Secure & customizable private LLMs for agentic AI in regulated industries.
Deploy production-grade language models on-prem, in your own cloud, or at the edge — fully sovereign, compliant, and auditable. Your data never leaves your perimeter, and every answer is grounded in your own knowledge.
Two ways to bring AI to sensitive data.
One sends your most sensitive information to a model you don't control. The other keeps everything inside your perimeter, on a model that's yours. Watch where the data goes.
Sending data to someone else's model
Every prompt and document leaves your perimeter for a third-party API you don't control.
- Prompts & documents leave your network
- May be retained or used to train vendor models
- Limited audit trail, residency & access control
- Compliance exposure — HIPAA · SOC 2 · GDPR
A model you own, inside your walls
Retrieval, inference, and agents all run on your infrastructure. Data stays contained — and audited.
- Data never leaves your perimeter
- Your model, your weights — no vendor training
- Every prompt & response captured in an audit log
- Sovereign, compliant & auditable by design
Deploy any leading open or frontier model — fully under your control
Everything you need to run AI privately.
One platform spanning private deployment, retrieval, agents, and governance — so you can move from pilot to production without surrendering control of your data.
Custom AI Agents
Purpose-built agentic workflows that reason over your data and take action inside your stack — securely.
On-Prem & Private
Run open-weight models entirely within your perimeter. No data leaves your environment, ever.
RAG & Retrieval
Ground every answer in your documents with retrieval pipelines tuned for accuracy and citations.
LLM-as-a-Service
Managed private inference in your cloud account — the control of self-hosting, none of the ops.
Hybrid LLM
Route sensitive work to private models and the rest to frontier APIs — one governed control plane.
Edge Deployment
Inference on local hardware for air-gapped, low-latency, and field environments.
Pre-configured AI appliances, ready to run.
We spec, build, and install GPU hardware sized to your models and your throughput — delivered ready for inference. Rack it in your data center or run it at the edge. No cloud dependency required.
- ▹Sized to your models and load
- ▹On-site setup & installation
- ▹Air-gapped & offline capable
Answers grounded in your own knowledge.
Your documents are indexed and retrieved at query time, so every response is grounded in your sources — with citations. Less hallucination, current answers, and a full record of where each fact came from.
- ▹Cited, source-grounded responses
- ▹Document-level access control
- ▹Connects to your existing data
Built for the security review.
Governance is not a bolt-on. Access control, audit logging, and data classification are part of the platform — the controls your compliance team will actually ask for.
Audit logging.
Every prompt, retrieval, and model response is captured for review, compliance, and incident response.
Access controls.
Role-based permissions, SSO, and document-level entitlements so models only see what each user may see.
Data tagging & redaction.
Classify, tag, and redact sensitive data — PII, PHI, and privileged content — before it ever reaches a model.
Connects to the data you already have.
Securely integrate the systems where your knowledge lives — clouds, warehouses, and document stores — without moving data out of your control.
Put private AI to work.
Security-first AI Agents
Agents engineered for regulated, high-stakes environments.
Email, Call & Meeting Summarization
Private summarization across your communication channels.
Internal Search
Semantic search across every internal knowledge source.
Multi-document Q&A
Ask questions spanning thousands of documents at once.
Custom Chatbots
Branded assistants grounded in your own corpus.
Offline AI Agents
Fully air-gapped agents for disconnected environments.
Knowledge Base Assistants
Turn your KB into an answer engine for staff and customers.
Contract Review
Surface risk, clauses, and obligations across agreements.
Why teams choose private.
Illustrative examples of the outcomes private deployment unlocks. Labeled “Sample” — not attributed to specific named clients.
Running our own models on-prem meant we could finally use generative AI on regulated data without a compliance fight. The audit trail alone made the security review trivial.
We needed answers grounded in privileged documents that could never leave our network. The retrieval pipeline gave us citations our reviewers actually trust.
Edge deployment let us put an assistant in environments with no connectivity at all. It just works, offline, on our own hardware.
Field notes on private AI.
CAPEX vs OPEX in Open Source AI Deployments
Choosing between owning AI infrastructure and paying for it as you go shapes cost, control, and flexibility for years, not just the first invoice. Most companies land on a hybrid approach that matches CAPEX to stable core workloads and OPEX to experimentation and burst capacity.
Why Open Source AI Is Cheaper Long-Term (Even When It Looks More Expensive)
Open-source AI often looks more expensive upfront than a subscription, but subscription costs, usage-based pricing, and vendor lock-in add up in ways that rarely show on the first invoice. Ownership, customization, and internal knowledge are what make the long-term math work in open source's favor.
Private LLMs for Quality Assurance in Manufacturing and Operations
Private LLMs give quality teams a real-time interpreter for maintenance logs, sensor streams, and operator notes that once lived in disconnected systems. Deployed behind the factory firewall, they turn scattered production data into plain-language explanations engineers can act on immediately.
Frequently asked.
Still have questions about deploying AI privately? Talk to an engineer who has done it before.
Book a CallWhat is a private LLM, and how is it different from ChatGPT?
Where can the models be deployed?
How do you keep our data secure and compliant?
Which models do you support?
How do you ground answers in our own data?
Bring generative AI to your most sensitive data.
Tell us about your use case and your constraints. We'll map a path to a private, compliant, production-grade deployment — on-prem, in your cloud, or at the edge.