Value capture
Where is AI changing throughput, revenue quality, decision speed, and strategic leverage?
For the Head of AI
Built for the leader the board holds accountable for AI.
Primary buyer: CIO, Head of AI, Head of AI Transformation, or newly hired CAIO in a finance enterprise. CEO, CFO, CISO, and audit committee are message variants; PE Operating Partners and AI-native finance CTOs are distinct secondary doors.
Operating stack
What is deployed, embedded, duplicated, unmanaged, or ready to scale?
Spend to outcome
Which AI investments produce measurable operating value, and which are unproven AI spend?
Evidence and control
Where are shadow tools, agent paths, policy exceptions, and unresolved exposure?
Solutions hub
Start with the question already live.
The hub keeps the Head of AI as primary owner, then thickens the CEO, CIO, CFO, and CISO frames with behavior-led routing.
CEO / Board
Board-ready AI value and risk: where AI changes the plan, where proof is missing, and what should be funded.
Head of AI / CIO
Approved tools, embedded SaaS AI, internal agents, shadow AI, owners, spend, and workflow evidence.
CFO
License waste, duplicate tools, cost per useful workflow, and the business case behind the next AI dollar.
CISO
Shadow AI, production traces, policy evidence, framework readiness, and control gaps.
We need to know what is already runningQuick AuditFind tools, embedded AI, owners, shadow AI, MCP paths, and evidence gaps.
We know the workflow that should move the numberDiscovery CallScope the transformation or engineering workstream and the eval harness it needs.
We have production AI but cannot prove behaviorEvalsSet baselines, drift checks, release gates, and output-quality evidence.
Compliance or customers are asking for proofGovernanceMap trace data into framework packs, exception logs, and owner-ready evidence.
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.