AI Automation

You want AI productivity gains but your data cannot touch a public cloud

We deploy private LLMs on your infrastructure — on-premise, air-gapped, or dedicated cloud — so your sensitive data never leaves your control.

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The challenges you're facing

Legal, HR, and finance teams forbidden from using public AI tools due to data classification policies

IP leakage risk when staff paste proprietary documents into ChatGPT or Copilot

No compliant path to AI productivity for teams handling PII, trade secrets, or regulated data

Enterprise AI That Stays Inside Your Perimeter

We deploy open-weight LLMs (Llama 3, Mistral, Qwen, Phi-4) on your own infrastructure — on-premise GPU servers, AWS private VPC, or Azure dedicated compute. The result is a ChatGPT-equivalent experience where all inference happens inside your network, with your data access controls, audit logging, and user authentication. No data leaves. No vendor dependency.

What you get

1

Use Case & Compliance Scoping

Define which teams, data types, and workflows the private LLM will serve, and map compliance requirements.

2

Infrastructure Design

Specify hardware (GPU type, VRAM, storage), network architecture, and access control model.

3

Model Deployment & Fine-tuning

Deploy the selected model, configure inference parameters, and optionally fine-tune on your domain data.

4

UI, Auth & Monitoring

Deploy a private chat UI with SSO/LDAP auth, usage logging, and model performance monitoring.

Technologies & tools

Llama 3.xMistralOllamavLLMOpen WebUINVIDIA A100/H100KubernetesPrometheus

Case study — anonymised

Legal Services — 200 lawyers

Before

Lawyers banned from using any public AI due to client confidentiality obligations. Junior associates spending 6 hours/day on research and drafting with no AI assistance.

After

Private Llama 3 70B deployed on dedicated GPU server, connected to internal document store. Lawyers use it freely for research, drafting, and summarisation.

3.5 hours/day reclaimed per associate, zero compliance incidents, full audit trail of every query

Further reading

Private LLM Deployment: A CTO's Guide

Your legal, HR, and finance teams want AI productivity gains. Your data classification policy says they can't touch a public cloud model. Here's how to close that gap.

Frequently Asked Questions

Common questions from enterprise and mid-market teams across India and internationally.

Which LLM models can be deployed privately?
We deploy Llama 3 (8B to 70B), Mistral (7B to 22B), Qwen 2.5, Phi-4, DeepSeek, and other open-weight models. Model choice depends on your hardware, latency requirements, and task complexity.
What hardware do I need to run a private LLM?
For a 70B model serving 50 concurrent users, you typically need 2× NVIDIA A100 80GB GPUs or equivalent. Smaller models (7B–13B) run on a single A100 or even an RTX 4090. We spec the hardware based on your expected load.
Can the private LLM access our internal documents?
Yes. We connect the LLM to a private RAG pipeline over your document store (SharePoint, Confluence, S3, network drives). The model can search and reference internal documents without those documents leaving your environment.
How is access to the private LLM controlled?
Access is gated by your existing SSO/LDAP. We integrate with Azure AD, Okta, or any SAML provider. You control which users and groups can access the AI, and every interaction is logged with user identity for audit.

Ready to get started?

Tell us about your situation and we'll respond with a tailored assessment within one business day.

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