Add Dissei Data org profile card (signpost — routes to dissei.ai/research)

README.md

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-title: README
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Dissei Data
+**Outcome-graded RL environments for institutional financial judgment.**
+
+Dissei builds reinforcement-learning environments from real institutional credit
+decisions — each one a documented transaction rebuilt as an agentic task, worked with
+analyst tools and graded against what actually happened.
+
+- **Point-in-time by construction.** A model sees only what was knowable at the decision
+  date; every exhibit is anchor-gated, so hindsight is excluded structurally — not by an
+  instruction it can ignore.
+- **Graded, not checked.** Reward decomposes into fail-closed critical gates, an
+  expert-authored graded rubric, a contradiction penalty, and a retrieval modulator that
+  rewards grounding in the record.
+- **Grading automation, disclosed.** Rubrics are authored and anchored by credit
+  practitioners who carried real risk; execution runs on LLM judges at temperature 0 that
+  apply the sealed rubric and never invent criteria. Answers are graded twice — against
+  best practice, and against the realized outcome.
+- **Sealed by architecture.** Answer material is time-locked and anchor-gated; every asset
+  ships with a per-asset contamination statement and right-to-train lineage.
+- **Operators, not an annotation farm.** The corpus is the by-product of an operating
+  lending business — incentive-bearing decisions with real consequences, not opinions
+  commissioned for a dataset.
+
+Environments (RL post-training) and sealed evaluations are available to frontier labs
+**under agreement**. We do not publish tasks, rubrics, graders, or leaderboards.
+
+**Methods & findings:** https://dissei.ai/research
+**Contact:** info@dissei.credit · Mutual NDA before anything sensitive.