Joggr automatically builds and maintains a unified knowledge base by connecting a company's codebase, conversations, and tools. This structured context ensures developers and AI agents always have accurate, up-to-date information where they work. The platform delivers human-friendly documentation and AI-ready context, reducing token waste and improving agent accuracy.
Funding
$120K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Software teams struggle to keep documentation synchronized with rapidly changing code, conversations, and tickets, leading to stale or missing context that degrades both human productivity and AI coding assistants. This “context rot” forces developers and AI agents to waste time searching for relevant information or, worse, generate incorrect outputs.
Solution
Joggr continuously ingests data from source‑code repositories, chat platforms, issue trackers, and design tools, then transforms it into a unified knowledge base. It automatically generates and updates markdown documentation while also exposing structured, pre‑indexed context for AI agents via a Machine‑Code‑Protocol (MCP) interface. Real‑time sync ensures that any code change, pull‑request, or discussion instantly refreshes both human‑readable docs and AI‑ready context, eliminating token bloat and outdated references. Native integrations with popular AI coding assistants (e.g., Claude Code, Cursor) let agents retrieve the exact information they need without redundant file scans. The platform also provides alerts for potential documentation drift and offers extensible APIs for custom workflows. By delivering accurate, up‑to‑date context on demand, Joggr accelerates feature delivery, reduces errors, and lowers the cognitive load on developers.
Target Audience
Joggr is designed for engineering teams that develop complex software systems and rely on AI coding assistants, including developers, DevOps engineers, and technical leads in mid‑size to large enterprises.
Features
- Automatic ingestion from GitHub, GitLab, Bitbucket, Slack, Microsoft Teams, Jira, Linear, Confluence, Notion, Miro, and other dev tools.
- Real‑time knowledge‑base updates triggered by commits, pull‑requests, and ticket status changes.
- Dual output: clean markdown documentation for developers and pre‑indexed MCP context for AI agents.
- Native connectors to AI coding assistants (Claude Code, Cursor, Windsurf, Copilot, etc.) with token‑efficient context delivery.
- Context‑drift detection and PR‑based alerts that flag stale or missing documentation.
- Extensible REST/GraphQL API and SDKs for building custom bots, workflows, or integration pipelines.
- Role‑based access control and end‑to‑end encryption to meet enterprise security standards.
- Zero‑maintenance mode that auto‑generates missing docs and fixes outdated sections without manual intervention.