01
Trace harness
Production traces captured into one measurement engine, wired into your existing stack rather than a parallel one. It is the single source that both the live operating view and the audit pack read from.
New, with Accorian: a real-time AI governance framework for control drift in enterprise AI.Read the framework
Product
Evals
The measurement layer that makes AI trustable and reliable in production. Repeatable tests on real traces for behavior, policy adherence, and drift, so a customer, board, or auditor acts on evidence instead of a vibe check.
Layers5
Shared core1-3
Frameworksmapped
The five-layer trust harness sits under every build.
Every claim in the report traces back to source evidence, ownership, and the workflow decision it supports.
Valuefund next
Riskcontain now
Fluencytrain where work changed
What we build
The measurement layer, made of parts your team owns.
We stand up each piece on your traces and hand it back runnable inside your release motion, not as a black box you rent.
02
Golden datasets and eval sets
Test sets seeded from real customer interactions and curated against the policies your buyer cares about. Versioned, reviewed, and owned by the team that ships the model, so the bar travels with the product.
03
Behavior metric pack
Accuracy, groundedness, refusal correctness, policy adherence, multi-turn consistency, and drift, scored per release, per cohort, and per feature instead of one blended accuracy figure.
04
Red-team suite
An eight-plugin battery covering PII leakage, access-control bypass, GDPR, SQL and prompt injection, hallucination, financial compliance, and IP risk, run on the same traces as the accuracy metrics.
05
Model comparison and prompt optimizer
Metric deltas across model versions, providers, and fine-tunes so an upgrade decision has evidence, plus an optimizer that searches for the prompt wording that actually moves those metrics.
06
CI gating and deploy gates
The eval suite runs inside your pipeline and blocks a release when behavior regresses, so a change reaches production only after it clears the scenarios that matter.
How the engagement runs
Build the pipeline, then hand over the loop.
We stand up the pipelines, red-team plugins, comparison harness, and optimizer, then operate them with your team and hand back a loop it keeps running. Scoping starts standard where your fleet fits common agent patterns, and goes custom where domain behavior needs its own plugins and metrics. Named phases, scoped per engagement.
Discovery
surface inventoryrisk taxonomymetric shortlist
Pipeline stand-up
evals wired to your tracesred-team plugins
Calibration
metric thresholdsrefusal baselinestenant scoping
CI and optimizer
CI gatingoptimizer loopdashboards
Handoff and ops
runbooksoperating cadence
Pipelines you own
eval and red-team pipelinesCI integration
Evidence on demand
live dashboardsframework-mapped pack
Use cases
Measure the surface the customer actually touches.
One accuracy number hides the failures that matter. The measurement layer tests each surface on its own terms.
01
Production chatbots
Intent accuracy, answer groundedness, refusal correctness, multi-turn consistency, and drift between releases, so a support or advisory bot stays right as the model and prompts change underneath it.
02
Agentic and planning tools
Tool-use correctness, plan validity, sub-step verification, recovery behavior, and end-to-end task success, because an agent that picks the wrong tool fails in ways a single answer score never sees.
03
RAG systems
Retrieval precision and recall, citation faithfulness, and hallucination rate measured against grounded sources, so a retrieval-backed answer is judged on whether it is actually supported.
04
Multi-tenant SaaS
Tenant-scoped evaluation under token isolation, with per-tenant accuracy, refusal, and policy adherence, so one customer's behavior and data never bleed into another's.
05
Model and version comparison
Metric deltas across model versions, providers, and fine-tunes, so a migration or upgrade ships on evidence rather than a demo that looked good in the room.
06
Red-team surface
Adversarial probes for PII leakage, access-control bypass, GDPR, SQL and prompt injection, hallucination, and IP risk, run continuously rather than as a one-time penetration test.
What the measurement layer changed
Shipped is not the same as working.
Representative outcome once the eval harness became the deploy gate. 95% stated, about 90% measured. Modeled and self-reported, not an audited fact.
Challenges
The failures this catches before your customer does.
01
Deployed is not working
A feature demos well and ships, then quietly gets a share of its answers wrong in production. Without repeatable tests on real traces, no one knows until a customer or regulator points it out.
02
Regression between releases
A model swap, a prompt edit, or a new tool silently breaks behavior that used to work. Evals in CI make that regression visible before the release goes out, not after the support ticket.
03
One number hides the surface
A single blended score can look healthy while refusals, citations, or a specific tenant are failing. Per-surface, per-cohort metrics show where the system is actually weak.
04
The audit scramble
When a buyer's security team or an auditor asks for evidence, teams reconstruct it by hand under deadline. The same traces, already mapped to SR 11-7, ISO 42001, NIST AI RMF, and the EU AI Act, are pulled on demand instead.
Questions buyers ask
Direct answers.
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.
Specialist AI builder, across the board
One builder, across the board.
We take your AI from strategy to outcome, with governance, audit, and evals built into every build. Start with a discovery call, or a quick audit.