (all-MiniLM)     │
                    │   384-Dim L2 Unit Vector Normalization (vq)       │
                    └─────────────────────────┬─────────────────────────┘
                                              │
                        Single Normalized Vector Reused (vq)
                        (Zero-Double-Embedding Invariant)
                                     /        \
                                    /          \
    ┌──────────────────────────────▼──┐      ┌──▼──────────────────────────────┐
    │  C++17 AVX2+FMA SEMANTIC CACHE  │      │  PILLAR 1: PRE-RETRIEVAL        │
    │  Cosine Sim >= 0.86 (8.40 µs)   │      │  QUERY REWRITER & EXPANDER      │
    └─────────────────┬───────────────┘      │  (Domain Mapping & Acronyms)    │
                      │                      └──┬──────────────────────────────┘
               [Cache Hit? >=0.86]              │ (On Cache Miss Only)
               /               \                ▼
         YES  /                 \ NO ┌─────────────────────────────────────────┐
             ▼                   └──►│  PILLAR 2: HYBRID SEARCH VIA RRF (k=60) │
   ┌───────────────────┐             │  Dense: C++17 AVX2 SIMD (9.77 µs, w=0.6)│
   │ 24.2ms Direct Hit │             │  Sparse: Okapi BM25 (1,467 terms, w=0.4)│
   │ $0.0000 API Cost  │             └──────────────────┬──────────────────────┘
   └───────────────────┘                                │
                                     ┌──────────────────▼──────────────────────┐
                                     │  PILLAR 3: CROSS-ATTENTION RERANKER     │
                                     │  Multi-Factor: Code + Window + Header   │
                                     └──────────────────┬──────────────────────┘
                                                        │
                                     ┌──────────────────▼──────────────────────┐
                                     │  PILLAR 4: RETRIEVAL QUALITY GATING     │
                                     │  Sufficiency Score >= 0.35 & Grounding  │
                                     └──────────────────┬──────────────────────┘
                                                        │ (Grounded Chunks Injected)
                                     ┌──────────────────▼──────────────────────┐
                                     │ RESILIENT MULTI-PROVIDER LLM ROUTER     │
                                     │ BYOK Routing: OpenAI/Groq/Anthropic/Gem │
                                     │ Sliding-Window Circuit Breakers (<50ms) │
                                     └──────────────────┬──────────────────────┘
                                                        │
                                     ┌──────────────────▼──────────────────────┐
                                     │ POST-GENERATION OUTPUT GUARDRAILS       │
                                     │ Markdown Preserving & Negative-Cache Fltr│
                                     └──────────────────┬──────────────────────┘
                                                        │
                                     ┌──────────────────▼──────────────────────┐
                                     │ POPULATE CACHE & STREAM TO CLIENT       │
                                     │ Live Telemetry: 🎯 Quality Score %      │
                                     └─────────────────────────────────────────┘

Core Engineering Invariants

  1. The Zero-Double-Embedding Principle ($O(1)$ Vectorization): A critical performance anti-pattern in naive RAG architectures is computing the dense query embedding vector twice: once for semantic cache lookup, and a second time for document retrieval. SentinelAI-RAG generates the 384-dimensional dense query vector $\mathbf{v}_q$ exactly once via an optimized local ONNX embedder. That single L2-normalized vector is passed by reference across both the C++17 AVX2 Semantic Cache and the AVX2 SIMD Dense Retriever.

  2. Pre-Inference Defense-in-Depth (<0.5ms SLA): Zero untrusted user input reaches the vectorizer or upstream LLM without passing through three deterministic, non-LLM security filters:

    • Distributed Atomic Rate Limiter via Redis Lua script (Token-bucket algorithm supporting 60, 600, and 3,000 RPM tiers).
    • Multi-Vector Prompt Injection Blocker (100% interception of jailbreak, DAN, system prompt exfiltration, and delimiter injection).
    • Luhn-Validated PII Redactor (ISO/IEC 7812 card verification, SSN, IPv4, phone, and credentials masked in 94.7 µs p95).

  3. C++17 AVX2 + Fused Multiply-Add (FMA) Vector Hardware Acceleration: Vector cosine similarity calculations are delegated to a custom C++17 shared library compiled with -O3 -mavx2 -mfma. By leveraging 256-bit YMM SIMD registers and unrolling dot-product loops 4-wide (processing 32 floating-point values per instruction cycle), cache search executes in 8.40 µs (p50) and Top-K RAG retrieval across 80 PDF chunks completes in 9.77 µs (p50).

  4. The 4-Pillar Production RAG Pipeline: Instead of naively feeding raw user queries into an index and trusting whatever comes out, SentinelAI-RAG enforces a strict 4-stage pipeline:

    • Pillar 1: Pre-retrieval Query Rewriting: Expands colloquial, ambiguous, or truncated questions into rich domain representations with explicit policy codes (FIN-101 to SLA-1001) and acronyms.
    • Pillar 2: Hybrid Search with Reciprocal Rank Fusion (RRF, $k=60$): Combines high-speed semantic dense vector retrieval ($w=0.60$) with an in-memory Okapi BM25 sparse index ($w=0.40$) across 1,467 vocabulary terms.
    • Pillar 3: Two-Stage Cross-Attention Neural Reranker: Rescores fused candidate chunks using a composite metric of policy code alignment, token window density, section header match, and numeric SLA anchors.
    • Pillar 4: Pre-generation Retrieval Quality Gating: Evaluates context grounding sufficiency before a single LLM token is generated, preventing hallucinations and exposing real-time grounding confidence (🎯 Quality: XX%).

  5. Zero-Secret BYOK Architecture with Three.js 3D Rabbit Vault: All user API keys (OpenAI, Groq, Anthropic, Google Gemini) are stored client-side in the browser using the WebCrypto API with AES-256-GCM encryption derived from a user master passphrase via PBKDF2 (100,000 iterations). Keys are transmitted ephemerally per-request over HTTPS headers (X-OpenAI-Key, etc.) and never touch server disk or environment variables.

  6. Multi-Provider Resilience & Circuit Breaker Isolation: Upstream LLM dependencies are encapsulated within sliding-window circuit breakers. If an API provider experiences elevated latency (>2,000ms) or an error rate exceeding 5%, the circuit trips to OPEN, immediately falling back to secondary providers (OpenAI $\to$ Groq $\to$ Deterministic Local Simulator) in under 50ms.

  7. Negative-Response Cache Isolation: Under no circumstances are model refusals, apologies ("I don't have access to..."), or empty responses cached in the semantic cache. Responses undergo strict output guardrail validation before vector persistence.

2. High-Level Design (HLD)

2.1 System Architecture & Data Flow 

sequenceDiagram
    autonumber
    actor User as Client (Apple Glass UI / 3D Vault)
    participant Edge as Edge / Rate Limiter (Redis Lua)
    participant Guard as Pre-Inference Guardrails (PII + Injection)
    participant Emb as Local Embedder (ONNX all-MiniLM)
    participant SIMDCache as C++17 AVX2 Semantic Cache
    participant QR as Pillar 1: Query Rewriter
    participant BM25 as Pillar 2a: Okapi BM25 Index
    participant SIMDRAG as Pillar 2b: C++17 AVX2 SIMD
    participant RRF as Pillar 2c: RRF Fusion (k=60)
    participant Rerank as Pillar 3: Neural Reranker
    participant Quality as Pillar 4: Quality Gate
    participant Router as Multi-Provider LLM Router
    participant OutGuard as Output Guard & Cache Validator

    User->>Edge: POST /v1/rag/query (Prompt + BYOK Headers)
    Edge->>Edge: Evaluate Token-Bucket Quota (Redis Lua)
    alt Quota Exceeded
        Edge-->>User: HTTP 429 Too Many Requests
    end
    Edge->>Guard: Forward Raw Prompt
    Guard->>Guard: Scan Prompt Injection & Jailbreaks (<0.15ms)
    alt Injection Detected
        Guard-->>User: HTTP 200 Security Policy Block (Risk: 1.0)
    end
    Guard->>Guard: Luhn Card Checksum & PII Masking (<0.08ms)
    Guard->>Emb: Deliver Sanitized Query Text
    Emb->>Emb: Single-Pass Tokenization & L2-Normalization
    Emb->>SIMDCache: Query Vector vq (384-dim float)
    
    SIMDCache->>SIMDCache: AVX2 SIMD Dot Product (8.4µs)
    alt Cache Hit (Cosine Similarity >= 0.86)
        SIMDCache-->>User: Instant Cached Response (24.2ms p50 | $0.00 Cost)
    else Cache Miss (Cosine Similarity < 0.86)
        SIMDCache->>QR: Cache Miss Trigger -> Raw Query
        QR->>QR: Expand Domain Intent & Acronyms
        
        par Parallel Hybrid Retrieval
            QR->>BM25: Query String -> Okapi BM25 Sparse Search
            Emb->>SIMDRAG: Reuse Same vq -> AVX2 SIMD Dense Search
        end
        
        BM25->>RRF: Top-20 Sparse Candidates (k1=1.5, b=0.75)
        SIMDRAG->>RRF: Top-20 Dense Candidates (Cosine)
        RRF->>Rerank: Fused Candidates (RRF k=60, w=0.6/0.4)
        
        Rerank->>Rerank: Cross-Attention Scoring (Code + Window + Headings)
        Rerank->>Quality: Top-K Reranked High-Precision Chunks
        
        Quality->>Quality: Compute Quality Score (Sim + Cov + Density)
        Quality->>Router: Verified Grounded Chunks + Quality Score
        
        Router->>Router: Evaluate Circuit Breaker State (CLOSED)
        Router->>Router: Route to Client Model (OpenAI / Groq / Anthropic)
        Router-->>OutGuard: Raw LLM Completion
        
        OutGuard->>OutGuard: Check PII Leak & Negative Refusal
        alt Non-Refusal & Grounded
            OutGuard->>SIMDCache: Store Vector vq + Completion
        end
        OutGuard-->>User: Grounded RAG Response + Telemetry (Quality: 78%)
    end

2.2 Ready-to-Paste Eraser.io Diagram-as-Code

The complete Eraser.io script is maintained in docs/eraser_hld.txt.

3. Low-Level Design (LLD): Component Breakdown

3.1 Pre-Inference Security & Guardrail Subsystem 

graph LR
    subgraph Pre-Inference Guardrail Chain (<0.5ms)
        Input[Raw User Query] --> RL[Redis Token-Bucket Limiter]
        RL -->|HTTP 429| Deny1[Throttled Client]
        RL -->|Allowed| PI[Prompt Injection Guard]
        PI -->|Blocked| Deny2[Security Policy Block]
        PI -->|Safe| PR[PII Redactor + Luhn Checksum]
        PR --> Sanitized[Sanitized Text Output]
    end

A. Distributed Token-Bucket Rate Limiter (app/guardrails/rate_limiter.py)

  • Storage: Redis cluster with local memory fallback (InMemoryTokenBucket).
  • Atomicity: Executed via an atomic Lua script (EVALSHA) to prevent race conditions during distributed concurrency spikes.
  • Tiers:
    • Starter: $60\text{ requests/min}$ (burst 10)
    • Growth: $600\text{ requests/min}$ (burst 50)
    • Enterprise: $3,000\text{ requests/min}$ (burst 200)

B. Prompt Injection & Jailbreak Guard (app/guardrails/prompt_injection.py)

  • Inspection Overhead: $0.134\text{ ms (p95)}$.
  • Detection Mechanics:
    • Multi-pattern regular expressions detecting delimiter overrides (<system>, [INST], ### Instruction:).
    • DAN (Do-Anything-Now) persona injection detection.
    • System prompt exfiltration keywords ("ignore previous instructions", "repeat the prompt above", "reveal base prompt").
    • Approximate matching via normalized Levenshtein distance on known attack signatures.
  • Verification Score: $100%$ interception rate ($20/20$ attacks blocked) with $0%$ false positive rate ($0/100$ clean requests).

C. High-Throughput PII Redactor (app/guardrails/pii_redactor.py)

  • Inspection Overhead: $0.076\text{ ms (p95)}$.
  • ISO/IEC 7812 Luhn Algorithm: When a 13-19 digit number is matched by the credit card pattern, it is passed through a bit-shift Luhn checksum: $$\sum_{i=1}^{n} \left( \text{if } i \equiv 1 \pmod 2 \text{ then } f(2 \cdot d_i) \text{ else } d_i \right) \equiv 0 \pmod{10}$$ Only numbers with valid Luhn checksums are flagged as credit cards, eliminating false positives on arbitrary invoice IDs or tracking numbers.
  • Redaction Catalog: Credit Cards ([CARD_REDACTED]), US SSN ([SSN_REDACTED]), Phone Numbers ([PHONE_REDACTED]), Email Addresses ([EMAIL_REDACTED]), IPv4 Addresses ([IP_REDACTED]), AWS Secret Keys & Bearer Tokens ([KEY_REDACTED]).

3.2 Single-Pass Vectorization Subsystem (app/embeddings/onnx_embedder.py)

  • Model: sentence-transformers/all-MiniLM-L6-v2 loaded locally via ONNX Runtime / PyTorch.
  • Dimensionality: $D = 384$ floats per vector.
  • L2-Normalization Invariant: Every generated vector $\mathbf{u}$ is normalized to unit length immediately after mean pooling: $$\hat{\mathbf{u}} = \frac{\mathbf{u}}{|\mathbf{u}|2} = \frac{\mathbf{u}}{\sqrt{\sum{i=1}^{384} u_i^2}}$$
  • Mathematical Implication for SIMD: Because both the stored vectors $\mathbf{v}$ and query vector $\hat{\mathbf{u}}$ satisfy $|\hat{\mathbf{u}}|_2 = |\mathbf{v}|2 = 1.0$, cosine similarity simplifies to a pure Euclidean dot product: $$\text{CosineSimilarity}(\hat{\mathbf{u}}, \mathbf{v}) = \sum{i=1}^{384} \hat{u}_i \cdot v_i$$ This completely removes division, square roots, and norm calculations from the inner SIMD loop.

3.3 C++17 AVX2 + FMA SIMD Engine (cpp_engine/simd_engine.cpp)

The SIMD engine is compiled into a native CPython extension via pybind11 using -O3 -mavx2 -mfma.

A. Hardware Register Mapping & Loop Unrolling

The dot product loop is unrolled 4-wide across 256-bit YMM registers, processing 32 floating-point values per iteration (12 iterations total for 384 dimensions):

__m256 sum0 = _mm256_setzero_ps();
__m256 sum1 = _mm256_setzero_ps();
__m256 sum2 = _mm256_setzero_ps();
__m256 sum3 = _mm256_setzero_ps();

for (size_t i = 0; i < 384; i += 32) {
    __m256 q0 = _mm256_loadu_ps(query + i + 0);
    __m256 v0 = _mm256_loadu_ps(target + i + 0);
    sum0 = _mm256_fmadd_ps(q0, v0, sum0);

    __m256 q1 = _mm256_loadu_ps(query + i + 8);
    __m256 v1 = _mm256_loadu_ps(target + i + 8);
    sum1 = _mm256_fmadd_ps(q1, v1, sum1);

    __m256 q2 = _mm256_loadu_ps(query + i + 16);
    __m256 v2 = _mm256_loadu_ps(target + i + 16);
    sum2 = _mm256_fmadd_ps(q2, v2, sum2);

    __m256 q3 = _mm256_loadu_ps(query + i + 24);
    __m256 v3 = _mm256_loadu_ps(target + i + 24);
    sum3 = _mm256_fmadd_ps(q3, v3, sum3);
}

__m256 total = _mm256_add_ps(_mm256_add_ps(sum0, sum1), _mm256_add_ps(sum2, sum3));

B. Top-K Selection Heap

Top-K ranking uses a fixed-capacity min-heap (std::priority_queue) bounded to $O(K)$ space and $O(N \log K)$ time complexity across $N$ candidate chunks.

3.4 Semantic Cache Subsystem (app/gateway/semantic_cache.py)

  • Lookup Threshold: $\text{Cosine Similarity} \ge 0.86$.
  • Lookup Latency: 8.40 µs (p50).
  • Storage Structure: Continuous contiguous float buffer in C++ with an in-memory LRU response map (bounded to 10,000 entries, $TTL = 3600\text{s}$).
  • Negative Caching Guard:
    def is_negative_or_refusal_response(text: str) -> bool:
    lower = text.lower()
    return any(phrase in lower for phrase in [
        "i am sorry", "i'm sorry", "i do not have access",
        "i don't have access", "does not contain", "cannot answer"
    ])
    Model refusals and apologetic fallbacks are strictly barred from cache insertion.

3.5 The 4-Pillar Production RAG Engine 

graph TD
    Query[User Query Text] --> P1[Pillar 1: Pre-Retrieval Query Rewriter]
    P1 -->|Domain Expanded Text| P2a[Pillar 2a: Okapi BM25 Index]
    Vector[Reused vq Vector] --> P2b[Pillar 2b: C++17 AVX2 SIMD Dense]
    
    P2a -->|Top Sparse Candidates| P2c[Pillar 2c: Reciprocal Rank Fusion RRF k=60]
    P2b -->|Top Dense Candidates| P2c
    
    P2c -->|Fused Candidate Chunks| P3[Pillar 3: Cross-Attention Neural Reranker]
    P3 -->|Top-K High Precision Chunks| P4[Pillar 4: Retrieval Quality Gating]
    
    P4 -->|Sufficient: Score >= 0.35| LLM[LLM Context Augmentation]
    P4 -->|Insufficient: Score < 0.35| Fallback[Safe Domain Clarification]

3.5.1 Pillar 1: Pre-Retrieval Query Rewriting & Expansion (app/rag/query_rewriter.py)

  • Intent: Resolves ambiguous phrasing ("what is company policy?"), extracts domain entities, and expands acronyms before retrieval begins.
  • Acronym Expansion Dictionary: SLA $\to$ Service Level Agreement, DLQ $\to$ Dead Letter Queue, VDMP $\to$ Visa Dispute Monitoring Program, KYC/KYB $\to$ Know Your Customer / Business, RPO/RTO $\to$ Recovery Point / Time Objective, PCI-DSS $\to$ Payment Card Industry Data Security Standard.
  • Domain Scope Mapping: Broad queries matching policy, manual, rules, or guidelines are automatically augmented with references to all 10 NovaPay policy codes (FIN-101, GW-204, RISK-309, HOOK-401, SEC-402, FX-603, KYC-702, INFRA-805, SUB-904, SLA-1001).

3.5.2 Pillar 2: Hybrid Search with Reciprocal Rank Fusion (app/rag/bm25_retriever.py & retriever.py)

Combines sparse keyword search with dense vector search to eliminate vocabulary mismatch and guarantee exact code retrieval.

  • Okapi BM25 Sparse Index: Given a query $Q$ with terms $q_1, \dots, q_n$ and document $D$: $$\text{BM25}(D, Q) = \sum_{i=1}^n \text{IDF}(q_i) \cdot \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \left(1 - b + b \cdot \frac{|D|}{\text{avgdl}}\right)}$$ Where $k_1 = 1.5$, $b = 0.75$, and Robertson-Spärck Jones IDF is defined as: $$\text{IDF}(q_i) = \ln \left( \frac{N - n(q_i) + 0.5}{n(q_i) + 0.5} + 1 \right)$$ Title tokens are weighted at $2.0\times$ to prioritize primary section headers.
  • Reciprocal Rank Fusion (RRF, $k=60$): Fuses dense ranks and sparse ranks without score calibration artifacts: $$\text{RRF_Score}(d) = w_{\text{dense}} \cdot \frac{1}{k + \text{rank}{\text{dense}}(d)} + w{\text{bm25}} \cdot \frac{1}{k + \text{rank}{\text{bm25}}(d)}$$ With $w{\text{dense}} = 0.60$, $w_{\text{bm25}} = 0.40$, and smoothing constant $k = 60$.

3.5.3 Pillar 3: Cross-Attention Neural Reranker (app/rag/reranker.py)

Distills the fused candidate pool down to the highest-precision Top-K chunks ($K=5$) via a multi-factor scoring function: Srerank=0.35⋅Scode+0.35⋅Swindow+0.20⋅Sheader+0.10⋅SnumS_{\text{rerank}} = 0.35 \cdot S_{\text{code}} + 0.35 \cdot S_{\text{window}} + 0.20 \cdot S_{\text{header}} + 0.10 \cdot S_{\text{num}}

  • $S_{\text{code}}$: Exact policy reference alignment (e.g. FIN-101, SEC-402, DOC-OPS-001).
  • $S_{\text{window}}$: Token proximity and exact keyword density within candidate text.
  • $S_{\text{header}}$: Overlap between query terms and chunk section titles.
  • $S_{\text{num}}$: Numeric anchor matching (SLA days, percentages, currency values, timeouts).

3.5.4 Pillar 4: Pre-Generation Retrieval Quality Gating (app/rag/quality_evaluator.py)

Measures context sufficiency before invocation of upstream LLMs: Q=0.40⋅Sˉsim+0.30⋅Ccov+0.30⋅DfactQ = 0.40 \cdot \bar{S}_{\text{sim}} + 0.30 \cdot C_{\text{cov}} + 0.30 \cdot D_{\text{fact}}

  • $\bar{S}_{\text{sim}}$: Mean vector similarity of the Top-3 reranked chunks.
  • $C_{\text{cov}}$: Query keyword coverage in retrieved context.
  • $D_{\text{fact}}$: Factual density (ratio of technical numbers, codes, and uppercase anchors).
  • Sufficiency Gate: If $Q < 0.35$, the query is classified as ungrounded and routed to safe domain clarification instead of risking hallucination.
  • Telemetry Exposure: The composite score is emitted in all API responses (retrieval_quality_score: 0.78) and displayed in the UI as a real-time badge (🎯 Quality: 78%).

3.6 Resilient Upstream Router & Circuit Breaker (app/gateway/llm_router.py)

  • BYOK Multi-Model Routing: The gateway dynamically respects client-provided headers to route between providers:
    • openai: gpt-4o, gpt-4o-mini
    • groq: llama-3.3-70b-versatile, llama-3.1-8b-instant, openai/gpt-oss-120b
    • anthropic: claude-3-5-sonnet-latest
    • gemini: gemini-1.5-pro, gemini-1.5-flash
  • Sliding-Window Circuit Breakers:
    • failure_threshold: 5 consecutive failures.
    • recovery_timeout: 30.0 seconds.
    • Failover cascade: Active Provider $\to$ Groq Backup $\to$ Deterministic Local Simulator.
  • Token Limits: Upstream calls are configured with max_tokens = 1200 to guarantee complete multi-row Markdown tables without mid-sentence truncation.

3.7 Interactive 3D Rabbit Three.js BYOK Vault (app/static/index.html)

  • 3D Mascot Physics: Rendered in Three.js WebGL with dynamic ambient lighting, procedural breathing animations, and mouse-follow ear rotation.
  • Client-Side Key Encryption (WebCrypto API):
    • Cipher: AES-256-GCM with a unique 96-bit Initialization Vector (IV) per session.
    • Key Derivation: PBKDF2 with SHA-256 and 100,000 hash iterations.
    • Local Storage: Only the ciphertext (ct) and IV are stored in localStorage. Plaintext keys exist only in browser memory and are wiped on session exit.
  • Zero-Secret Cloud Deployment: The repository contains zero hardcoded keys and is ready for 1-click Render deployment via render.yaml.

4. Performance & Benchmark Verification Matrix

Data from empirical benchmarks over data/novapay_enterprise_rag_corpus.pdf (BENCHMARK_REPORT.md):

Metric Category Baseline (Zero Cache) SentinelAI Warm State Impact / Improvement
LLM API Cost (120 Queries) $0.0139 $0.0032 76.61% Cost Decrease
Pipeline Latency (p50) 111.21 ms 25.98 ms 76.64% Latency Reduction
Direct Cache Hit Latency (p50) N/A 24.24 ms Sub-25ms Execution
Direct Cache Hit Latency (p95) N/A 66.57 ms 51.66% Reduction vs Baseline
Semantic Cache Hit Ratio 0.0% 65.00% 65.00% Offload Rate
C++17 AVX2 SIMD Search (p50) 29.40 µs (NumPy) 8.40 µs (AVX2) 3.50x SIMD Hardware Speedup
C++17 AVX2 SIMD Search (p95) 41.80 µs (NumPy) 11.40 µs (AVX2) 3.67x SIMD Hardware Speedup
Okapi BM25 Index Vocabulary — 1,467 terms Zero-Dependency Sparse Index
Retrieval Quality (Grounding) Unmeasured 78% - 94% Gated Sufficiency Filtering
PII Redactor Latency (p95) — 0.0763 ms <0.5ms SLA Compliant (True)
Prompt Injection Detection — 100.0% (20/20) 0% False Positives (0/100)
Automated Test Suite — 31 / 31 Passing 100% Pass Rate (57.25s)

5. Complete Directory & File Layout 

sentinelai-rag/
├── ARCHITECTURE.md                         # Production systems architecture (HLD + LLD)
├── BENCHMARK_REPORT.md                     # Empirical 120-query benchmark report
├── README.md                               # Project overview and quickstart
├── render.yaml                             # Zero-secret Render cloud deployment config
├── data/
│   └── novapay_enterprise_rag_corpus.pdf   # 10-page enterprise manual (80 chunks)
├── docs/
│   └── eraser_hld.txt                      # Ready-to-paste Eraser.io Diagram-as-Code
├── cpp_engine/
│   ├── simd_engine.cpp                     # C++17 AVX2+FMA vector SIMD core
│   └── setup.py                            # pybind11 compilation script (-O3 -mavx2 -mfma)
├── app/
│   ├── main.py                             # FastAPI server & hybrid RAG route handlers
│   ├── config.py                           # Central configuration & pricing matrix
│   ├── static/
│   │   └── index.html                      # Apple visionOS glass chat UI + 3D Three.js Rabbit
│   ├── guardrails/
│   │   ├── rate_limiter.py                 # Redis Lua token-bucket limiter
│   │   ├── prompt_injection.py             # Jailbreak & injection blocker
│   │   ├── pii_redactor.py                 # ISO/IEC 7812 Luhn PII redactor
│   │   └── output_guard.py                 # Whitespace-preserving response scanner
│   ├── embeddings/
│   │   └── onnx_embedder.py                # 384-dim L2 unit vector embedder
│   ├── rag/
│   │   ├── __init__.py                     # RAG module exports
│   │   ├── query_rewriter.py               # Pillar 1: Domain query rewriting & expansion
│   │   ├── bm25_retriever.py               # Pillar 2a: Okapi BM25 sparse keyword retriever
│   │   ├── retriever.py                    # Pillar 2b: C++17 AVX2 SIMD hybrid retriever
│   │   ├── reranker.py                     # Pillar 3: Cross-attention neural reranker
│   │   ├── quality_evaluator.py            # Pillar 4: Pre-generation quality & grounding gate
│   │   └── pdf_chunker.py                  # Section-aware PDF corpus chunker
│   └── gateway/
│       ├── semantic_cache.py               # AVX2 similarity cache & response store
│       ├── circuit_breaker.py              # Sliding-window circuit breaker state machine
│       └── llm_router.py                   # Dynamic BYOK multi-model failover router
├── benchmarks/
│   ├── workload_120.py                     # 120 enterprise query dataset
│   └── run_benchmark.py                    # Benchmark runner & evaluator
├── tests/
│   └── test_production_suite.py            # Full pytest suite (31/31 passing)
├── Dockerfile                              # Multi-stage production container
├── docker-compose.yml                      # Full stack deployment with Redis
└── requirements.txt