Almanac provides a context layer for engineering teams by automatically building and maintaining a searchable, AI-powered knowledge base from a company's codebase. The platform supplies grounded answers with citations from GitHub, Slack, and Jira, enabling developers and coding agents to work from a trusted, auto-updating single source of truth. This eliminates the need for repeated explanations and reduces the knowledge transfer tax for fast-moving teams.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering teams lose substantial time and incur errors when critical context about code—such as migration patterns, incident postmortems, and architectural decisions—is scattered across repositories, chat threads, and issue trackers. Developers and coding agents often make incorrect assumptions or require repeated explanations, slowing delivery and increasing risk.
Solution
Almanac is a context layer for engineering teams that automatically reconstructs knowledge scattered across a codebase and connected tools to answer questions about the code with grounded, cited responses. The platform continuously ingests and indexes information from code, pull requests, chat messages, and incident reports into a unified wiki-like search interface. Developers can ask questions, such as whether to add an index, and receive an answer with inline citations linking to the exact PR, Slack message, or Jira issue that supports each claim. This ensures both human developers and coding agents operate from an auto-updating, shared source of truth without needing to manually curate documentation.
Target Audience
Almanac is designed for engineering teams at fast-moving companies, especially those using AI coding agents, that want to maintain a shared mental model of their codebase without incurring documentation overhead.
Features
- Auto-updating wiki that reconstructs context from connected sources without manual authoring
- Grounded answer engine that generates responses with citations to verifiable source artifacts
- Integrations with GitHub pull requests, Slack conversations, and Jira incident reports to pool relevant knowledge
- Context-aware agent chat that gives coding agents trustworthy reference material to reduce hallucinations
- Unified search across repositories and linked collaboration tools with clear provenance on every claim