GitMir provides software and company intelligence infrastructure that creates a living, queryable model of how a product and organization actually work—from business logic and decisions down to the implementing code. The platform connects to existing engineering tools and repositories to give both people and AI agents precise, verifiable answers about product behavior, reducing the time spent reconstructing understanding before and during development work.
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Developer Tools
- Enterprise Software
- Software Only
Funding
Funding not disclosed
Founders
Product
Problem
Engineering teams spend significant time reconstructing what a product already does, what a change touches, and whether the result matches intended behavior before writing a single line of code. This repeated understanding work—across briefs, code, docs, and reviews—drives up costs, slows delivery, and increases rework, especially as AI agents enter the workflow and require precise context to produce correct results.
Solution
GitMir builds a living model of how a product and company actually work, converting raw repositories, documents, and tool data into a unified intelligence layer spanning product features, code architecture, decisions, legal terms, and metrics. The platform connects to GitHub, GitLab, Bitbucket, Notion, Confluence, Jira, and other tools via APIs and MCP, then answers natural-language questions with sentences that name the exact parts of the product they come from. This shared, versioned product model stays available across the engineering loop—from incoming requirements and impact analysis to development tasks, implementation, and commit review—so both people and AI agents work from the same verified understanding without pulling senior engineers aside. GitMir publishes measured results, including a 90-to-20-minute reduction in time to understand a change, rework dropping from 69% to 5%, and AI cost per correct answer falling from $0.35 to $0.08 on its benchmark.
Target Audience
Primary customers are engineering teams and engineering leaders at software product companies who need to reduce the time spent on understanding, clarification, and rework, as well as organizations deploying AI coding agents that require precise product context to generate correct code.
Features
- Unified intelligence model that converts raw API, code, PRD, terms, and data into product features, code architecture, documents, decisions, legal compliance, and usage metrics
- Natural-language Q&A that returns answers with named source parts, enabling verification without needing to know code identifiers
- Connectors for repositories (GitHub, GitLab, Bitbucket), documents (Notion, Confluence), and tools (Jira, Asana, ClickUp, Linear, CodeRabbit) via API and MCP
- Impact analysis that surfaces affected areas, records, actions, endpoints, screens, and customer paths for any proposed change
- Three deployment modes—cloud, private source, and on-premises—differing only in which boundary source code crosses, with the Local Connector processing only permitted data
- Published benchmark results with 1,050 blind-graded answers across a 459,339-line repository, showing 92% answer quality with 8.4K context tokens and $0.07 cost per answer