More intelligence.
Less compute.
GitMir builds systems that solve complex problems without requiring ever-larger AI models. We separate knowledge from reasoning — making intelligence more efficient, inspectable and reusable.
Knowledge and reasoning
are not the same thing.
AI models are powerful knowledge engines. GitMir moves the reasoning architecture outside model weights into a structured, mutable object graph.
Knowledge
Models, databases, documents, scientific data, code and external systems.
Meaning
Objects, relationships, states, rules, processes and constraints.
Reasoning
A dynamic graph that determines how information is interpreted and what happens next.
Runtime
Interprets the reasoning graph into executable behavior and feeds outcomes back into the model.
Code is implementation.
We extract what it means.
Software repositories can contain gigabytes of implementation detail. The business logic they express can be represented far more compactly. GitMir interprets source code into a structured model of entities, states, events, rules, processes and dependencies — while preserving traceability back to the source.
SCALE — · SHEET 1/1
Interpret once.
Reconstruct the meaning of a software system from its source and keep the model synchronized as the code evolves.
Reason many times.
Developers, agents, auditors and analysts can work from the same compact representation instead of repeatedly reconstructing the system from code.
A repository of software meaning.
GitHub stores source code. GitMir models how software works. Open-source repositories can become searchable, comparable, machine-readable models of product behavior — updated as their source repositories change.
One architecture.
Different problem spaces.
Software
Understand product logic, give agents a compact shared model and verify the impact of changes.
Explore Software →Enterprise
Turn fragmented processes, decisions and company knowledge into explicit, executable intelligence.
Explore Enterprise →Science
Represent relationships, constraints and hypotheses as reasoning structures that can evolve during discovery.
Explore Science →The architecture should prove itself.
We measure GitMir against model-centric analysis and reasoning on real workloads.
Representation size
Source representation vs. structured meaning model.
Tokens / compute
Resources required to understand and reason about the same system.
Reasoning time
Time to reach a usable structured understanding or decision.
Accuracy
Correctness of the resulting interpretation and downstream action.
Rework
How often work must be repeated because dependencies or rules were missed.
Human supervision
Review and intervention required to reach the intended outcome.
Asked by engineers and CIOs.
The meaning of a system is interpreted once into a compact structured model. Each task receives only the relevant slice of that model instead of raw context, so the same decisions need fewer tokens — and the interpretation cost is paid once, not per request.
An explicit representation of what software actually does — objects, relationships, states, rules and processes — kept synchronized with the source code and readable by people and machines alike. GitHub stores source code; GitMir models how software works.
Retrieval returns text fragments that a model must re-interpret every time. GitMir returns structured objects with identities, relations and rules, with traceability back to source — so reasoning starts from meaning, not from search results.
GitMir is model-agnostic. Knowledge engines are interchangeable — Claude, GPT, Gemini or open-weight models — while the reasoning layer stays yours: inspectable, correctable and portable.
Yes — that is the first application. GitMir reconstructs the product model from the repository, and coding agents (Claude Code, Codex, Cursor) work against that model: impact is computed before changes and results are verified after.
The source-code layer already exists.
We are building the meaning layer above it.
A shared representation that people, software and AI can understand, reason over and reuse.