I’m a co founder of Everdone (https://everdone.ai ).
We’re building a Work-as-a-Service platform — a place where teams trigger real work (not chats) and get production-ready outputs via AI. Everything is usage-based, like compute.
We’re starting with developer services because that’s where we’ve spent most of our lives as builders.
1. CodeDoc (live today) AI documentation for GitHub repos — generated file-by-file, not guessed top-down.
How it works: - Connect GitHub → choose repos → select files - Instant documentation is generated - Docs can be shared with unlimited team members - You can set up automation at the repo/branch level -- runs daily -- only newly changed files are re-documented -- users can specify which files/ folders/ extensions to ignore - We don’t train on customer code
Outputs explain what the file does, functions, DB operations, API I/O, dependencies, libraries used, etc. The tone is intentionally simple so junior developers can understand the project quickly.
We’ve documented a few OSS repos publicly (Next.js, FastAPI, Pandas, etc.) so you can see it in action.
Pricing: - 50 files free - $0.25 per file for AI documentation - $0.025 per file/month for hosting and access - Unlimited team members; no subscriptions or seat-based pricing
2. Coming next (developer side) - CodeReview (AI-assisted code reviews) - DBDoc (database documentation) - ChangeLog (auto changelogs from Git diffs)
3. And beyond engineering Long term, Everdone will support other departments: content, analytics, reporting, QA, growth ops, sales ops, and more. All usage-based — all producing real artifacts that organizations can audit and measure.
We think teams need a system of record for AI work. Not chat logs — actual outputs.
Would love feedback — technical, critical, or skeptical. Happy to answer questions.