AIUC-1
Agent-level certification evidence across data, security, safety, reliability, accountability, and societal risk.
AI Governance · Compliance Evidence
One evaluation infrastructure. Every framework.
Frameworks tell you what to track. They never say what good enough looks like. TrustEvals sets the baselines and produces continuous, framework-mapped evidence from one trace pipeline. ISO 42001, NIST AI RMF, EU AI Act, AIUC-1, and SR 11-7, all on the same evidence.
One trace pipeline. Every evidence output.Trace data in.
Trace data in.
Framework packs out.
Continuous evidence
pipeline
pipeline
SR 11-7 AI
Model-risk style documentation, validation, monitoring, and change control.
ISO 42001
AI management-system evidence mapped to operating controls and ownership.
NIST AI RMF
Govern, Map, Measure, and Manage signals from the same trace pipeline.
EU AI Act
High-risk obligations, technical-file inputs, and human oversight evidence.
Baseline problem
Trace data in. Framework packs out.
Compliance is anchored on baselines per use case: the framework names what to track, but the production trace proves whether the AI holds.
ISO 42001
Clauses 4-10, audited body, and the certification track procurement asks for.
NIST AI RMF
GOVERN, MAP, MEASURE, MANAGE, plus the GenAI Profile.
EU AI Act
High-risk risk management, Annex IV documentation, human oversight, and post-market monitoring.
SR 11-7 AI
Model inventory, validation, monitoring, change control, and ongoing performance review.
AIUC-1
Data and privacy, security, safety, reliability, accountability, and societal risks.
Signal
Production traces tagged with classification, source, baseline, policy outcome, owner, and timestamp.
Engine
Evaluate against per-use-case baselines, detect drift, apply versioned policy, and preserve source lineage.
Outputs
ISO packet, NIST profile, EU Annex IV file, AIUC-1 attestation, SR 11-7 model file, and owner-ready exception log.
Sequencing posture
Choose continuous, periodic, layered, or deferred honestly.
The posture matrix prevents teams from buying a static stamp when production AI needs a live evidence stream.
Continuous
In production, customer-facing, or under regulator scrutiny. Run continuous evidence.
Periodic
One-time stamp or procurement gate. Use the evidence pack, but do not confuse it with live assurance.
Layered
Both are needed. Sequence the live trace pipeline first, then assemble framework packs from it.
Deferred
Pre-production or internal pilot. Start with a Quick Audit before building a compliance program.
Do you certify us?
No. TrustEvals produces the evidence a certifier, auditor, customer, or risk owner needs, plus proof it is still accurate next month.
What if the regime changes?
Changes land at the mapping layer. The source evidence stays: traces, baselines, owners, policy outcomes, incidents, and version history.
Can one engagement cover all frameworks?
Yes. The same infrastructure feeds multiple framework packs; framework work is the mapping layer, not a second evidence pipeline.
Is TrustEvals certified?
SOC 2 is on the roadmap. We disclose current status and do not claim what we do not have.
01
AIUC-1
Agent-level certification evidence across data, security, safety, reliability, accountability, and societal risk.
Open detail
02
SR 11-7 AI
Model-risk style documentation, validation, monitoring, and change control.
Open detail
03
ISO 42001
AI management-system evidence mapped to operating controls and ownership.
Open detail
04
NIST AI RMF
Govern, Map, Measure, and Manage signals from the same trace pipeline.
Open detail
05
EU AI Act
High-risk obligations, technical-file inputs, and human oversight evidence.
Open detail
Evidence trail
The number only matters when the work beside it is visible.
Each proof artifact now shows what changed, what TrustEvals installed, what evidence was captured, and where the reader can inspect the case.
Evidence cases
AI-native finance SaaS
A release gate the product team and customers could inspect.
95%stated accuracy after the deploy-gate work
Before
~60% FP&A accuracy and repeated double-checking before release.
01Golden set
02Regression DAG
03Reviewer checks
04Release decision
Result
95% stated accuracy, about 90% measured, with 144% NRR provenance kept beside the claim.
- 90+ scenarios
- deterministic SQL fast paths
- reviewer-agent checks
- claim labels kept explicit
AI-native finance SaaS / FP&A95% stated accuracy, ~90% measured, 144% NRRFinance SaaS false positives20% fewer false positives, rollout to 100+ customersUS commercial real estate$20M modeled 10-yr NOI/NPVTrust harnessnew frameworks map on top, not a re-plumbProof disciplineclaims stay tied to engagement evidence
From uncertain FP&A accuracy to a deploy gate our customers could review.
CTO, AI-native finance SaaSTrustable, reliable AI in production
Start with the AI work that moves the number. Keep the proof built in.
Start with Strategy, Transformation, or Fluency; use Quick Audit when the first need is an independent read on what is already running.