2 week AI Audit
AI AuditVisibility, risk, outcomes.AI TransformationCapture AI value across workflows.AI GovernanceContinuous evidence, not paperware.AI FluencyWorkforce that compounds.AI engineeringBuild AI inside your product.EvalsContinuous evaluation in production.All servicesThe full service map.
Not sure where to start?Take the 5-min maturity assessment →Maturity modelOpen assessment
By industry
Private equityPortfolio-wide value capture.Regulated financeThe finance umbrella.Banks & capital marketsRegulator-defensible under SR 11-7.FintechShip trusted AI fast.Asset & wealthFor advisors and ops.REITs / real estateAI-driven NOI uplift.InsuranceUnderwriting + claims, instrumented.
Field notes, scorecards, compliance
12 LeversWhere value capture shows up.Baseline problemWhy measurement collapses.Adoption → assuranceThe sequence every AI team walks.Adoption scorecard9 proxies. 5 minutes.All scorecardsFour 8-minute scorecards.Compliance frameworksISO · NIST · EU AI Act · AIUC-1.Resources hubAll chapters and field notes.
Featured chapter
The 12 Levers of Enterprise AI Adoption.
Twelve places where AI value capture actually shows up, each mapped to who owns it and what good looks like.
Read more →
Case Study

AI-native finance SaaS case study.

25% accuracy lift, 20% fewer false positives, and 90+ regression scenarios across high-risk document workflows.

The company already had AI in production. The gap was evidence: what the agents were doing, where behavior drifted, and what a regulated buyer could trust.

Back to case studies
WorkstreamAI Engineering + Evals
ScenarioAI-native finance SaaS shipping LLM-backed product into bank and asset-management customers
Next moveAI Engineering and Evals, with governance evidence as the buyer-facing output.
Evidence snapshot
25%
Accuracy lift

Measured on the hardest production agent after the prompt and eval loop was applied.

20%
False positives reduced

Reduction observed after the eval pipeline went live and release checks became measurable.

90+
Regression scenarios

High-risk document behaviors covered so releases could be tested before buyers saw them.

Starting point.

Eval pipeline gaps blocking enterprise procurement at regulated finance buyers. Production agents handling thousands of customer interactions a week with no measurement layer.

What we found.

  • Enterprise procurement needed stronger evidence than product demos could provide.
  • The hardest agent lacked enough regression coverage to catch failures before release.
  • Engineering and go-to-market teams were using different language for the same reliability gaps.

What shipped.

  • Behavioral eval pipeline across priority document categories.
  • 90+ regression scenarios tied to high-risk agent behavior.
  • Prompt optimizer loop for the hardest agent and buyer-readable evidence language.

Proof.

  • 25% accuracy lift on the hardest agent.
  • 20% reduction in false positives after the eval pipeline went live.
  • 90+ high-risk document scenarios covered in regression testing.