README.md

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# 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 We do not publish tasks, rubrics, graders, or leaderboards.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.