Industries we work in, finance first
AI value and risk change by the work you run.
Finance is where we go deepest: private equity, banks and capital markets, fintech, asset and wealth, real estate, and insurance. The broader delivery record spans education, manufacturing, food, agritech, cybersecurity, FP&A, and AI-native product work, expressed qualitatively and anonymized.
Private Equity
Portfolio-wide AI value capture and control coverage.
Regulated Finance
Exam-ready AI evidence across banking, wealth, payments, and lending.
Banks & Capital Markets
Regulator-defensible evidence under model-risk pressure.
Fintech
Ship reliable AI without losing enterprise buyers.
Asset & Wealth
Advisor, ops, and research workflows with evidence attached.
Real Estate
AI-driven NOI, leasing, valuation, and asset-model proof.
Insurance
Underwriting and claims AI that can be defended.
Healthcare
Evidence-linked, human-gated AI for medical coding and revenue cycle.
Manufacturing
Plant, quality, procurement, and service workflows made reliable.
By sector
What each sector actually gets.
Each priority sector names the operating evidence you receive, from exam-ready proof to defensible decisions.
Regulated Finance
Evidence that survives the next exam
Banks, credit unions, wealth, payments, lending, and capital-markets infrastructure.
Private Equity
A value and risk read the IC can underwrite
Portfolio-wide AI activity turned into fundable workflows and contained exposure.
Real Estate
NOI, leasing, valuation, and reporting proof
AI outputs tied to source records and economics an operator can defend.
Fintech
Reliable AI that clears enterprise buyers
Feature behavior, evals, and procurement evidence on one pipeline.
Insurance
Defensible underwriting and claims decisions
Controls and evidence for consequential outputs before they become the record.
Asset & Wealth
Source-linked proof behind advice and research
Recommendations, research, and client-facing AI you can defend to a client.
Healthcare
Coding and clinical workflows you can audit
Every suggested code traces to the chart and is human-gated before it posts.
Manufacturing
Plant and quality AI you can defend
Operational workflows instrumented for reliability, exception handling, and value.
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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