We ship production software with AI agents. We can set your team up to do the same.
For engineering teams and agencies adopting AI-assisted delivery — mobile-led or not. The workflows we set up are the ones we use to ship, held to a production standard.
- 01 Audit — 5 days
- 02 Sprint — 2–4 weeks
- 03 Embedded — ongoing
Your team has the AI tools. This is why the speed hasn’t arrived.
The licences aren’t the problem. The gap is between a tool that writes code and a workflow that ships it.
Output you can't trust
Agents produce plausible code fast. Without conventions and tests shaped for them, plausible fails review — and the team quietly stops using the tools.
No harness
The tools were dropped into repos built for humans. No CLAUDE.md conventions, no test factories, no CI rules for agent output — so results are inconsistent by default.
The review bottleneck
AI writes faster than people can review. Unless the workflow produces small, well-tested, reviewable PRs, cycle time doesn't move — it just piles up at review.
Agents don’t need better prompts. They need a better repo.
We structure the codebase, tests and CI so that agent output is mergeable by default. Six pieces, all of them boring on purpose — and all of them running in our own production work today.
Repo conventions
CLAUDE.md conventions, structured context and house rules that agents actually follow — the repo teaches the agent how this team builds.
Multi-agent orchestration
Claude Code agents planning, implementing and reviewing as separate roles — not one chat window doing everything badly.
Parallel execution
Git worktrees so multiple agents work the backlog simultaneously, each in an isolated checkout, without treading on each other.
Stories end-to-end
Agents picking up Jira stories and carrying them to a reviewable pull request — implementation, tests and description included.
MCP integration
Your tools wired in over MCP: Postman for API surfaces, plus custom and self-hosted MCP servers for internal systems.
Tests & CI gates
Test factories and CI rules sized for agent output — so what lands is mergeable and reviewable, not just plausible.
The AI Delivery Audit — 5 days
One senior engineer inside your repos and workflow for a week. Fixed scope, fixed fee, remote or on-site. It ends with evidence on your own codebase — not a slide deck.
- Timeline: 5 days
- Format: Fixed scope
- Outcome: Report + pilot + roadmap
Delivery baseline
How your team actually builds: repo structure, review flow, CI, test coverage, where cycle time goes — and why the AI tools you already pay for aren't landing.
Working pilot
We set up a harness on one real repo — conventions, guardrails, CI rules — and run one real backlog story through an agentic workflow to a reviewable PR. Evidence, not slides.
Security & data review
Where code and context flow under each tool option, SaaS vs self-hosted MCP, agent permissioning — and the answers your compliance team will ask for.
Report & roadmap
Written findings, the pilot harness left in place, a prioritised adoption roadmap — and a scoped proposal for the enablement sprint, if you want one.
Built for teams where security says no by default.
Enterprise AI adoption doesn’t usually fail on capability — it fails at security review. We’ve built software inside regulated enterprise environments, so we design for the compliance boundary from day one instead of retrofitting it.
- Senior experience operating inside regulated enterprise environments — security review is familiar territory, not an obstacle.
- Data boundaries mapped before any tool touches code: what leaves your network and what doesn't, under each option.
- Self-hosted MCP servers where SaaS integrations are a non-starter.
- Least-privilege permissioning for agents — scoped access, no blanket credentials.
- Every agent change lands as a pull request through the same review gates as human code. Auditable by default.
The proof is the shipped work.
Every workflow on this page is one we use to ship production apps for the UK’s biggest brands. That’s the standard the AI-assisted work is held to — App Store review, crash rates, real users. Not a demo repo.
Audit first. Everything after it is optional.
AI Delivery Audit
Baseline, working pilot on your codebase, security review, report and roadmap. The low-risk way in.
Enablement sprint
Implement the roadmap: tooling and guardrails wired into your repos, hands-on upskilling, a playbook your team keeps.
Embedded delivery
Senior engineers shipping in your team with these workflows — proving the method on your backlog, month by month.
Straight answers, before you enquire.
What does AI-enabled delivery actually mean here?
AI agents doing real backlog work — Claude Code executing Jira stories through to reviewable pull requests — inside a harness of repo conventions, tests and CI gates that makes the output mergeable. Not autocomplete, and not a chatbot.
Which tools do you work with?
Claude Code first: multi-agent orchestration, parallel execution with git worktrees, and MCP integration including Postman and custom or self-hosted MCP servers. We also work with Cursor and Copilot where teams already have them — the harness matters more than the badge.
Can this work in a regulated or security-conscious environment?
Yes. We have operated in regulated enterprise environments and design for the compliance boundary from day one: mapped data boundaries, self-hosted MCP options, least-privilege agent permissioning, and output that arrives as auditable pull requests through your existing review gates.
Do we need to be a mobile team?
No. The method is language-agnostic — repo conventions, tests, CI and review gates apply to any stack. Our proof happens to be mobile: the same workflows ship our production iOS and Android work.
How do we start?
With the AI Delivery Audit: five days, fixed scope, one senior engineer inside your repos and workflow. It ends with a written report, a working pilot harness on one of your repos, and a prioritised roadmap. Pricing on enquiry.
What's left behind if we stop after the audit?
Everything the audit produced: the report, the roadmap, and the pilot harness on your repo — conventions, guardrails and CI rules your team can keep using without us.
See it work on your codebase, not ours.
Five days, fixed scope, one senior engineer — and a real story from your backlog shipped through an agentic workflow.