Give it specifications, documentation, contracts, source code, operational information or other large inputs. It can work through inputs far beyond what fits into the model’s context window.
GitMir Enterprise
Private AI
Inside your infrastructure
Run capable AI across your company without sending your code, documents, prompts or operational data outside your security boundary.
GMM Private runs on your infrastructure.GitMir Intelligence gives it a current understanding of your software and company.One private AI layer for your people, agents and systems.
- 30B
- Private GitMir model
- Beyond the context window
- Work with inputs far larger than the model can take at once
- Your infrastructure
- VPC · on-premises · isolated environments
- Your data
- Stays inside your boundary
Close the AI loop
Most enterprise AI still depends on an external model.
Today
- Your company sends context out
- The model returns an answer
- Every new task requires more context again
…and again, for every task
With GitMir Enterprise
GitMir gives you another option
The model, the Intelligence, the agents and the data can all run inside your infrastructure.
No external model is required for the work you choose to keep private.
GMM Private
A compact model
Built for very large inputs
GMM Private is GitMir’s 30B model deployed on your own infrastructure.
Published GMM-30B results
500 requirements
5 / 5GMM-30B
0 / 5same model alone
Complete answers on the tested questions.
340,000 characters · 13 tasks in one request
13 / 13GMM-30B
0 / 13same model alone
Every requested task completed in the published test.
New documents
55 / 56GMM-30B
14 / 56same model alone
Correct answers on documents not used before the test.
2,000 requirements
All 16 requirements in a dependency chain returned correctly in the published test
Measured 4–5 Oct 2026 · the same model with GitMir and alone, on the same inputs and questions · See the benchmarks →
GitMir Intelligence
Your company’s Intelligence
Beside your private model
A private model solves the inference problem. GitMir Intelligence solves the understanding problem.
Instead of reconstructing your company from raw files on every task, your AI can work from a current Intelligence layer covering the systems you choose to connect.
- Software
- Business rules
- Documentation
- Decisions
- Operational knowledge
- Dependencies
One Intelligence across the companyYour models can change. Your agents can change. The underlying understanding stays inside your infrastructure.
Everything stays inside
- GMM Private
- Your AI model runs on your servers.
- GitMir Intelligence
- Your company’s Intelligence stays in your environment.
- Private MCP
- Agents access Intelligence through an endpoint inside your boundary.
- Your agents
- Internal agents and applications use the model and Intelligence without moving company data outside.
- Your data
- Code, documents, prompts, answers and operational information stay where your security policy requires them to stay.
From private model
to private AI infrastructure
Not another isolated chatbot. GitMir Enterprise becomes an AI layer that your existing systems can use.
Engineering
Agents work across large codebases, specifications and product knowledge without sending proprietary context to an external model.
Discuss this workloadDocuments
Work across large specifications, policies, contracts and internal documentation that cannot be uploaded to public AI services.
Discuss this workloadInternal agents
Give company agents a private model and a shared source of Intelligence.
Discuss this workloadOperations
Work with internal rules, processes and operational information inside the company boundary.
Discuss this workloadProduct
Keep product logic, decisions and documentation available to both people and AI.
Discuss this workloadRegulated environments
Run inside the infrastructure and network boundaries your organization requires.
Discuss this workloadInternal agents
Give every internal agent
a private brain
Your company may have dozens or hundreds of AI agents. They should not each reconstruct the company independently.
The result is one private AI infrastructure shared across the organization.
Connect them to
- GMM Privatefor reasoning and execution
- GitMir Intelligencefor current company understanding
- Private MCPfor access
The model does not need
to be enormous
The largest cloud model is not automatically the best architecture for every enterprise task.
GitMir is designed around a different objective
Get more useful work from a smaller model while keeping the entire workload private.
That changes the economics and the security model of enterprise AI.
Your sensitive workload no longer has to depend on sending increasingly large prompts to an external frontier model.
For software organizations
AI that knows your software
before it changes it
For software organizations, GitMir Intelligence maintains a current model of how the product works. Agents receive the relevant behaviour, rules and dependencies before they act. Changes can then be checked against the same Intelligence before they merge.
Nearly 3×fewer errors and omissions8% of the answer score lost versus 22% for the strongest measured alternative, using the same model
210×+less source textat least, on the largest measured question
Every pushcurrent after every pushthe Intelligence stays synchronized with the product
See the Enterprise engineering benchmark →
One example, end to end
From a requirement to an accepted change
About the public example
Supabase Studio — the dashboard behind a database platform. 459,339 lines of source interpreted · 2,023 described parts · 409 endpoints · 17 described flows. Riskiest place to change: Database — 30 things depend on PGTable. The agent’s work in the tour is illustrative. Open the public model →
One private stack
Company data, Intelligence, the model and access for your agents — all inside your infrastructure.
Nothing needs to cross the boundary
Deployment
Deploy where your policy requires it
Private CloudInside your VPC.
On-premisesInside your own infrastructure.
IsolatedFor environments with no external data path.
Deployment topology, access, updates and operational responsibilities are agreed as part of the Enterprise implementation.
Data boundary
Where code, intelligence and answers live
Three ways to run GitMir, and what each one keeps inside your network. Only a private deployment keeps everything inside.
| What | Cloud | Private Source | Private deployment |
|---|---|---|---|
| Where the source code is read | On GitMir’s servers, from your git host | Inside your network, by the GitMir Local Connector | Inside your boundary, by your own GitMir installation |
| Where derived intelligence is stored | On GitMir’s servers | Synchronized outward to GitMir and stored there | Inside your boundary |
| Where MCP answers come from | GitMir’s MCP endpoint | GitMir’s MCP endpoint, from the synchronized intelligence | A private MCP endpoint inside your boundary |
| What leaves your network | The code GitMir reads, and the questions your people and agents ask | Derived intelligence and the questions asked — not the source code | Only requests to hosts your administrator names, such as a model or a mail gateway; any other request is refused before it is sent |
| Business, customer and operational data you connect | Read by GitMir | Stays in your environment | Stays inside your boundary |
| Where product analytics go | To GitMir: which actions happen and when, not your code | To GitMir, as in Cloud | Stay inside your boundary, or are switched off; a weekly count leaves only when a person exports it |
| Who operates it | GitMir | You run the connector; GitMir runs the service | You, on your infrastructure, with GitMir’s help as your contract sets out |
Built for Enterprise control
Data boundaryYour sources and derived Intelligence stay inside your environment.
IdentitySSO, roles, groups and project-level access.
Model controlRun your private GMM deployment instead of depending on an external inference endpoint.
IntegrationsRepositories, documents, internal systems and supported company data.
ProcurementEnterprise agreement, invoicing, deployment responsibilities and support.
No training on your dataYour private code and Intelligence are not used to train models for other customers.
Start with the workload
you cannot send to the cloud
You do not need to migrate your entire AI stack to evaluate GitMir. Choose one difficult workload:
- a large specification
- a private codebase
- an internal knowledge corpus
- a regulated workflow
- an agent that currently requires enormous external context
We deploy it inside your boundary and measure the result.
GitMir Enterprise
Your AI. Your Intelligence
Your infrastructure
A private 30B model capable of working through very large inputs.A persistent Intelligence layer for your company.Private access for your agents and systems.