Gateways
AI gateways, browser agents, MCP paths, copilots, embedded SaaS AI, and approved vendor surfaces.
Platform
The five-layer trust harness under every build.
One trace pipeline supports Strategy, Transformation, and Fluency work. Layers 1-3 are the shared source, trace, and baseline core; frameworks like ISO 42001, NIST AI RMF, SR 11-7, AIUC-1, and the EU AI Act map on top without a re-plumb.
Layers5
Shared core1-3
Frameworksmapped
The five-layer trust harness sits under every strategy, transformation, and fluency build.
Every claim in the read traces back to source evidence, ownership, and the workflow decision it supports.
Valuefund next
Riskcontain now
Fluencytrain where work changed
Platform routes
Each layer opens into a concrete evidence surface.
The operating panel above is a navigation surface: menu items and table rows land on the gateway, trace, baseline, framework, and memo details that make the platform inspectable.
Scanners
Discovery passes that find sanctioned tools, shadow AI, duplicated spend, and workflow-level usage.
Identity
Owner, reviewer, user, role, and approval context attached to each material AI output.
Devices
Endpoint and browser context for AI work that happens outside a central platform console.
SaaS admin
Admin and billing evidence that connects license posture, access, and embedded AI features.
Code
Agent, app, and workflow code paths tied to deploy gates, traces, and reviewer checks.
Observability
Production traces, drift signals, exceptions, and behavior evidence that prove the workflow held.
Sources
The systems that speak: connectors, exports, logs, and system-of-record evidence.
Trace
What happened in the workflow, including source lineage and reviewer decisions.
Baseline
What good means before the output becomes the record: eval harnesses and success bars.
Framework
Which rulebook applies, with mappings layered on top of the same evidence stream.
Memo
The board and audit pack that turns operating evidence into a readable decision artifact.
Lever 09
Agent-behavior evaluation is the deepest moat.
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.
Behavior, not demos
Evaluate agents against production traces, role boundaries, tool authorization, groundedness, and reviewer outcomes.
One baseline per use case
Set the bar before the output becomes the record, then preserve the result as evidence for Governance and Audit.
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.
EvalsOpen evalsEditable outputsOpen editable outputsGovernanceOpen governanceOperating read
Measure production behavior before the output becomes the record.
prod outputFP&A variance explanationsource lineage attached
01trace02baseline03review04gate
release95% stated / ~90% measured
Turn one evidence register into memos, maps, packs, and board updates.
Evidence registerfresh
SourceFP&A warehouselinked
OwnerFinance productassigned
Riskcustomer-visible outputreview
Decisionrelease approved95%
Evidence90+ regression scenariosattached
Connect every AI source to policy evidence, ownership, and exception handling.
CopilotChatGPTembedded SaaS AIinternal agentsproduction tracespoliciesworkflow evidence
See value, risk, fluency, and control signals in one decision-ready view.
Open operating read
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.