Platform
The evaluation and evidence layer under every build.
One pipeline captures what your AI does in production, checks it against a bar you set, and produces framework-mapped proof. ISO 42001, NIST AI RMF, SR 11-7, AIUC-1, and the EU AI Act map on top without re-plumbing.
Platform routes
Twelve cards, two jobs.
The first seven cards are where AI runs and gets seen: gateways, scanners, identity, devices, SaaS admin, code, and observability. The last five are the trust harness that turns that activity into a board-readable evidence stream: Sources, Trace, Baseline, Framework, and Memo. Open any card for the detail.
01
Gateways
AI gateways, browser agents, MCP paths, copilots, embedded SaaS AI, and approved vendor surfaces.
02
Scanners
Discovery passes that find sanctioned tools, shadow AI, duplicated spend, and workflow-level usage.
03
Identity
Owner, reviewer, user, role, and approval context attached to each material AI output.
04
Devices
Endpoint and browser context for AI work that happens outside a central platform console.
05
SaaS admin
Admin and billing evidence that connects license posture, access, and embedded AI features.
06
Code
Agent, app, and workflow code paths tied to deploy gates, traces, and reviewer checks.
07
Observability
Production traces, drift signals, exceptions, and behavior evidence that prove the workflow held.
08
Sources
The systems that speak: connectors, exports, logs, and system-of-record evidence.
09
Trace
What happened in the workflow, including source lineage and reviewer decisions.
10
Baseline
What good means before the output becomes the record: eval harnesses and success bars.
11
Framework
Which rulebook applies, with mappings layered on top of the same evidence stream.
12
Memo
The board and audit pack that turns operating evidence into a readable decision artifact.
The operating panel
One panel, from AI source to board memo.
Each layer answers one question and hands back one output, all from the same trace.
Capabilities
What the platform gives your AI team.
Evals
Measure production behavior before the output becomes the record.
Evals run on real production traces, not eval-set demos. Every release is gated on behavior, policy adherence, and drift before the output is trusted as the system of record.
Continuous evaluation on live traces
Policy and drift gates on every release
Source-anchored, reviewable evidence
Reusable evidence register
Turn one evidence register into memos, maps, packs, and board updates.
Governance
Connect every AI source to policy evidence, ownership, and exception handling.
Operating view
See value, risk, fluency, and control signals in one decision-ready view.
Agent behavior
Agent-behavior evaluation is where the evidence goes deepest.
The five-layer trust harness is strongest where agent behavior becomes observable: tool calls, traces, reviewer decisions, policy exceptions, drift, and release gates tied to the workflow.
01
Behavior, not demos
Evaluate agents against production traces, role boundaries, tool authorization, groundedness, and reviewer outcomes.
02
One baseline per use case
Set the bar before the output becomes the record, then preserve the result as evidence for Governance and Audit.
03
Frameworks map on top
ISO 42001, NIST AI RMF, SR 11-7, AIUC-1, and EU AI Act packs reuse the same agent-behavior evidence instead of creating new pipelines.
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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