AI Adoption & Enablement
Your team becomes the team that builds with AI.
We run a guided sprint on one of your real, high-stakes workflows. Your people learn the patterns by shipping a working proof of concept, and they walk away able to build the next one without us.
The gap between AI wins and operational AI
Your team is already using AI. Engineers are in Claude and Cursor, ops is drafting in chat, and the best results live inside one or two people's long, private conversations. None of it is reusable. None of it is auditable. The cat is out of the bag, and the question is no longer whether AI can help. It is whether your company can operationalize it.
That gap is where we work. We take the wins that are trapped in someone's personal chat history and turn them into a pattern your team owns, with the people who own the workflow doing the building.
The Applied AI Sprint
The Applied AI Sprint is a guided build on your own real problem. About half the time is hands-on building. The other half is practical enablement: managing context, catching hallucinations, and moving from one-off prompting to durable workflows with sequences, gates, and checks.
We deliver it two ways:
- Developer track, built on Claude Code, for engineering teams.
- Operator track, built on Claude Cowork, for the ops, finance, supply chain, and merchandising people who own your most valuable workflows and do not write code.
Same method. Same learn-by-building approach. Same outcome: a working proof of concept and a team that can repeat it.
Six steps from problem to capability
1. Pick the right problem.
We start with a high-stakes internal workflow owned by a domain expert. Real dollars, real pain, and a person who already knows the logic. That is what keeps the work production-grade instead of a demo.
2. Pre-scope before day one.
We run a discovery session to extract the existing logic and lock scope, so the sprint goes to building and teaching instead of archaeology. The better we understand the before, the more impressive the after.
3. Build and teach, side by side.
Four guided virtual sessions, roughly three hours each, with two senior facilitators and live breakout coaching. Your team builds the real thing while learning the patterns underneath it.
4. Encode the logic to last.
Durable rules go into reusable skills and deterministic scripts, not disposable chat history. The math lives in code. The context lives in a shared project. Anyone on the team can run it and extend it.
5. Point it at production.
We end with an operationalization path: a way to trigger the workflow, validation checks so you can trust the output, and a leadership-ready show-and-tell.
6. Leave you with capability.
You keep the proof of concept, a short playbook of the patterns, and a team that can build the next one on its own.
Deliverables
- A pre-scoping session that turns your existing workflow into a working architecture before the sprint begins.
- Four guided virtual sessions with two senior facilitators and hands-on breakout coaching.
- Between-session office hours for unblocking and design questions.
- Baseline and mid-point team surveys, so the learning is measured rather than assumed.
- A working proof of concept built on your own real problem.
- A short playbook of the patterns your team applied.
- A board-ready story you can take to leadership.
Distributed teams welcome. The format is fully virtual and runs across about two weeks.
Developer or operator
Developer track - Claude Code
For engineering teams ready to build agentic workflows. Skills, subagents, spec-driven development, context engineering, validation loops, and a path to production on the Agent SDK and AWS. Your engineers leave with a repeatable way to turn a hard problem into a deployable tool.
Operator track - Claude Cowork
For the domain experts who own the workflow and do not write code. The same method, with Cowork as the surface. We store the relevant history in a shared project, build a workflow your team queries and extends, keep a human in the loop on every decision, and capture decisions as live, shareable artifacts. Your operators leave running a process they used to do by hand.
Who it's for
- E-commerce and logistics teams reconciling invoices or validating carrier rates.
- Supply chain teams wrestling with vendor pricing complexity that off-the-shelf software cannot handle.
- Any data-warehouse-backed ops team with a smart-person-living-in-chat wedge problem.
- Regulated or high-spend environments where auditability matters.
What makes this different
- Real money keeps it honest. We build against a problem with real dollars on the line, so the work stays production-grade instead of a toy.
- Capability, not just delivery. Your team does not watch us build. They build with us, and they can do it again when we leave.
- Scope is locked before we start. Pre-scoping means the sprint builds instead of discovering, which is the step most training skips.
- We teach the patterns, not the use case. Context engineering, validation loops, and harness thinking transfer to every workflow you tackle next.
- There is a phase two. Every sprint ends with a clear path from proof of concept toward production, and usually a clear next problem to take on.
Proof from the field
A roughly $100M e-commerce business had a co-founder who had already used Claude to catch material errors in a major vendor's invoices. The wins were real, but they lived in long personal chat sessions that nobody else could run.
Over four virtual sessions, their team turned that tribal knowledge into reusable skills, deterministic scripts, and a shared project anyone could use. They left with a working reconciliation proof of concept, a measured jump in confidence around managing AI behavior under load, and momentum to run more structured AI work themselves. Their head of product and engineering called it the company's first major AI hackathon and asked us to scope the next one.