
Detail finds latent bugs in codebases via deep AI-powered scans
SOC 2 Type II Certified. Zero Data Retention. All code and usage data can be purged based on your requirements. Our model providers retain nothing.
82.9% of Detail findings were marked as correct by humans or agents. Detail bugs average 88th percentile importance vs code review bots.
Source: detail.dev
Detail currently supports GitHub repositories only (GitLab and self-hosted Git are not yet supported). The product works best on repos with 250+ files, excluding smaller codebases. Scans take hours rather than minutes, which trades compute for quality but limits rapid feedback. The $30/committer/month Team pricing was criticized on Hacker News as steep for enterprises, with one commenter suggesting a bug-bounty-style pay-per-bug model as an alternative. Self-hosting is only available on the Custom (Enterprise) tier. No C#/.NET support yet.
The long scan times mean it cannot provide the near-instant feedback that code review bots offer. Detail explicitly acknowledges it works best on app backends and has been tested most thoroughly on that domain.
The AI code review market was valued at $1.67B in 2024 and is projected to reach $8.07B by 2033 (19.8% CAGR per Growth Market Reports). The broader generative code review segment reached $2.12B in 2025 and is forecast to hit $8.73B by 2030 (32.7% CAGR per Research and Markets). The static code analysis market, which overlaps significantly, is valued at $4.3B in 2025 with projected growth to $12.7B by 2033 (22.1% CAGR).
Key growth drivers include developer productivity demands, security and compliance pressures (GDPR shift-left), DevSecOps adoption, and LLM advancements enabling context-aware bug detection. North America is the largest market. Detail operates in the intersection of AI code review and proactive codebase scanning, differentiating from reactive PR-review bots by performing full-repo deep scans.
Detail (qqbot, Inc d/b/a Detail) is a pre-seed AI-powered bug scanning company founded in 2023 by Dan Robinson, former CTO of Heap (9 years), with Sachin Iyer as founding engineer. The company raised $1M in pre-seed funding from angel investors including Guillermo Rauch (CEO, Vercel), Olivier Pomel (CEO, Datadog), Christina Cacioppo (CEO, Vanta), Arash Ferdowsi (co-founder, Dropbox), Alex Graveley (creator of GitHub Copilot), Barry McCardel (CEO, Hex), Dev Ittycheria (former CEO, MongoDB), Guy Podjarny (founder, Snyk/Tessl), and Zach Lloyd (CEO, Warp). Detail differentiates from code review bots by performing full-repo deep scans that take hours rather than seconds, exercising code in thousands of ways via Runloop sandboxed environments.
Three-tier pricing model targeting engineering teams. Project tier: pay per scan, up to 5 monthly contributors, bugs delivered to Linear, Jira, or GitHub Issues, email support. Team tier at $30/committer/month: monthly full repo scan, weekly scans over recent changes, additional scans a la carte, private Slack channel support, up to 8 repos. Custom/Enterprise tier: custom scan cadence, custom targeting, SAML SSO and SCIM, SOC 2 Report available, self-hosting option.
All plans include a free first scan with no credit card required, serving as a product-led growth entry point. The pricing positions Detail above basic code review bots but below dedicated security platforms, emphasizing the value of deep compute-intensive scans over shallow per-PR reviews.
We exercise your code to find thousands of bugs. We pick the top 1% you're most likely to care about and send them to you.
We built an agent that can write thousands of new tests for a typical codebase, all merge-quality.
Source: detail.dev
Source: news.ycombinator.com
Source: growthmarketreports.com
Benchmarking shows a random Detail bug ranks more important than a random code review bot finding 91% of the time. The product is GitHub-only, supports 11 languages, and integrates with Linear, Jira, and GitHub Issues. Pricing is three-tier: pay-per-scan (Project), $30/committer/month (Team), and custom enterprise. SOC 2 Type II certified with zero data retention. Key risks include: GitHub-only limitation, 250+ file repo requirement, long scan times limiting iterative feedback, pricing skepticism, and intense competition from well-funded competitors.
Source: detail.dev
Source: detail.dev