Services
AI Strategy, Transformation, and Fluency, built with proof from day one.
We set the AI strategy, transform the workflows where AI changes revenue, margin, or cycle time, and build workforce fluency around the new operating model. Governance, Audit, and Evals are built into every build, so every build is measurable, defensible, and ready for the board.
The service catalogue
Eight ways in, one proof discipline.
01
AI Strategy
Decide where AI pays before budget drifts. Vendor shortlist, build-versus-buy, rollout sequence, board case, and fluency baseline.
02
AI Transformation
Move AI into the work that changes the business, converting one priority workflow into a measurable gain in revenue, margin, or cycle time.
03
AI Governance
Prove what already runs holds. Shadow AI, policy exceptions, MCP paths, model-risk proof, and review cadence.
04
AI Fluency
Make the workforce fluent where work changed. Role-level capability, usage depth, review discipline, and durable enablement.
05
AI Engineering
Ship AI products at enterprise quality. Release gates, architecture hardening, and buyer-ready reliability evidence.
06
AI Audit
Get the quick independent assessment. A two-week board-ready opinion on value, exposure, owners, and evidence gaps.
07
Evals
Measure production behavior. Golden datasets, cheap checks before expensive ones, drift detection, and deploy gates.
Software, built into every build
08
Governance platform
The evidence platform your team runs after handover. Framework-mapped evidence, exception handling, and continuous audit trail.
Software, built into every build
How we deliver
We own the whole arc, then hand it back.
End-to-end delivery, run by a lean senior pod: one lead architect plus a handful of strong engineers, senior-heavy so the AI multiplier actually lands. The same architect runs scope to handover, so nothing degrades in the relay between a strategy deck and a build team.
Scope & discovery
Find the consequential workflow
Name the durable asset
PRD
Personas & jobs
Acceptance criteria
Success metrics
Reference architecture
Eval/governance spine wired in
Grounding & drift by design
AI-native build cycle
Weekly cadence
Demoable slice early
AI-accelerated
Eval & governance
Claim-level checks
Decision logs
Evidence vault
Deploy & handover
Runbooks
Shadowed team
Capability transfer
We build it, and we stay the independent check
Independent eval layer
Governance, Audit & Evals in every build
Not a bolt-on
One team with a GRC partner for regulated work
HIPAA / SOC2 / independence
Delivered work
What we have built, by sector.
Anonymized. Outcomes are described qualitatively; customers and partners stay confidential unless explicitly approved.
Healthcare
Explainable medical-coding assist
A coder-workflow app plus an evidence-linked autonomous-coding pipeline: OCR, clinical entity extraction, code mapping, confidence scoring, hallucination detection, and an independent reviewer model, every code traced to chart text and human-gated. Workflow app in production; the coding pipeline a POC with a launch partner.
Agri-food
Governed BI for a global processor
No-touch, read-only ERP extraction into a governed clean-data layer, then production, farmer-intelligence, and management dashboards that named owners actually run. Delivered and in production.
Manufacturing
Shift-level decisioning for a glass plant
A full ingestion-to-warehouse-to-dashboard stack with a custom dirty-data job-mapping engine, engineered efficiency KPIs, and shift-level defect and downtime tracking. Moved a manual-spreadsheet plant toward shift-level decisioning; in UAT.
Financial services
Agentic financial-analysis harness
Governed-context RAG over company P&Ls, multi-model agentic generation, and a maker-checker adversarial review, behind an eval trust layer that enforces accuracy, PII-safety, and enterprise authorization on every run. Raw P&L to senior-approved, eval-checked analysis.
Private markets
Cross-portfolio FP&A reporting
Embedded as a multi-company delivery partner across a family-office portfolio: per-company financial apps delivered, plus conversational natural-language reporting with role-based access, company scoping, and audit trails in build.
Education
Recruitment & placement automation
Student and placement workflow automation for a higher-education institution, in soft-launch, part of a delivery track that spans regulated and operating environments.
How deep it goes
The architecture motifs that recur in every build.
Every engagement runs the same five-stage method: teardown to the durable asset, a real PRD with acceptance criteria, a reference architecture with the eval spine wired in from the start, a phased AI-accelerated build with a demoable slice early, then hardening and handover. These are the patterns that recur underneath.
Enforcement
Block, don’t just report
Synchronous request-path guardrails are kept separate from the async observe-and-eval lane, so policy can actually block a bad call instead of only logging it after the fact.
Data residency
Run evals where the data lives
In-tenant, in-VPC eval runners return pass/fail plus hashes, never raw rows. Metadata-only, no-egress ingestion for regulated data, with literal scrubbing before any model call.
Evidence
Tamper-evident, not a bare hash
Append-only, actor-attributed audit logs with write-once storage and a hash-chain checkpointed externally, so the evidence trail holds up to an independent assessment.
Measurement
Calibrate the judge
Judge-to-human agreement is the attestation proof point. No automated pass-rate ships without a sample size and confidence interval; gates measure relative lift over a baseline, not brittle absolute accuracy on messy data.
Routing
Deterministic first, model second, human third
Where an objective oracle exists, numbers are computed in code and the model only narrates source-tagged figures. The model is a commodity input; the governed, evaluated layer around it is the asset.
Reuse
Multi-tenant-ready cores
Config-driven onboarding and per-tenant key isolation, with a clean background-IP carve-out, so proven components redeploy and per-deployment integration cost stops compounding.
How TrustEvals engages
Start with the live AI question, then install proof.
The work should feel like an operating path, not a menu. We identify the consequential AI workflow, install the evidence loop, and leave the team with a system they can keep running.
01
AI Audit
Find what is running, who owns it, and where evidence is missing.
02
Eval harness
Measure production behavior against baselines before output becomes record.
03
Reviewer loop
Route exceptions, approvals, and owner judgment into the work.
04
Deploy gate
Block or release high-stakes AI changes with evidence attached.
05
Evidence pack
Turn traces, baselines, and reviewer decisions into a board-ready record.
How we ship: a named TrustEvals practitioner embeds for the engagement window, hardens the measurement layer, then hands the operating loop to your team. The first read is scoped to the gap, not sold as one bundle.
Cybersecurity / compliance GRC firm: First Quick Audit that became the template for the ongoing view.
PE and portfolio companies: Transformation with board-ready value plus risk evidence.
AI-native product team: Evaluation infrastructure on production chatbot and agent fleets.
US commercial real estate: NOI-anchored AI agents and valuation evidence.
PE operating partner
Start with a portfolio read.
Value capture, control coverage, and repeatable board evidence across companies.
CIO / CFO / CISO
Start with the live executive question.
Spend, control, production behavior, or evidence freshness becomes the first measurable workstream.
AI-native product team
Start with release reliability.
Baselines, regression suites, reviewer loops, and production traces before customer-visible output ships.
Is the AI Audit a fourth pillar?
No. It is the entry read: the fast independent view of what is running, what is working, what is exposed, and which workstream should start.
Do we run every workstream?
No. Start with one or two. The first read sizes the gap, then the follow-on work is scoped to the operating problem, not sold as one bundle.
How does this work with our Big-4, boutique, or in-house partner?
We complement consulting teams by making AI recommendations measurable: eval pipelines, trace evidence, observability, owner review, and an operating loop your team can keep running.
Do we have to engage services to use the platform?
No. The platform is the product. Services exist when the platform needs to land against a real operating problem, with a named practitioner for the engagement window.
One clear view
Not the delivery pipeline. The assessment it leaves behind.
Where each service lane points, the question it answers, and the artifact it hands back.
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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