AI engineering audit
A repo-level review of where coding agents can safely help, where they create maintenance drag, and what needs to change first.
- Agent readiness map
- CI and test gaps
- Security and privacy boundaries
Gotchacode / AI engineering consulting
Ship faster with AI agents without letting code quality rot.
Gotchacode helps small software teams turn coding agents into a disciplined delivery system: better repo context, stronger tests, safer PR review, cleaner CI, and less guesswork.
The work happens in your codebase, alongside the standards your team already relies on. You leave with working improvements and a system your engineers can keep using.
1–2 weeksTypical sprint size
3 offersAudit, setup, sprint
0 fluffRepo work over theory
Services
Practical offers for teams that already have code to ship.
This is not a prompt workshop. The work happens inside your repo, CI, review flow, and the habits your team will keep after the engagement.
Agent workflow setup
A practical setup for teams that want agents inside real engineering work without lowering the bar for review, tests, or architecture.
- Repo context packs
- Prompt and skill workflows
- PR review loops
Modernization sprint
A focused sprint to clean up CI, dependencies, test coverage, docs, and developer workflows so the codebase is easier to ship with.
- CI repaired
- Risky dependencies handled
- Docs and guardrails added
Approach
AI makes output cheaper. Good engineering judgment becomes more valuable.
Most teams do not need another tool subscription. They need a way to decide what agents may change, how code gets verified, and where human review still matters.
“Speed is useful only when the code remains understandable, testable, and safe to change again.”
Tests first
Agent work is routed through fast feedback, regression checks, and clear acceptance criteria.
PR discipline
Review prompts, diff hygiene, and handoff notes make generated code easier to trust.
Private-code safe
Security and data boundaries are treated as engineering requirements, not footnotes.
System fit
The setup follows your repo shape instead of forcing a generic AI workflow onto it.
Process
A consulting engagement should end with working assets, not a slide deck.
Map the repo
Inspect the codebase, delivery process, tests, CI, docs, and review habits.
Find leverage
Identify the work AI should accelerate and where senior judgment still matters.
Install guardrails
Add workflows, prompts, checks, docs, and conventions that make the work repeatable.
Ship proof
Finish with cleaner CI, safer reviews, faster tasks, and a team-ready playbook.
Start small
The easiest opening is a one-repo audit.
Send a short note about your codebase, team size, stack, and where AI-assisted work currently feels risky or slow.