AI Coding Pipeline
A Series B dev tools startup had a roadmap built for a team twice its size. Engineers were already using AI assistants, but in an ad-hoc way that produced inconsistent quality and piled pressure onto senior reviewers. We helped them turn scattered AI usage into a disciplined pipeline, so the whole team could ship faster without lowering the bar.
The challenge
The problem wasn't whether to use AI, it was how to use it without creating a quality mess that senior engineers had to clean up.
- Eight engineers, twelve engineers' worth of roadmap. Demand outpaced capacity every sprint.
- Inconsistent AI output. Everyone prompted differently, so quality swung wildly between PRs.
- Review was the bottleneck. Senior engineers spent their days reviewing AI-generated code instead of building.
- No safety net. Without evals, a confident-but-wrong AI change could slip through and break production.
Our approach
We designed an agentic pipeline tuned to their codebase and standards, with automated quality gates that block bad merges before a human ever sees them. The pipeline has five stages, and every change passes through all of them.
stages:
- plan # break the task into a reviewable spec
- code # agent implements against the spec
- review # AI self-review + senior human review
- eval # custom evals + full test suite gate
- ship # merge only when every gate is green- Custom agents, not generic ones. Tuned to their conventions, libraries, and architecture so output looked like the team wrote it.
- Eval gates that block bad merges. Automated checks catch regressions and hallucinated APIs before review.
- Humans on the meaningful decisions. Seniors review architecture and intent, not boilerplate.
- Owned by the team. We handed over docs and training so the pipeline keeps paying off after we left.
How we worked
We traced how work actually flowed from ticket to production and found the real bottlenecks.
Custom evals and a hardened test suite became the non-negotiable quality bar.
We iterated on prompts and context until agent output matched the team's standards.
Training and runbooks so the team owns and evolves the pipeline themselves.
Results
The team shipped at 4× their previous velocity while adding zero new flaky tests, and senior engineers got their time back for the work only they can do. Quality didn't just hold, it became more consistent, because every change now clears the same gates.
"It feels like we hired a senior team overnight, except the quality bar went up instead of down."CTO, dev tools startup
Stack
Want this pipeline for your team?
We'll audit your SDLC and design an AI coding pipeline with evals and guardrails that fit how you actually work.