What does AI enablement include?

Use-case discoveryWorkshops that surface the workflows where AI will actually save time or money, ranked by value and effort.
Hands-on labsYour team builds with AI on their own tasks, with our engineers alongside.
AI-native engineeringHow to use coding agents in parallel on real codebases while keeping architectural control and quality.
Prompting & contextPractical techniques for getting reliable results, and knowing what models can’t do.

How do you teach engineers to work with coding agents?

By doing it on their code. We pair with your engineers to decompose real tickets into agent-sized tasks, run agents in parallel, review and integrate their output, and set up the tests, conventions and context files that make agents effective. The goal is a team that ships faster without giving up ownership of the architecture.

What about non-technical teams?

For operations, content, marketing, sales and legal teams, we focus on the work they already do: what to hand to an assistant, how to give it the right context, how to check its output, and when to ask for a purpose-built tool instead. Many of our best build projects start as ideas from these sessions.

What changes afterwards?

A ranked backlog of AI opportunities, owned by the team.
Shared practices and templates for prompting, context and review.
Engineers who can run agentic development safely on production code.
A clear line between what to do with off-the-shelf tools and what to build.

Questions

Is this tied to a specific AI vendor?

No. We teach with the tools your organization has approved and explain the trade-offs between providers, so your team isn’t locked into one vendor’s way of working.

Can enablement be combined with a build?

Yes, and it works best that way. Teams adopt a new system faster when they helped shape it, so we often run enablement alongside a Prototype Sprint or product build.

Updated 2026-10-23