Evaluate before you ship.
For finance. Production evals that measure real model behavior on real traces. Drift, hallucination, refusal correctness, policy adherence, multi-turn consistency.
Evals are how a finance AI team turns 'we tested it on staging' into evidence a regulator, an auditor, and a CISO will accept. Continuous, not point-in-time.
Four moves, one pipeline.
Each move produces a durable artifact your team owns and runs in CI. The harness, the dataset, the metric set, the evidence pack.
Trace harness
Production traces captured into a measurement engine. One source of truth for the operating view and the audit pack. Wired into your existing stack, not a parallel one.
Eval set and golden datasets
Seeded from real customer interactions, curated against the policies your finance buyer cares about. Versioned, reviewed, owned by the team that ships the model.
Behavior metric pack
Accuracy, groundedness, refusal correctness, policy adherence, multi-turn consistency, drift. Measured per release, per cohort, per surface.
Framework-mapped evidence
The same traces, mapped to the SR 11-7, ISO 42001, NIST AI RMF, and EU AI Act artifacts an auditor or buyer will pull on demand.
Evals produce the measurement layer.
Trace pipelines, behavior metrics, golden datasets, red-team plugins. The measurement layer that any framework reads from.
AI Governance turns trace data into evidence.
Evals are the measurement layer. AI Governance is the assurance face of the same data. The same trace pipeline produces both.
- SR 11-7Model risk management for finance examiners.
- ISO 42001AI management system certification track.
- NIST AI RMFGovern, map, measure, manage artifacts.
- EU AI ActHigh-risk obligations on the same trace data.
Book the AI Audit.
Thirty minutes to size the discovery surface: employees, devices, SaaS admin access, developer tooling, internal agents, Shadow AI exposure, and the outcome read you need at the end.
Questions buyers actually ask.
A repeatable test that measures whether an AI system produces the behavior its builders claim. Run on real production traces, against named metrics like accuracy, groundedness, refusal correctness, and policy adherence. Not a vibe check.
AI product teams shipping LLM-backed surfaces to customers in finance. Chatbots, agentic tools, retrieval pipelines, multi-tenant SaaS. The CTO, VP Engineering, or Head of AI owns the engagement.
Evals produce the artifacts governance frameworks ask for. The same trace data feeds the operating view and the framework-mapped evidence pack. One pipeline, two outputs.
No. Observability tells you what happened. Evals tell you whether what happened was correct. We integrate with your observability stack rather than replace it.