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Intelligence Infrastructure

Same AI.
Different intelligence.

Closer to human intent.
Less context. Better results.

Give your people and AI systems better intelligence — without changing the models, agents or workflows they already use.

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WORKS WITH CLAUDE · CODEX · CURSOR · COPILOT · ANY MCP-COMPATIBLE SYSTEM

Benchmark

Same model. Same task.
Different result.

What the model was givenAnswer qualityContext usedCost / questionLatency
GitMir92%8.4K tokens$0.074.8s
Unblocked78%27.4K tokens$0.199.6s
Augment Code74%31.6K tokens$0.2210.9s
Sourcegraph Cody69%38.1K tokens$0.2612.4s
Qodo64%35.8K tokens$0.2411.7s
Greptile66%42.7K tokens$0.2913.1s
Raw repository context58%61.3K tokens$0.4118.9s

SAME REPOSITORY. SAME QUESTIONS. SAME MODEL.

View benchmark methodology
Semantic distance

AI works best
closer to human intent.

People ask
“INTENT

Add loyalty rewards to checkout.

“INTENT

Allow customers to pause their subscription.

“INTENT

What will break if we change refunds?

What has to happen next

Traditional AI

  1. Human intent
  2. Search
  3. Technical context
  4. Reconstruction
  5. Reasoning
  6. Answer

GitMir

  1. Human intent
  2. Intelligence
  3. Reasoning
  4. Answer

People communicate through intent and outcomes — not files, functions and dependencies. GitMir gives AI intelligence closer to the level where people actually think and work.

The product

Build intelligence once.
Use it everywhere.

Your existing AI stack stays exactly where it is.

SOURCESGitHubGitLabCompany sourcesMCPCONSUMERSClaude CodeCodexCursorCopilotInternal agentsEmployees
GITMIR · FIG. 01 · BUILD INTELLIGENCE ONCE, USE IT EVERYWHERE
SCALE — · SHEET 1/1

ONE INTELLIGENCE LAYER. FOR EVERY HUMAN AND AI SYSTEM.

AVAILABLE THROUGH MCP.

Start with GitHubAbout GitMir Lab
Capability

One intelligence layer.
Better decisions across the company.

Ask what used to require hours of reconstruction — in the words the work was assigned in.

Developer
  • What should I know before making this change?
  • What can break?
  • Give my coding agent everything it needs to complete this task correctly.
CTO
  • Where is engineering complexity creating the most risk?
  • Which changes affect the largest part of the product?
  • Where is AI repeatedly spending money reconstructing context?
Product
  • What changes for customers if we ship this?
  • Where does actual product behavior differ from what we intended?
  • What should we fix first?
AI Product
  • What does this agent need to know to complete this task?
  • Where are we repeatedly paying AI to reconstruct the same knowledge?
  • How can we reduce cost per successful customer outcome?
Executive
  • What changed?
  • What matters?
  • Where is the risk?
See real answers on a real system
Economics

Understand once.
Use that intelligence everywhere.

Context is what you pay for — on every question, every day, by every agent. The measured difference against the strongest system tested:

−63%cost per question
−69%context required
−50%time to answer
+18%successful outcomes

Better outcomes are only part of the return. The same intelligence improves engineering, product and executive decisions.

AGAINST UNBLOCKED. AGAINST RAW REPOSITORY CONTEXT THE SAME MODEL COSTS $0.41 A QUESTION AND ANSWERS 58% CORRECTLY.

Deployment

Your code stays
where you decide.

Cloud

Connect GitHub in one click.
GitMir continuously updates intelligence as your software changes.

Private Source

Source code stays in your environment.
The GitMir Local Connector processes permitted sources locally and securely synchronizes derived intelligence with GitMir Lab.

Enterprise

Everything stays inside your boundary.
Private GitMir deployment, private MCP, and VPC, on-prem or air-gapped options.

Questions

Asked by engineers and CIOs.

What does GitMir actually do?

It gives your people and AI systems better intelligence about your software, without changing the models, agents or workflows they already use. The same model, given that intelligence, answers questions it otherwise gets wrong.

Do we have to change our AI stack?

No. GitMir connects over MCP and works with Claude, Codex, Cursor, Copilot and any MCP-compatible system. It is a layer of intelligence, not a replacement for the tools you run today.

Why would the same model give a better answer?

Because of semantic distance. People describe what they want in terms of products, customers and outcomes; most systems must translate that back into files, symbols and dependencies before they can begin. GitMir gives AI intelligence closer to the level where people actually think and work, so less reasoning is spent reconstructing meaning.

Where does the source code go?

Wherever you decide. Cloud connects GitHub directly; Private Source keeps the code in your own environment and synchronizes only derived intelligence; Enterprise keeps everything inside your boundary.

Do you publish how it works?

We publish benchmark methodology and measurable outcomes. GitMir's intelligence technology is proprietary — we do not publish the internal representation or interpretation system.

Give your AI better intelligence.

Keep your models. Keep your workflows. Change what they understand.

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