The measurement layer that makes AI trustable.

Deployed is not the same as working. Most production AI never moves the number because its output becomes the record before anything proves it earned it. Evals are the repeatable, production-grade tests on real traces that prove it did, named metrics, release behavior, evidence a customer, board, or auditor can act on.

This is the substrate beneath every workstream. The same trace data measures value capture, proves policy adherence, catches drift, and feeds the audit. Value on one side, control on the other, one measurement engine.

Tensile AI stands up the pipelines, red-team plugins, model-comparison harness, and prompt optimizer, then operates them with your team.

CTA: Book a discovery call

Secondary CTA: Book a quick audit

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.

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.

Put a measurement layer under the work.

Book a discovery call to scope evals against the surface your customers touch, or get the quick audit for a fast independent read on whether your AI holds. Either way, you leave with proof, not a vibe check.

CTA: Book a discovery call

Secondary CTA: Book a quick audit

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.