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PRIVATE & ON-PREMISE AI

Self-hosted AI, deployed on your infrastructure

We deploy open-source AI for businesses that can't put their data in someone else's cloud — Glean alternatives, private GPT, RAG over your documents, all running in your tenant. No data leaks. No per-seat lock-in. No vendor surprises.
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5–10×

Cheaper than per-seat ChatGPT Enterprise or Glean at scale, once seat count crosses ~100 users.

10+

Private AI implementations across regulated and confidentiality-sensitive industries.

100%

Your data stays in your tenant — no third-party LLM provider sees a token of it.

What you get from a private-AI deployment

Six outcomes regulated companies — and any team uneasy about feeding client data to OpenAI — get from a private deployment.

Data Sovereignty

Your documents, conversations, embeddings, and audit logs all live in your tenant. The LLM runs on your infrastructure, so no third-party AI provider sees a token of it.

Open-Source Stack

Onyx, LibreChat, Open WebUI, vLLM, Ollama. Battle-tested, MIT-licensed software with no vendor that can change pricing on you or shut you down.

Permission-Aware Retrieval

Connector-based RAG that respects RBAC, matter walls, and file-level permissions. Employees only see what they have access to in source apps.

Cost Predictability

One-time deployment plus a flat managed retainer. No per-seat surprises as headcount grows. At scale, 5–10× cheaper than ChatGPT Enterprise or Glean.

Compliance-Ready

HIPAA, GDPR, SOC 2, attorney-client privilege, and government data-residency rules are all addressable when the LLM and data live in your tenant.

Bring Your Own LLM

Run Llama, Mistral, Qwen via vLLM or Ollama, or route to OpenAI, Anthropic, Gemini through an LLM gateway. Switch models without rebuilding the stack.

Why off-the-shelf SaaS misses on private AI

ChatGPT Enterprise, Glean, and the rest of the multi-tenant LLM SaaS were designed for the median knowledge worker at the median company. That’s fine for general productivity, but it leaves the parts where data sensitivity, regulatory posture, and seat economics make or break the deal stuck on autopilot. Three patterns we see repeatedly: (1) data exposure that fails legal, compliance, or InfoSec review the moment usage scales past pilot; (2) per-seat pricing that punishes you for rolling out the tool to the people who’d benefit most; and (3) permission models that ignore your actual ACLs — your interns can ask the assistant about board minutes.

Worse, none of these vendors get smarter from your data because they’re multi-tenant. Your usage patterns and corrections train the next vendor’s customer, not your stack.

Private AI lives inside your tenant. Your data, your embeddings, your audit logs, and any model fine-tunes — all under controls your security team already owns. The system gets smarter from your usage, on infrastructure you already trust, with no third-party LLM provider in the data path.

The open-source stack we deploy

Onyx — Open-source Glean alternative

  • Enterprise AI search and chat over 40+ workplace apps
  • Plug-and-play connectors: Slack, Drive, Confluence, GitHub, Jira, Salesforce, SharePoint, Notion
  • Permission-aware retrieval — respects each source's ACLs
  • In production at Netflix, Ramp, and 1,000+ teams

LibreChat / Open WebUI — Self-hosted ChatGPT

  • Multi-model chat UIs — OpenAI, Anthropic, Gemini, Bedrock, plus self-hosted
  • Agents, code interpreter, web search, SSO, MCP support
  • Battle-tested at Shopify, Daimler, Boston University, ClickHouse, Stripe
  • Replaces ChatGPT Team / Plus subscriptions on your tenant

vLLM / SGLang — Production LLM serving

  • High-throughput inference for Llama, Mistral, Qwen, and other open models
  • Paged attention, continuous batching, low-latency serving on Kubernetes
  • Throughput tuning, per-team quotas, full observability
  • Where private AI gets economic at scale

Ollama — Easy local model deployment

  • Fastest way to run open-source LLMs on a workstation, single GPU, or production
  • Pairs with Open WebUI for an instant private-ChatGPT setup
  • Local-first option for the smallest pilots
  • Same model API surface as OpenAI / Anthropic

The private-AI cluster — solutions we deploy

Eight discrete deployment options across the private-AI cluster. Built on open-source tools (Onyx, LibreChat, vLLM, Ollama) running on your infrastructure — not in a vendor’s multi-tenant cloud.

1. Private ChatGPT for Business

A self-hosted ChatGPT-style interface — Open WebUI or LibreChat — connected to your Slack, Drive, Confluence, and documents. Replaces the ChatGPT Team / Plus subscriptions your employees are already paying for out of pocket.

2. LLM Inference & Serving Infrastructure

Production model serving on your GPUs — vLLM, SGLang, TensorRT-LLM — with horizontal scaling, observability, and per-team quotas. The compute substrate underneath every private-AI workload.

3. vLLM Development & Deployment

High-throughput vLLM deployment with paged attention, continuous batching, and Kubernetes-native scaling. Tuned for your model and your traffic pattern — latency, throughput, and cost-per-token targets defined up front.

4. Ollama Deployment & Integration

Local-LLM deployment for workstations, single GPUs, or small production instances. Fastest path to an end-to-end private setup — Ollama + Open WebUI gets a 50-person team to private ChatGPT in days, not weeks.

5. Self-Hosted ChatGPT Deployment

Open WebUI and LibreChat install, SSO/SAML, branding, and multi-LLM routing. The chat-UI half of a private-AI stack — connected to OpenAI, Anthropic, Gemini, or your own self-hosted models.

6. Self-Hosted AI for Business

End-to-end self-hosted AI for businesses going off OpenAI — chat UI, retrieval, model serving, and observability glued into one operating stack you own. The whole package for teams who want one engagement, one stack.

7. Air-Gapped & Offline AI for Regulated Industries

Fully disconnected AI for classified environments and regulators with hard data-residency rules. Onyx + a private LLM deployed in your air-gapped environment, with audit logs, SSO, and compliance documentation included.

8. Chat With Your Documents — Private RAG

Single-corpus document chat that stays inside your tenant. Ideal for legal matter files, M&A data rooms, internal knowledge bases, or research libraries — the data goes in, the answers come out, nothing leaves.

START TODAY

Talk to a Private AI expert

Bring us your industry, your data-sensitivity profile, and your seat economics. We’ll come prepared with the right stack — and a directional read on what you can stand up in your tenant next sprint.

Ask us about

  • Open-source Glean alternative (Onyx / Danswer) deployment
  • Private ChatGPT for business (LibreChat / Open WebUI)
  • Production LLM serving with vLLM / SGLang on your GPUs
  • Permission-aware retrieval (Slack / Drive / Confluence / iManage)
  • Air-gapped + compliance-ready (HIPAA / SOC 2 / privilege)
  • Bring-your-own-LLM gateway and LLMOps observability

    Contact Us
    Need experts to collaborate with for your AI/ML journey? Drop us an email and we will get in touch

    When you need private AI, not ChatGPT Enterprise or Glean

    ChatGPT Enterprise and Glean cover the median knowledge worker well — general chat, generic enterprise search, off-the-shelf retrieval. That’s enough if your data sensitivity profile and seat count are well-represented by the average customer.

    But the teams winning on private AI need things multi-tenant SaaS structurally can’t deliver:

    • Models and embeddings inside your tenant — not in a vendor’s multi-tenant cloud
    • Permission-aware retrieval that respects your ACLs — not a generic share-everything default
    • Audit logs your CISO and regulator can actually audit — not a vendor SOC report
    • Bring-your-own-LLM gateway — Llama, Mistral, Qwen, plus optional OpenAI / Anthropic / Gemini via your enterprise contract
    • Flat licensing economics — not per-seat charges that compound as headcount grows
    • Integrated with your stack — Slack, Drive, Confluence, iManage, SharePoint, NetDocuments — not a parallel SaaS island

    Private AI runs inside the environment your security team already owns, and gets better the more your people use it — which is the part vendors can’t sell back to you next year as a paid feature.

    Frequently asked questions

    At small scale (under ~50 seats), hosted SaaS is often cheaper. Past ~100 seats the math tips fast. A 100-person company on Glean at ~$60/seat/month pays $72,000/year just in software. A standard private-AI deployment plus a year of managed service runs roughly the same — but you own the stack from year two on. ChatGPT Enterprise at 500 seats is $360K–$600K/year; the deployment plus a year of managed beats one year of those licences.
    Yes. Onyx alone ships 40+ connectors out of the box (Slack, Google Drive, Confluence, GitHub, Jira, Salesforce, Notion, SharePoint, and more). For legal-specific systems like iManage and NetDocuments we add custom connectors as part of the deployment. Connectors sync incrementally and respect each source's ACLs — users only see what they'd see in the source app.
    Because the LLM and the data both live in your tenant, your existing certifications and BAAs carry over — there's no new third-party data processor to evaluate. We add audit logging, SSO/SAML, RBAC, and encryption-at-rest as part of every deployment, and on enterprise engagements we deliver a SOC-2-readiness pack and the documentation for HIPAA, GDPR, and regulator review.
    Either. The chat and search layers (LibreChat, Onyx) are model-agnostic. You can start with OpenAI or Anthropic via your enterprise contract and add self-hosted models on vLLM/Ollama later — or go fully self-hosted from day one. We'll recommend the option that matches your data-sensitivity profile and the unit economics of your usage.
    Yes — that's usually the right path. A common sequence: start with Open WebUI + Ollama (or LibreChat + a hosted LLM) for the first business unit. Add Onyx for cross-app search once that's working. Move to self-hosted vLLM serving once usage justifies the GPU spend. Each step compounds on the last; nothing has to be rebuilt.
    Every engagement includes post-launch support standard. After that, an optional managed retainer covers monitoring, version upgrades, connector additions, and quarterly reviews. Or you can take ownership in-house — we hand off a complete runbook either way.

    Related solutions in the private-AI cluster

    Additional resources

    AI Transformation Workshop

    Half-day strategy workshop to identify the highest-ROI private-AI moves for your team. Book a workshop →

    AI Strategy Session

    60-minute scoping call. Directional read on the right private-AI stack for your environment + a sample architecture. Book a session →

    AI Consultant vs In-House Team

    Honest tradeoffs comparison for build-vs-buy decisions on private AI for your organization. Read the comparison →

    Ready to deploy private AI?

    A 45-minute strategy call. We come prepared with the right stack for your industry, your data sensitivity, and your seat economics — and a directional read on what you can stand up in your tenant next sprint.

    Book a Strategy Session
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