GitMir
GitMir builds a living model of how a software product actually works, converting repositories, documents, and tool data into a unified intelligence layer that answers natural-language questions with verifiable source references. The platform connects to GitHub, GitLab, Jira, and other tools via APIs and MCP, providing this shared product understanding to both people and AI coding agents across the engineering workflow. GitMir offers three deployment modes—cloud, private source, and on-premises—and its GMM Local model runs entirely on a company's own hardware for data-sensitive environments.
- Developer Tools
- Artificial Intelligence
- Software Only
- AI Agents
- Data & Analytics
- Enterprise Software
Funding
Founders
Founder details are not available yet.
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 reconstruction across requirements, implementation, and review consumes hours per person per week and leads to rework and errors, especially as AI agents generate code without holding the whole product in context.
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 usage 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, CTOs, and engineering leaders at software product companies who need to reduce 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
- 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
- MCP-based integration for AI agents and connected systems, providing direct access to the product model
- GMM-1: a cloud reasoning model handling inputs over one million tokens with higher accuracy and lower cost per task
- GMM Local: a 30B model that runs on NVIDIA 24 GB+ or Apple Silicon 32 GB+ machines, keeping documents and code entirely on-premises
- Visual changelog showing business logic changes per commit, with "what changed" summaries computed on-demand when a project is opened
- Credit-based usage model where credits pay for reading repositories, not for asking questions