Sparkles

Teams often need small code changes, like updating a price on a pricing page, but only engineers can safely touch the repository, creating a bottleneck for simple requests. Existing AI coding tools do not fit this gap well: Cursor is built for a single engineer working in an IDE, and Lovable builds new apps from scratch rather than working inside an existing codebase. Non-technical teammates have no safe way to propose a change themselves, and engineers end up handling trivial edits that take time away from harder work. There is also no shared, visible place for a team to see what changes are being attempted across their codebase.

Sparkles connects to a team's existing GitHub repositories and lets anyone in the organization describe a change in plain language, either from a web app or by mentioning the Sparkles bot in Slack. An agent (Claude Code, Codex, or OpenCode) works in an isolated, disposable cloud sandbox, streaming every command and file edit live so someone can jump in with follow-ups mid-run. The agent hands back the work as a GitHub pull request, where existing branch protections, CI checks, and human review still apply before anything merges, so production is not touched directly. Tools like Slack, Linear, Notion, Sentry, and secrets managers such as Doppler or Infisical connect once and are then available to every agent run. Usage is billed through a shared organization credit pool, where one credit equals $0.10 of model inference across any supported model.