An in-house programme for engineering teams: build a reliable AI delivery workflow around your codebase — SPECIFY → BUILD → SHIP.
AI changes engineering work. We apply Reshape to the habits around it.
Vibe coding in an IDE is a useful starting point, not a delivery system. Without a harness, AI loses context, breaks contracts and produces code that cannot pass a production Quality Gate.
Eight sessions across SPECIFY → BUILD → SHIP. Each phase leaves the team with better rules, agents and context: a practical Oper8 workflow shaped around your engineering reality.
Programme
Eight four-hour sessions across four weeks, followed by 60–90 days of adoption support.
Audience
Backend, frontend, QA, DevOps, tech leads and architects, sponsored by a CTO or VP Engineering.
What remains
Agents, rules and context shaped around your stack and codebase. This is working infrastructure, not a slide deck.
The team works through SPECIFY → BUILD → SHIP on real tasks. At each phase, the harness gains agents, rules and context — until it becomes working infrastructure rather than a presentation.
The common thread · 00
Not a separate module, but the foundation for all eight sessions. Agents fit your frameworks, rules fit your code style and context comes from your codebase. By the end, the harness is part of the team's working environment and can keep evolving.
Agents
for your stack, frameworks and build
Rules
code style, review and testing
Context
packages for modules and domains
Phase · SPECIFY
Turn an ambiguous task into a precise artefact that an AI agent can follow. Build context packages, specification templates and review questions.
What remains
Phase · BUILD
Work through generation, refactoring and code review with agents. Configure rules, fixtures and fallbacks for your stack so more changes pass review first time.
What remains
Phase · SHIP
Connect agents to CI/CD, testing and incident review. Then use 60–90 days of monitoring to see what is adopted, where it fails and what to improve.
What remains
The phases follow a sequence and the harness grows with each one. After the programme, 60–90 days of monitoring help the team improve adoption and refine the rules.
These are patterns from real codebases and engineering teams, not polished demos on toy repositories.
Tech leads · Java / Spring
A team of tech leads applied SPECIFY → BUILD → SHIP to its real Java/Spring work. The result was a shared harness for frameworks, context and review patterns.
Engineering teams · delivery workflow
Several engineering teams rebuilt their delivery workflow around AI: clearer specifications, controlled generation, review and testing with agents in daily work.
Read more about our work in client cases. We name only clients cleared for public reference; other examples remain anonymised.
AI-DLC is delivered as an in-house programme built around your team's real engineering work. If you are still choosing the right starting point, we can map it with you.
Dates to be confirmed
We’ll find you the next one
Ask about datesYour codebase, your delivery workflow
Company programme with adoption support
Talk through the programmeTalk through your engineering workflow
We reply within one working day to arrange a 30-minute fit call. We will review your current delivery workflow, understand the stack and outline how the harness could fit your codebase.
Compare the workshop, AI Champions 2.0, the executive programme, AI Literacy, AI-DLC and vibe coding.
Compare training optionsHelp managers build working tools with AI, then support adoption with mentoring.
Explore Vibe CodingIdentify the processes and teams that should come first before you scale AI delivery.
Book a diagnosticWe’ll look at your problem, pick the right format, and show where AI actually strengthens your operations.
50+
companies have gone through AI transformation with us — from diagnostics to working solutions.
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