OnBoardAI is a developer onboarding system for GitHub repositories. It is described as structured onboarding for codebases rather than a chatbot, with the stated goal of getting engineers productive in hours rather than days. The service is aimed at engineering teams, and the page specifically calls out 3–20 person teams that have outgrown informal onboarding.
Its workflow starts by scanning a repository and building a structured onboarding plan. The generated plan includes architecture, critical areas, risks, and key files, and team notes can be layered on top. OnBoardAI also suggests first tasks so new hires begin contributing, and it supports assignments, progress tracking, manager visibility, and contextual chat and Q&A. The plan is tied to the repository and is updated as the code changes, with optional sync to keep onboarding current. The page also mentions role-aware views and learning paths for frontend, backend, and full-stack roles.
Delivery is tied to GitHub: users connect a repo, pick the repositories their team works in, and then move through planning, assignment, and tracking. The product materials describe a manager view for onboarding progress and assignments, and say that keys for chat stay on the server. Pricing is offered as monthly plans: Essential at $29 per month for 2 repositories and 2 users, Pro at $79 per month for 5 repositories and 5 users, Team at $249 per month for 20 repositories and 20 users, and a Custom (BYOK) plan at $199 per month that uses Anthropic or Gemini API keys and custom limits. The page also says all paid plans include full onboarding features, and that free trials are available upon request.
OnBoardAI sits in PulseGate's Developer Tools category. It focuses on accelerating developer onboarding and productivity by automating codebase learning and training. It is built as a B2B product for engineering teams and managers. OnBoardAI is paid. It ships for the web.
OnBoardAI builds and maintains OnBoardAI, and it first shipped in 2024. Among its 5 catalogued features are codebase analysis, structured learning paths, and automated first tasks.
Summary written by a language model from the project’s public pages.
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