Case studies

Real delivery work, anonymized

These cases show what we changed, which KPIs we selected at the start, and what improved after the engagement.

Anonymized client engagement / Operations

Scaling AI use without policy drift

AI use was growing faster than review standards, so teams needed shared rules before scaling.

31% faster review handoff78% approved usage

Case study illustration for an executive AI rollout decision path

Anonymized client engagement / Strategy

Turning rollout debate into a decision path

Engineering, legal, security, and procurement needed one safe first boundary.

4 groups aligned9-day decision path

Case study illustration for replacing vague AI ROI talk with an adoption model

Anonymized client engagement / Finance

Replacing vague ROI talk with an adoption model

Leadership wanted evidence before turning AI adoption into a productivity claim.

1 scorecard adopted3 baseline KPIs

Anonymized client engagement / Operations

Reducing platform and product handoff confusion

Several teams used AI, but ownership and escalation rules changed from team to team.

34% fewer review loops9 workflow decisions

Case study illustration for aligning post-acquisition teams on one AI operating model

Anonymized client engagement / Growth

Aligning post-acquisition teams on one AI model

Two engineering groups needed one shared language for AI tools, review, and adoption.

2 orgs aligned42% less tool drift

Case study illustration for enterprise AI governance scoping before rollout

Anonymized client engagement / Governance

Choosing enterprise scoping before rollout broke

The buyer needed security, legal, procurement, and several teams aligned before enablement.

5 functions mapped0 wrong-package weeks

How rollout becomes real

Each engagement connects advice to team behavior

Map current use

We find where teams already use AI and where approval is missing.

Approve the workflow

We define the tool path, guardrails, and human review rule before training scales.

Enable by role

Leaders, managers, reviewers, and engineers each get the decisions they need.

Review adoption

We check whether teams actually use the approved model.

Client stories

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We like publishing useful case studies. If you are open to one, mention it when we talk. We always anonymize the company, team, and sensitive details, and apply a 6% project discount when a publishable case study is agreed upfront.

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Scale AI in engineering with control.

We help define the workflows, guardrails, and proof you need.

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