Will it stick after you leave?

That's the entire design. We embed on real work, grow champions inside the team, and hand over artifacts they own. We taper out on purpose so the capability lives with your people, not in a retainer.

Isn't this just training?

No. Training is a room and a slide deck. This is us working alongside your team on their actual backlog, changing how they ship and operate day to day. The new way of working is the deliverable, not a certificate.

What if we just want you to build the thing?

Then you want Build, and we'll happily do that. Enablement is for when the goal is your team's capability, not a feature we hand over. Plenty of clients do both, in sequence.

How long does it take, and which tools?

Six to eight weeks is the shape we recommend, capped at three months. On tooling, we bring what we trust day to day, including Claude Code and an AI-native SDLC, but we adapt to your stack and guard-rails rather than dictating them.

Services / AI Enablement

AI-native enablement

Your team, building with AI by default.

We embed as an enabling team, in the Team Topologies sense, and transfer the capability into your people. For your engineers, that means an AI-native way of shipping. For your operators, it means automating the dull work so they can do the work that matters. Then we step out.

6 to 8 weeksCapability transferBuilt to step out

Talk to us How we build with AI

Engagement shape

6 to 8 wks

Collaboratewk 1-2
Facilitatewk 3-6
Step backwk 7-8

Built to taper out. We make ourselves redundant, not fill seats.

Why adoption stalls

A licence isn't adoption.

MIT found 95% of enterprise GenAI pilots deliver no measurable business impact. The tools mostly work. What fails is adoption: a licence gets bought, a demo gets applause, and nothing about how the team actually ships or operates changes. Capability you don't own doesn't stick. It has to be built into your people, in your context, on your real work.

The pattern: an enabling team

Borrowed from Team Topologies. We embed alongside one of your teams, raise their capability on real work, and deliberately disband. A permanent enabling team is an anti-pattern, so we treat being temporary as the point, not a caveat.

Who we work with

Two teams, two kinds of win.

For engineering

An SDLC that's AI-native by default.

We work alongside your engineers on their real backlog, not a toy project.
Adapt how they ship: specs, agents, reviews and tests woven into the existing flow.
Engineers keep the architecture and the judgment calls; agents take the toil.
They come out faster, without the slop that unguided AI creates.

For operations

Automate the dull, free up the meaningful.

We find the repetitive, manual work that quietly eats your team's week.
Stand up AI tooling and agentic workflows to handle the routine parts.
People stay on the judgment, the exceptions, and the work that needs a human.
Cognitive load drops. The team does more of what it's actually for.

The shape of the work

Heavy up front. Lighter every week.

Collaboration, then facilitation, then step back. Intensity tapers as your team becomes self-sufficient.

Weeks 1-2
01Collaborative discovery
- Embed with one team, on their real work
- Assess where their capability actually sits
- Map the tooling and guard-rails that fit your org
Weeks 3-6
02Embed & transfer
- Run live work through agentic tooling, together
- Shape a team-compatible AI-SDLC, skills and plugins
- Grow the internal champions who carry it on
Weeks 7-8
03Withdraw to autonomy
- The team operates on its own, with us on hand
- Hand over the artifacts they now own
- Final handover, then we step out

After that, optionally and lightly: a little retained advisory, the same engagement with your next team, or we expand into building alongside you. Six to eight weeks is the shape we recommend, capped at three months. A permanent enabling team is an anti-pattern.

What stays after we go

Capability you own.

Everything we bring is meant to outlast us. When we leave, the capability stays with your people, not in a contract you have to renew.

What we deliberately don't do

Dictate your toolingDeliver the work for youStay permanentlyOwn a governance gate

01
An AI-SDLC tailored to your team
Not a generic playbook. The way your engineers already ship, reshaped around agents, skills and plugins they keep using.
02
Automations that take the dull work off ops
Agentic workflows for the repetitive tasks, so your operators spend their time on judgment, not busywork.
03
Internal champions who carry it on
People on your team who can teach the next person. The capability spreads on its own once we've gone.
04
Lower cognitive load, owned outright
Success is capability owned and load reduced. The win is what your team can now do without us, not our hours.

The tooling we bring

What we help you navigate.

We don't dictate your stack. But this is the frontier tooling we work with every day, and help the teams we embed with navigate. Go deeper on any of it:

AI-Native SDLCHow we apply AI across the full delivery lifecycle, not just the code.

Claude Code ConsultancyWe build with Claude Code as core infrastructure, not just talk about it.

AI Agent FactoryWhat running agent fleets really takes, and why most teams shouldn't build their own.

AI Agent DevelopmentBuilding production agents end to end, tools, memory, orchestration.

Is it for you?

When an enabling team is the right call.

This is for you if

You've bought AI tools, but adoption across the team is patchy at best.
Your engineers want to work AI-native and don't have a path that fits how they ship.
Your ops team is buried in repetitive work that AI could quietly take off them.
You want the capability living in your team, not a permanent dependency on a vendor.

Skip it if

You want us to build the AI product for you. That's Build, not this.
You want a permanent embedded team. An enabling team is temporary by design.
Your team can't free up any time to learn. Enablement needs their hands on the work.

Questions

The cynic's questions.

Will it stick after you leave?: That's the entire design. We embed on real work, grow champions inside the team, and hand over artifacts they own. We taper out on purpose so the capability lives with your people, not in a retainer.
Isn't this just training?: No. Training is a room and a slide deck. This is us working alongside your team on their actual backlog, changing how they ship and operate day to day. The new way of working is the deliverable, not a certificate.
What if we just want you to build the thing?: Then you want Build, and we'll happily do that. Enablement is for when the goal is your team's capability, not a feature we hand over. Plenty of clients do both, in sequence.
How long does it take, and which tools?: Six to eight weeks is the shape we recommend, capped at three months. On tooling, we bring what we trust day to day, including Claude Code and an AI-native SDLC, but we adapt to your stack and guard-rails rather than dictating them.

AI Enablement in the wild