Turn AI into an operating plan finance can defend.
Finance teams need a defensible read on where AI is already changing work, where it is creating risk, and which workflow deserves the next dollar. The AI Audit gives that read; services close the gap by transforming the workflow, building the evidence, or lifting the team.
Two-week first read. Follow-on work is sized to the gap, not sold as a menu.
One read first. Then the right workstream.
The Audit separates strategy from transformation, current state from steady state, governance gaps from measurement evidence, and opportunity from shadow-AI risk. Evals sits underneath every workstream as the measurement layer.
Most engagements start with the AI Audit.
The Audit maps approved tools, shadow AI, embedded features, internal agents, spend, and workflow readiness. The output is the operating read: current state, steady state, governance gaps, opportunity map, risks, and the next move.
Audit feeds the workstreams. You don’t run everything.
Most teams run one or two workstreams after the Audit. The operating read decides the order.
The sequencing question is simple: are we choosing the strategic bet, transforming a workflow, building the evidence layer, or helping people use AI well in the work?
Four 8-minute scorecards. One per decision surface.
Take the one closest to where you're stuck. Each scorecard turns the fuzzy AI question into a small set of decisions a board can act on.
The AI Adoption Scorecard is the upstream sequencing instrument used in the PE outbound sequence: Strategy × Fluency plus four diagnostic questions a board can act on.
Four spaces. One methodology.
Each tile links to the deep version on the relevant service or industry page.
Route by buyer shape.
| Buyer shape | Best entry point |
|---|---|
| PE Operating Partner | Industries · Private Equity → |
| CEO / Board (single firm) | Solutions · CEO → |
| CIO / CAIO | Solutions · CIO → |
| CFO | Solutions · CFO → |
| CISO | Solutions · CISO → |
| AI-native finance product team (build need) | Services · AI Engineering → |
| AI-native finance product company (evals need) | Services · Evals → |
| Industry-led search (PE / Banks / Fintech / REITs / Insurance) | Industries → |
Hands-on. Fixed-scope. Platform-anchored.
A named TrustEvals practitioner embeds with your team for the engagement window. We ship code where it matters: multi-agent orchestration, eval pipelines, and observability, then hand the system to your engineering team at the end. The operating view keeps improving across engagements.
Book the AI Audit.
Thirty minutes to size the discovery surface: employees, devices, SaaS admin access, developer tooling, internal agents, Shadow AI exposure, and the outcome read you need at the end.
What gets asked every week.
No. The AI Audit is the entry read. It gives the current state, the steady state, the governance gaps, and the opportunity map before we sequence any workstream.
No. Most teams start with the Audit, then run one or two workstreams. The point is sequencing, not buying a full menu.
Well. We are not competitive with consulting firms. We are the platform that makes their AI recommendations measurable. Bring your consulting partner; the Audit feeds their engagement as much as ours.
No. The platform deploys standalone. Most customers run it themselves after the Audit. Services are optional depth when the problem is big enough, or when the customer's team wants a practitioner alongside them for the engagement window.