AI-assisted engineering
Move your team from ad hoc AI coding to a governed, production-grade practice - so production-readiness is structural, not something you have to remember to ask for.
What's included
- Tool evaluation for your stack.Claude Code, Cursor, GitHub Copilot, and Amazon Kiro (formerly AWS Q Developer) assessed against how your team actually works - we recommend, we don't mandate a single vendor.
- A governed workflow in your own repo. Spec-driven planning, TDD enforcement, and scoped-diff discipline set up as rules and hooks around the tools your team already runs, not a document nobody reads.
- Real execution verification.Structural checks that the program was actually run and the behavior confirmed - not just a green unit test reported as "it works".
- Training and pairing.Your team learns the plan → approve → implement → verify loop against your own backlog, not a toy example.
- Org rollout guidance. A path for taking the practice from one team to several, without diluting the discipline along the way.
Why this is not vibe coding
AI coding assistants make fast, plausible-looking output easy - that's "vibe coding", and left to informal habits it produces the same failure modes every time: diffs that touch files nobody scoped, tests skipped or gamed until they pass against buggy code, "tests pass" reported as "it works" without the program ever actually running, decisions from yesterday's session gone today, and an inconvenient test quietly deleted instead of the bug underneath it fixed.
Fixing this with better prompts alone doesn't hold - a prompt is a one-time suggestion, not a standing rule, and it doesn't survive a context reset or a Friday deadline. What holds is structure: rules that define what good practice looks like in your repo, hooks that enforce them on every edit whether or not anyone remembers to ask, and tools that give the model real context instead of a guess.
The organizational side of this is proven, not theoretical: evaluating GitHub Copilot and Amazon Kiro (formerly AWS Q Developer) for a platform team's day-to-day work, building an MCP-backed CLI agents toolkit wired into GitLab, Confluence, Jira, and SonarQube, and sharing what worked - and what didn't - across a large DevOps community.
Engagement models
Ways to work together
Whether you need a second opinion or a hands-on partner, there is a model that fits how your team works.
Advisory
For teams that need senior direction on AI tooling without a full engagement.
€220/ hour
- Tool evaluation for your stack
- Workflow & guardrail design review
- Async access for questions
- Pairing sessions with your team
Workflow rollout
Most popular
Scoped setup of a governed, spec-driven workflow in your repo - shipped and adopted end to end.
Fixed bid
- Rules & hooks enforcing TDD and scoped diffs
- Real execution verification wired in
- Team training on the plan-approve-implement-verify loop
- Handover docs & runbooks
Embedded
An ongoing practice partner as AI-assisted engineering rolls out across your teams.
Custom
- Dedicated weekly capacity
- Org rollout guidance across teams
- Continuous workflow refinement
- Quarterly practice review
Frequently asked questions
Looking for the delivery, cost, and reliability engagement instead?
Cloud-Native Solution Engineering
DevOps, FinOps, and SRE as one engagement - delivery, cost, and reliability measured with DORA.
Ready to ship faster on AWS?
Tell us what you are building. We will map the fastest safe path to production and the platform to keep it there.