The AI practice

Two places AI pays off. We work in both.

In the business, where the right workflow gives a team its week back. And in the engineering team, where the right setup makes good developers faster without lowering the bar. Most firms pick one. The interesting problems sit in between.

Track 01 · AI in the business

Find the workflows. Pilot two. Keep what moves the number.

We start with how work actually moves through the company, not with a list of AI use cases from a vendor.

Weeks 1–2

Map

Interviews and shadowing with the people doing the work. We write down where time goes, where errors come from, and where judgment is actually required.

Then

Rank

Each candidate workflow gets scored on value, risk, and effort. You see the whole list, including the ones we recommend against.

Then

Pilot

One or two workflows, built with the team that owns them, with a baseline measured first. At the end you have a number and a decision.

Typical workflows

  • Intake and triage of requests, tickets, or applications
  • Drafting routine documents, proposals, and responses
  • Summarizing calls, meetings, and long threads into action
  • Reconciling data between systems that do not talk to each other
  • Research and first-pass analysis that a person then checks

Guardrails we build in

  • A person approves anything that leaves the building
  • Clear rules for what data a model may and may not see
  • Logs you can audit when something looks wrong
  • A way to turn it off that does not break the process
  • Written ownership so it survives staff changes

Track 02 · AI in the engineering team

Coding agents are a team practice, not a personal tool.

Left alone, every engineer adopts AI differently and the codebase pays for it. We make the rollout deliberate.

Choose the tooling on purpose. Assistants, agents, and review bots overlap and the pricing is confusing. We help you pick a set that fits your stack, your security posture, and your budget, and we tell you what to skip.

Set the conventions. Context files, prompt patterns, what an agent may change without a human, how AI-generated code is labeled and reviewed. Written down, agreed, and enforced in the repository rather than in people's heads.

Set up the repository for agents. Fast tests, clear structure, documented commands, and the guardrails that make an agent's first pull request one you would actually merge.

Measure it. Cycle time, review load, defect rate, and the things your team actually complains about, captured before and after so the conversation stays honest.

Teach it. Pairing sessions with engineers and leads, in your codebase, on real tickets. The goal is a team that no longer needs us.

Questions we get asked

Straight answers.

Which model?

It depends on the workflow, your data, and your vendor posture, and it changes every few months. We design so the model is swappable and pick the best fit at the time.

Is our data safe?

We start from what your data may and may not touch, then choose tooling and contracts that fit. If the honest answer is that a workflow should not use a hosted model, we say so.

Will this replace people?

In our experience it removes the parts of a job people like least and makes the judgment parts more visible. We design with the people doing the work, and we are honest with you about what changes.

What if it does not work?

Then you have a measured result, a written reason, and a clean place to stop. We would rather end a pilot early than scale something that does not move the number.

Can you just train the team?

Yes. Training on its own is a real engagement, as long as it happens in your codebase or on your workflows rather than on slides.

Start with an audit, or bring us a workflow.

Either way the first conversation is free, short, and ends with a clear recommendation.

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