Fixed-scope diagnostic
AI Engineering Audit
A repo-level review of where coding agents can safely help, where they will create maintenance drag, and what needs to change first.
- Agent readiness map
- CI and test gaps
- Security and privacy boundaries
AI engineering systems for small software teams
Ship faster with AI agents without letting code quality rot.
Gotchacode helps engineering teams turn coding agents into a disciplined delivery system: better repo context, stronger tests, safer PR review, cleaner CI, and less guesswork.
1-2 wks
typical sprint size
3 offers
audit, setup, sprint
0 fluff
repo work over theory
gotchacode-agent-system
Audit Result
Repo is agent-ready after 7 guardrails
Verification
Unit testspassing
CI workflowclean
Review notesready
Security scopebounded
Agent Playbook
01 read repo context
02 add failing test first
03 patch smallest surface
04 run checks and summarize risk
-28%
Lead time
-34%
Review churn
0
Broken CI
Services
Practical offers for teams that already have code to ship.
This is not a prompt workshop. The work happens inside your repo, your CI, your review flow, and the habits your team will keep after the engagement.
High-leverage implementation
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
1-2 week delivery
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
Positioning
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.
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.
AI-assisted development, kept honest by tests and review
Security-aware workflows for private codebases
Practical GitHub, CI, dependency, and repo hygiene
Clear handoff docs your team can keep using
Process
A consulting engagement should end with working assets, not a slide deck.
01
Map the repo
I inspect the actual codebase, delivery process, tests, CI, docs, and review habits before recommending tools.
02
Find leverage
We identify the work AI should accelerate and the work that still needs senior engineering judgment.
03
Install guardrails
I add workflows, prompts, checks, docs, and conventions that make agent-assisted work repeatable.
04
Ship proof
The engagement ends with visible improvements: cleaner CI, safer reviews, faster tasks, and a team-ready playbook.
Start Small
The easiest opening is a repo audit.
Send a short note about your codebase, team size, stack, and where AI-assisted work currently feels risky or slow.
Good first project
A one-repo audit with a written action plan and priority fixes.
Commercial shape
Fixed-scope projects first, then monthly retainers for teams that want ongoing help.
Best fit
Small teams with real product code, limited senior bandwidth, and pressure to ship safely.