For the Head of AI
Built for the leader the board holds accountable for AI.
You own AI for a finance enterprise, and the board wants a straight answer: where it is paying off, where it is exposed, and what to fund next. Your CEO, CFO, and CISO each need that answer in their own terms, and one read serves them all.
By seat
One read, four questions.
All four views are rendered from one evidence pipeline over your production AI, so the CEO’s value number and the CISO’s exposure map are the same data, formatted differently, not four separate projects.
CEO
Value capture
Where is AI changing throughput, revenue quality, decision speed, and strategic leverage?
Head of AI / CIO
Operating stack
What is deployed, embedded, duplicated, unmanaged, or ready to scale?
CFO
Spend to outcome
Which AI investments produce measurable operating value, and which are unproven AI spend?
CISO
Evidence and control
Where are shadow tools, agent paths, policy exceptions, and unresolved exposure?
Solutions hub
Start with the question already live.
You stay the owner. Every stakeholder, CEO, CFO, and CISO, gets the same read, framed for the question they actually ask.
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.
Situation
Entry point
What it does
We need to know what is already running
Quick Audit
Find tools, embedded AI, owners, shadow AI, MCP paths, and evidence gaps.
We know the workflow that should change the business outcome
Discovery Call
Scope the transformation or engineering workstream and the eval harness it needs.
We have production AI but cannot prove behavior
Evals
Set baselines, drift checks, release gates, and output-quality evidence.
Compliance or customers are asking for proof
Governance
Map trace data into framework packs, exception logs, and owner-ready evidence.
Capabilities
What one evidence pipeline gives every seat.
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.
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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