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. Framework packs out.
Produce the evidence once from a continuous evidence pipeline, then each framework pack maps on top without a second data pipeline. TrustEvals is not the certifier: we produce the evidence your certifier, auditor, and risk owner rely on, and the proof it is still accurate next month.
AIUC-1
Agent-level certification evidence across data, security, safety, reliability, accountability, and societal risk.
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
The framework names it. The trace proves it.
Compliance is anchored on baselines per use case: the framework names what to track, but the production trace proves whether the AI holds.
International · Certifiable
ISO 42001
Clauses 4-10, accredited certification body, and the certification track procurement asks for.
EU · Binding law
EU AI Act
Risk management for high-risk systems, Annex IV documentation, human oversight, and post-market monitoring.
US banking · Supervisory
SR 11-7 AI
Model inventory, validation, monitoring, change control, and ongoing performance review.
US · Private standard
AIUC-1
Data and privacy, security, safety, reliability, accountability, and societal risks.
One trace, captured once.
Signal
Production traces tagged with classification, source, baseline, policy outcome, owner, and timestamp.
Evaluated against baselines.
Engine
Evaluate against per-use-case baselines, detect drift, apply versioned policy, and preserve source lineage.
No second pipeline.
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.
Evidence trail
Proof you can inspect, case by case.
Each case shows what changed, what we built, the evidence we captured, and where you can check it. Every number carries its honest qualifier.
AI-native finance SaaS
A release gate the product team and customers could inspect.
Before: ~60% FP&A accuracy and repeated double-checking before release.
Result: 95% stated accuracy, about 90% measured. The customer ran 144% NRR alongside the reliability work.
Golden set → Regression suite → Reviewer checks → Release decision
90+ scenarios
deterministic SQL fast paths
reviewer-agent checks
sourced claim register
Open evidence →
Finance SaaS release confidence
Critical outputs stopped moving without enough release proof.
Before: High-stakes outputs needed re-checking because the team lacked a shared proof layer.
Result: 20% fewer false positives and a rollout path to 100+ customers.
Criticality map → Golden scenarios → Reviewer loop → Customer rollout
weighted evaluation graph
scenario ownership
release notes
customer-facing proof
Open evidence →
US commercial real estate
A contested valuation became one evidence pack.
Before: Six fragmented sources and no board-ready record behind the valuation.
Result: ~$20M modeled 10-year NOI uplift (NPV basis), tied to source logic and predicted year-end valuation.
Source register → Valuation logic → Evidence pack → Board read
six sources unified
NOI assumptions preserved
model exceptions
year-end valuation trail
Open evidence →
95%
FP&A accuracy · stated (~90% measured)
144%
net revenue retention · finance-SaaS customer, alongside the reliability work
$20M
NOI / NPV uplift · modeled
90+
regression scenarios · eval gate
Stated, not an audited fact. Modeled, not realized. The discipline we sell is the discipline we hold ourselves to.
Buyer evidence
From uncertain FP&A accuracy to a deploy gate our customers could review.
CTO, AI-native finance SaaS
The number finally had the mechanism beside it: the evaluation graph, reviewer checks, and deterministic paths.
Finance product team
The valuation model finally had one evidence pack behind the number.
Transformation lead, real estate operator
The same trace that helped the build also fed the governance memo.
Risk leader, regulated team
Specialist AI builder
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.
TrustEvals
Strategy, transformation, fluency. Governance, audit, and evals built in.
TrustEvals is owned by DataFortress, Inc.
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