← Selected Work
Global 1000 Strategy TeamConsultingEmbed2024 — present

A senior AI pod, embedded.

A dedicated pod of senior AI engineers and a product lead working inside a Global 1000 strategy team — shipping in week two, not month two.

Embedded AI pod
Duration
12-month embed
Team
Pod of 4
Stack
Matched client (Azure)
First ship
Week 2
Delivered
3 internal tools
Handover
Patterns retained in-house
The challenge

The internal team was capable but lacked senior AI depth and the velocity to ship. They wanted expertise inside the team — not a separate workstream they'd have to manage at arm's length.

Our approach

We embedded a pod inside their sprints, systems, and channels. Ownership stayed with the client; accountability for outcomes was shared. We transferred patterns and tooling as we went, so the capability stayed when we rotated off.

What we built

Internal AI tools shipped alongside their own engineers — plus a reusable set of patterns, an evaluation harness, and guardrails the team kept after the engagement.

Tech stack
Application
TypeScriptReact.NET
AI / data
PythonDatabricksAzure OpenAIAzure AI Search
Practice
Eval harnessPrompt & pattern libraryCI for models
Infra
AzureBicepGitHub Actions
What we delivered
  • 3 internal AI tools shipped with the client team
  • A reusable pattern & prompt library
  • An evaluation harness & CI for model changes
  • Onboarding docs & a capability-transfer plan
  • A guardrail framework retained in-house
Timeline
Week 1
Onboarding into systems & sprints
Week 2
First production ship
6 months
Three tools live + pattern library
12 months
Capability transfer & rotate-off
Data at work
Wk 2
To first production ship
3
Internal tools shipped
100%
Patterns retained in-house
Outcomes
Wk 2
First production ship
3
Tools shipped with the team
100%
Capability retained
Next case →The AI backbone of a top consulting firm's practice.

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