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.
AI for HR: Private Talent Screening, Policy Parsing & Workforce Planning
See how private AI helps HR streamline talent screening, parse policies, and plan smarter workforces without exposing sensitive data.
From Documents to Decisions: How BYOD-AI Transforms PDFs Into Business Intelligence
Static documents become searchable, interactive, and invaluable tools for informed decision-making.
Why Generative AI Fails Without Domain Context—And How to Fix It
Generative AI fails without domain context. Learn how expert data, guardrails, and feedback loops turn shaky outputs into reliable answers at work now
Real-Time Document Verification Using Internal AI Models
Real-time document verification with internal AI models boosts speed, cuts fraud risk, and ensures compliance with instant, secure validation.
Why Multimodal Private LLMs Are the Next Enterprise Standard
Discover why multimodal private LLMs are becoming the enterprise standard for secure, cross-channel AI insight and smarter operations.
Privacy-Preserving Analytics: LLMs for Internal BI Dashboards
Explore how privacy-preserving analytics use private LLMs to power BI dashboards with plain-language insights while keeping sensitive data secure.
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.