Deliverable 01
An AI-aided-delivery review contract.
One-page document. What a human must verify on an agent-drafted change, what the model must show its work on, what's off-limits without a second pair of eyes.
S1 · Build · Phase 2 — booking now for later this year
AI-Aided Development
Senior engineers paired with AI coding agents, custom tooling, and evaluation harnesses — applied to your delivery or operationalised in your team.
AI-aided development is easy to make faster and hard to make better. The difference lives in the review contract, the measurement loop, and the internal library — not in the seat licences. We do this work with teams that already have senior engineers and want to preserve that seniority while raising the throughput.
This service ships in Phase 2. If you’re planning ahead and want a slot in the second half of the year, tell us the shape of the team and we’ll pencil something in.
When to bring us in
Signals we're the right fit.
- Your engineering leaders can see AI-in-the-loop is happening; they just want it to be honest about what it's doing.
- Individual engineers have found tools they like and there's no shared story about safety, review, or measurement yet.
- The organisation is buying seats but nobody's measured whether they're paying off.
- You've had one incident caused by an unreviewed agent-drafted change and would like it to be the last.
- You want AI-aided delivery on a production codebase without pretending it's a green-field toy.
How we approach it
The moves that repeat.
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Establish the review gate before the throughput.
An AI-drafted diff without a competent human on the review is a liability. We codify the review contract first — who reads what, what the model must justify, when a human writes tests instead of agreeing with them.
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Wire the eval loop.
Measure defect rates, revert rates, PR review time, and cost per merged line. Not to score engineers — to know whether the tooling is earning its keep, which pattern of use pays, and which one just looks busy.
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Build the shared tooling.
Prompts, tool configurations, model routing, cost caps, and audit trails go into a small internal library the whole team uses. One place to update; one place to look when something misbehaves.
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Ship a playbook.
What to use AI for. What to not. What to only use AI for when a senior is co-piloting. Written from your codebase, not from a vendor blog.
What you get
Deliverables, in plain English.
Deliverable 02
Cost + quality dashboards.
Per-engineer, per-repo, per-week. Rolled up for leadership; drilled down for team leads. Wired to real data, not to vibes.
Deliverable 03
An internal AI-tools library + prompts pack.
Shared scaffolding for the patterns your team keeps re-doing. Ships in the org's language and framework, tested, versioned.
Engagement models
Shapes the work comes in.
PILOT
Two-team pilot.
Six weeks. Two engineering teams. We instrument, we pair, we measure, we hand back a decision-ready report.
PROGRAMME
Rolling programme.
Quarter-by-quarter engagement across a wider engineering group. Practice guild, monthly review, playbook updates in your repo.
Related services
Often composed with this one.
S2 · Build
Agentic Systems Engineering
Multi-step LLM workflows with clean topology, tool surfaces, memory, evaluation, and safety — built to land in production, not stall in a demo.
S3 · Build
LLM Integration
Chat, RAG, and the wider integration stack — measured, regression-tested, and cost-aware from the first prompt.
S7 · Sustain
AI Enablement & Training
From AI-curious to AI-capable — briefings, bootcamps, and cohort programmes with measurable uplift.
Ready to talk about a ai-aided development engagement?
A 30-minute discovery call. We'll get to the shape of the work and whether we're the right fit.