Private LLM deployment for data that can't leave the building.
A private AI stack runs language models inside your own infrastructure — on-premise servers or your own cloud account in India — so sensitive data never goes to a public AI API. Techcream helps Indian SMEs in healthcare, finance and manufacturing deploy right-sized open-source models with RAG, access control and audit logs.
Privacy rules and costs are pushing AI in-house
Hospitals, NBFCs, insurers and manufacturers want AI's productivity gains, but patient records, financial data and process IP can't be pasted into a public chatbot. Small open-source models have become good enough for most business tasks — and with the DPDP Act raising the stakes on personal data, running AI in your own environment is often the simplest path to yes.
A private AI stack that keeps data in-house
Sensitive work never leaves your boundary; only safe tasks can be routed to public APIs.
- Your servers / Indian cloud region
Your staff
- Web app
- Slack / Teams
- Vernacular chat
Gateway
- SSO & roles
- PII redaction
- Smart router
Private models
- Open-source LLM
- Embeddings
- Fine-tunes
Your data
- Patient / client records
- Documents
- Audit logs
Optional
- Public API for non-sensitive tasks only
Everything needed to go live.
Model selection & sizing
Right-sized open models (Llama, Qwen, Mistral, Gemma and others) matched to your tasks and budget.
Deployment
On-prem GPU servers, Indian cloud regions or your VPC — with vLLM / Ollama-style serving.
Private RAG
Assistants over your documents with everything — embeddings included — kept in your environment.
Fine-tuning
Optional tuning on your terminology, formats and languages, including Indian languages.
Security & audit
SSO, role-based access, encryption, audit logs and data-retention controls.
Hybrid routing
Send only non-sensitive tasks to frontier APIs, keep sensitive ones local — automatically.
From first call to production.
- 1
Assess
Classify data, map use cases, and estimate volume and hardware needs.
- 2
Pilot
Benchmark candidate models on your real tasks before buying hardware.
- 3
Deploy
Install, secure and integrate the stack with your systems and SSO.
- 4
Support
Monitoring, model updates and capacity planning as usage grows.
Where it pays off first.
- ✓Clinical documentation and discharge summaries in hospitals
- ✓Loan, KYC and claims document processing for NBFCs and insurers
- ✓SOP, quality and maintenance assistants on the factory floor
- ✓Internal knowledge assistants for legal and CA firms
- ✓Vernacular customer support with data kept in India
We've built this before.
Smart Dhandha
Our own back-office SaaS handles payroll, compliance and financial data for Indian businesses.
MSPAC
Secure handling of sensitive data in a data-visualisation application for a US state agency.
Custom LMS
Multi-dashboard platform with role-based access for students, tutors and admins.
Why teams choose Techcream for Private AI Stack.
Built for Indian businesses
We understand Indian SMEs' budgets, compliance needs and infrastructure realities.
Benchmark before you buy
We prove model quality on your data before you invest in hardware.
Small models, real results
Right-sized models keep hardware and running costs within an SME budget.
End-to-end ownership
From GPU sizing to the app your staff uses — one team, one point of contact.
Private AI Stack, explained.
A private LLM is a language model that runs on infrastructure you control — on-premise or in your own cloud account — so prompts and data are never sent to a third-party AI provider.
Not as a blanket rule. The DPDP Act and Rules focus on consent, purpose limitation, security safeguards and accountability. Running AI privately makes those obligations much easier to meet for sensitive data. Always confirm specifics with your legal advisor.
For most business tasks — summarisation, extraction, classification and RAG over your documents — modern open models perform well, especially when paired with good retrieval. We benchmark on your tasks to be sure.
It depends on model size and usage. Many SME workloads run on a single GPU server; we size it after a pilot so you don't overspend.
Let's scope your Private AI Stack project.
Tell us about the workflow or problem. We'll reply within 24 hours with initial thoughts and a time to talk — no sales pitch.
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