Case Study · Robotics SaaS · Series B · 14-person engineering team
Helix Robotics cut senior code-review time by 68% — and stopped shipping regressions.
How a 14-person engineering team running ~20 pull requests a week deployed Ship and turned every PR into a knowledge-graph-aware code review with verdicts back in GitHub.
Challenge
The problem behind the problem.
Helix Robotics ships firmware updates, a fleet-control dashboard, and a customer portal out of one monorepo. About twenty pull requests a week. Two senior engineers were burning 8–12 hours each on review, and regressions still slipped through — usually field renames or removed exports that broke a downstream consumer nobody flagged.
Their QA pipeline did what every QA pipeline does: ran the tests. It didn't know which lines of code mattered to which other lines of code. The reviewers — who did know — were the bottleneck.
They'd tried bolt-on AI review tools that left comments on every diff. The signal-to-noise was worse than ignoring them. They needed something that understood their code, not something that paraphrased the diff.
Solution
What we actually shipped.
We deployed Ship across Helix's monorepo. Ship parses the entire repository into a typed knowledge graph — pages, components, API routes, models, hooks, services — with edges for IMPORTS, RENDERS, CALLS, READS_FROM, WRITES_TO, TESTS. The graph rebuilds on every push, so every PR is reviewed against the actual current shape of the codebase, not last quarter's.
Six specialized reviewers run in parallel on every PR, all powered by Claude Sonnet: breaking-change detection (cross-references the graph for affected dependents), regression-risk flagging (fan-in scoring — files with 10+ dependents flag HIGH), test-coverage auditing (catches significant changes shipping without test edges), pattern-violation checking, data-consistency verification (field renames propagating across read/write paths), and incomplete-change detection (APIs without frontend backing, or vice versa).
Verdicts post back to GitHub as check runs with inline comments. Critical findings block the merge. Helix routed flagged findings into Linear tickets via Ship's Jira/Linear integration so the right author picks them up without anyone manually triaging the queue.
In their words
"We weren't slow because our engineers were slow. We were slow because the review step didn't scale. Ship moved the bottleneck somewhere it stopped mattering."
— VP Engineering, Helix Robotics
Result
The number on the retainer report.
In the first 60 days, Helix's senior engineers reclaimed 68% of the time they used to spend on PR review. Median PR turnaround dropped from 18 hours to 3. Ship blocked 7 merges that contained un-flagged breaking changes — including one that would have silently broken the fleet-control API for three customer integrations.
Helix now runs Ship on every repo. The senior engineers spend their time on architectural review and the long-tail flagged findings; the routine 'does this break anything?' question is answered before they open the PR. Ship is on a Pro tier with a custom enterprise SSO add-on we shipped in month three.
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