Private enterprise AI
Private AI, built around your business.
Deploy AI inside your own infrastructure. Your documents, knowledge and workflows stay within your environment while your teams get the benefits of modern AI.
Your data. Your infrastructure. Your AI.
One team across the whole lifecycle
- 01
Assess
Find where AI can create real value and identify the infrastructure, data and governance requirements before you build.
- 02
Architect
Design the models, retrieval systems, agents, data flows and infrastructure around your organization's requirements.
- 03
Deploy
Run AI inside your own servers, private cloud or controlled environment.
- 04
Manage
Monitor, optimize and maintain the system as models, data and business requirements evolve.
The problem
AI adoption gets complicated when the data can't leave.
Enterprise AI is rarely just a model problem. Sensitive documents, internal knowledge, access permissions, infrastructure, security requirements and governance all have to work together.
- Confidential data
- Internal documents and business knowledge may not belong in public AI services.
- Control
- Organizations need control over where models run, how data is processed and who can access the system.
- Integration
- AI needs to work with existing applications, databases, document repositories and business processes.
- Governance
- Teams need clear documentation around data flows, access, security, model behavior and operational controls.
That’s where private enterprise AI comes in.
What we build
An AI system around your business.
- 01
Private LLM deployment
Run capable language and multimodal models within your own infrastructure or controlled private environment.
- Llama
- Mistral
- Open-source models
- GPU infrastructure
- Docker
- Private cloud
- On-premise
- 02
Enterprise RAG
Turn internal documents and knowledge bases into searchable, conversational sources of truth.
- Document ingestion
- OCR
- Chunking
- Embeddings
- Vector search
- Hybrid retrieval
- Reranking
- Citations
- Access-aware retrieval
- 03
Document intelligence
Extract, understand, classify and summarize information from the documents your business already relies on.
- For example
- Contracts
- Policies
- Reports
- Technical documentation
- Invoices
- Internal knowledge bases
- 04
AI agents & automation
Connect AI to the tools and workflows your teams already use.
- For example
- Report generation
- Research workflows
- Internal support
- Data analysis
- Workflow automation
- Enterprise APIs
- 05
Security & access control
Design AI systems around the organization's existing security model.
- RBAC
- Authentication
- Authorization
- Data isolation
- Audit logs
- Encryption
- Secrets management
- 06
AI governance
Build the technical documentation and operational controls required to manage AI responsibly. Designed to support your organization's compliance and governance requirements.
- Data-flow documentation
- Model documentation
- Access policies
- Auditability
- Risk assessment
- AI usage policies
- Monitoring
- 07
Private infrastructure
Deploy and operate the AI stack where your organization requires it.
- On-premise servers
- Private cloud
- Dedicated GPU infrastructure
- Containerized deployments
- Secure APIs
- Monitoring
- 08
Managed AI
Keep the system reliable after launch.
- Model updates
- Performance monitoring
- Retrieval optimization
- Infrastructure monitoring
- Security updates
- Evaluation
- Cost optimization
- Technical support
Technologies are chosen per engagement. Not every organization needs every component.
How we work with you
Assess. Build. Operate.
Private AI is a system that keeps evolving with your data and your models. The engagement is shaped the same way: a clear assessment, a production build, and an operating relationship that keeps it reliable.
- 01
AI readiness assessment
Before building, understand.
Analyze your current infrastructure, data, workflows and AI opportunities. Identify where AI can create value, what should remain human-controlled and what technical or governance risks need to be addressed.
Deliverables
- AI opportunity map
- Architecture recommendations
- Data assessment
- Risk & governance assessment
- Implementation roadmap
- 02
Private AI implementation
From architecture to production.
Design and deploy the AI system around your infrastructure and business requirements.
Includes
- Architecture
- Model selection
- RAG
- Agents
- Data pipelines
- Integrations
- Security
- Deployment
- Testing
- Documentation
- 03
Managed AI operations
AI doesn't end at deployment.
Continuously monitor, maintain and improve the system as your business, data and models evolve.
Includes
- Monitoring
- Model upgrades
- Evaluation
- Optimization
- Security maintenance
- Infrastructure support
- Continuous improvements
Technical architecture
From your data to production AI.
Six layers, each one a place where enterprise requirements show up: permissions travel with the data, retrieval respects them, models run where you decide, and everything is observable.
- Layer 1
Business data
- Documents
- Databases
- Knowledge bases
- Internal applications
- APIs
- Layer 2
Data processing
- OCR
- Parsing
- Cleaning
- Chunking
- Metadata
- Access permissions
- Layer 3
Knowledge
- Embeddings
- Vector database
- Hybrid search
- Reranking
- Retrieval
- Layer 4
Private AI
- LLMs
- Multimodal models
- Inference
- Prompt orchestration
- Agents
- Layer 5
Applications
- Internal assistants
- Document intelligence
- Reports
- Enterprise search
- Workflow automation
- APIs
- Layer 6
Governance
- Authentication
- RBAC
- Audit logs
- Monitoring
- Evaluation
- Security
Why private AI
Two good answers to different questions.
Public AI services
Useful when
- Data sensitivity is low
- External APIs are acceptable
- Rapid experimentation is the priority
Private AI
Useful when
- Data is highly sensitive
- Infrastructure control matters
- Organizations need customized access controls
- Internal knowledge must remain controlled
- Governance and auditability are important
- AI needs deep integration with internal systems
Private doesn't mean isolated from modern AI. It means having control over how AI interacts with your business.
Security & governance
Designed for controlled environments.
- Data control
- Keep sensitive enterprise information within the environment you define.
- Access control
- Control which users, teams and systems can access AI capabilities and underlying knowledge.
- Auditability
- Track important system activity, access and AI interactions.
- Model control
- Choose where models run and how they are updated.
- Governance
- Document data flows, system behavior, risks and operational controls.
- Observability
- Monitor performance, failures, usage and system health.
Specific security and compliance controls are designed according to each organization's infrastructure, regulatory environment and requirements.
Representative deployment
Private knowledge assistant
- Problem
- An organization has thousands of internal documents spread across multiple repositories.
- Solution
- Deploy a private RAG system that allows authorized employees to ask questions across internal knowledge while respecting document-level access controls.
- Architecture
- Private LLM
- Document ingestion
- Embeddings
- Vector database
- Hybrid retrieval
- RBAC
- Audit logging
- Result
- Employees can access relevant organizational knowledge through a controlled AI interface without turning the entire document repository into a public AI data source.
The retrieval techniques here, hybrid vector and full-text search fused with Reciprocal Rank Fusion and answers that cite their sources, are the same ones we shipped in Vemio. See Vemio
Technology
The stack we already run in production.
- Models
- Llama
- Mistral
- Gemini
- Hugging Face
- PyTorch
- TensorFlow
- Data & retrieval
- RAG
- Embeddings
- pgvector
- Vector databases
- Hybrid search
- Full-text search
- Backend
- Python
- FastAPI
- Node.js
- PostgreSQL
- Redis
- Celery
- Infrastructure
- Docker
- Nginx
- GPU infrastructure
- Private cloud
- On-premise
- Monitoring
- AI applications
- AI agents
- Document intelligence
- Enterprise search
- Workflow automation
- Multimodal AI
Contact
Have sensitive data and an AI problem?
Tell us what your team is trying to automate, understand or build. We'll help you determine whether private AI is the right architecture, and what it would take to put it into production.