Prove your AI product behaves as claimed.
Evals are repeatable production-grade tests on real traces, named metrics, and release behavior. They turn “the model seems to work” into evidence a customer, board, or auditor can act on.
TrustEvals stands up the pipelines, red-team plugins, model-comparison harness, and prompt optimizer, then operates them with your team.
Measure production behavior by surface.
AI product companies need more than one accuracy number. The measurement layer has to test the real surface the customer touches.
Production chatbots
Intent accuracy, answer groundedness, refusal correctness, multi-turn consistency, and release drift.
Agentic and planning tools
Tool-use correctness, plan validity, sub-step verification, recovery behavior, and end-to-end task success.
RAG systems
Retrieval precision and recall, citation faithfulness, and hallucination rate against grounded sources.
Multi-tenant SaaS
Tenant-scoped evaluation under JWT isolation, with per-tenant accuracy, refusal, and policy adherence.
Model comparison
Metric deltas across model versions, providers, and fine-tunes so upgrade decisions have evidence.
Red-team surface
PII leakage, RBAC bypass, GDPR violations, SQL injection, prompt injection, hallucination, financial compliance, and IP risk.
Start standard. Go custom where risk demands it.
The choice is not a maturity badge. It is a scoping decision based on how much of your product behavior fits common agent patterns.
Standard Evals
Included: Pre-built pipelines for accuracy and groundedness; the 8-plugin red-team suite; model comparison; prompt optimization; CI execution layer; multi-tenant JWT.
Pick this when: Your agent fleet uses common patterns: chat, RAG, tool-use, or multi-tenant SaaS, and you want to run fast.
Custom Evals
Included: Standard pipeline plus customer-specific eval design, domain-specific red-team plugins, bespoke metrics, and integration with your CI/CD and observability stack.
Pick this when: Your agent fleet is domain-specialized, and the standard plugins miss the behavior or materiality threshold that matters.
Build the pipeline, then hand over the operating loop.
The deliverables you keep are the eval pipelines, red-team plugins, CI integration, dashboards, optimizer loop, and runbooks. The cadence continues after handoff.
| Phase | Standard | Custom | Output |
|---|---|---|---|
| Discovery | Week 1 | Weeks 1 to 2 | Surface inventory, risk taxonomy, and metric shortlist. |
| Pipeline stand-up | Weeks 2 to 4 | Weeks 3 to 6 | Eval pipelines and red-team plugins wired to your traces. |
| Calibration | Weeks 4 to 6 | Weeks 6 to 8 | Metric thresholds, refusal baselines, and tenant scoping. |
| CI and optimizer | Weeks 6 to 8 | Weeks 8 to 10 | CI gating, prompt optimizer loop, and dashboards live. |
| Handoff and ops | Weeks 8 to 10 | Weeks 10 to 12 | Runbooks, operating cadence, and ongoing partnership shape. |
After handoff: weekly metric review, monthly red-team refresh, and quarterly model-comparison sweeps, adjusted to your release cadence.
Four artifacts, one pipeline.
Each artifact is owned by your team and runnable inside your release motion. 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 feature.
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, and release gates. The measurement layer that every workstream reads from.
Evals are the measurement layer under the work.
Governance consumes this evidence, but Evals also feed the AI Audit proof layer, Transformation workflow measurement, Fluency telemetry, and AI Engineering release confidence.
- AI AuditProof for the operating read and the next funded move.
- AI TransformationWorkflow measurement for value, quality, and cycle-time deltas.
- AI GovernanceEvidence mapped to SR 11-7, ISO 42001, NIST AI RMF, and the EU AI Act.
- AI FluencyTelemetry that shows whether people are better at the actual work.
- AI EngineeringRelease confidence for AI-native finance product teams.
Start with the 2-week AI Audit.
Leave with the operating read: AI value, AI risk, fluency gaps, owners, and the next funded workstream.
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 features to customers in finance. Chatbots, agentic tools, retrieval pipelines, multi-tenant SaaS. The CTO, VP Engineering, or Head of AI owns the engagement.
Common agent patterns and speed point to Standard. Domain-specialized agent fleets point to Custom. We scope the right path during discovery.
Standard Evals usually reach initial infrastructure in 6 to 10 weeks. Custom Evals usually run 8 to 12 weeks. Both transition into an operating cadence after handoff.
Governance consumes the evidence, but Evals are not a Governance child. The same measurement layer feeds the AI Audit, Transformation, Governance, Fluency, and AI Engineering work.
Yes. Standard plugins cover PII, RBAC, GDPR, SQL injection, prompt injection, hallucination, financial compliance, and IP violations. Custom plugins can be authored for domain-specific risks.
No. Observability tells you what happened. Evals tell you whether what happened was correct. We integrate with your observability stack rather than replace it.