Helion
Helion is the internal AI platform for the research division of a tier-1 investment bank. It serves analysts across rates, equities, and credit with retrieval, synthesis, and structured reasoning systems — under regulatory and reputational constraints that do not tolerate ambiguity.
01 — Problem
The client had run two years of pilots and accumulated thirty-seven internal LLM tools, none of which had passed compliance review. Every promising prototype died in the gap between research interest and production discipline.
What was missing was not models. What was missing was infrastructure for trust.
02 — Approach
We built Helion as a platform whose primary product is observability — every retrieval, prompt, completion, and citation is captured, evaluated, and traceable to a source document. Models are interchangeable; the surrounding system is not.
Evaluation was treated as a first-order engineering concern, not a checklist. Domain experts contributed to a continuously-running evaluation suite that any production change must clear before promotion.
03 — System
A retrieval architecture with semantic, lexical, and structured signals; a prompt and tool registry with versioning and rollback; evaluation harnesses for grounding, factuality, and analyst alignment; and an audit surface that satisfied compliance from day one.
The platform is model-agnostic by design. It has been migrated across three frontier models in production with zero downtime.
04 — Outcome
Helion now serves more than 2,400 analysts daily and is the only AI surface approved for production research output across the firm. Time-to-first-draft on standard reports has fallen by 64%. Hallucination rates on citation-bearing answers measure below 0.4% on the live evaluation suite.
Measured, not asserted.
Daily analysts served
2,400+
Time-to-draft reduction
−64%
Citation hallucination rate
<0.4%