Private LLM Deployment
Run powerful open models on your own infrastructure.
- Llama, Mistral, Qwen, Gemma and Indic models
- GPU sizing and inference optimisation
- vLLM and TensorRT-LLM serving
- Air-gapped installations
Generative AI · Private LLMs · Fine-Tuning
Generative AI Development With Private LLMs You Fully Control
We design and deploy generative AI applications on private, fine-tuned and open-source large language models on your cloud, on-premise or air-gapped so sensitive data never leaves your control.
- Private & On-Prem LLMs
- LLM Fine-Tuning
- Indic-Language Models
- GenAI Apps & Copilots
✕
Generative AI Without Giving Away Your Data
Generative AI can draft documents, summarise reports, answer questions and write code but many organisations cannot send confidential data to public AI services. Regulated industries, PSUs and government bodies also face data-residency rules and growing interest in sovereign, India-hosted AI.
AI Technocrats offers generative AI development services built around private large language models. We select the right open-source or commercial model, adapt it to your domain with RAG and fine-tuning, wrap it with guardrails, and deploy it on infrastructure you control from a single GPU server to a Kubernetes cluster.
The result is generative AI that understands your terminology, respects your access controls, supports English and Indian languages, and keeps costs predictable.
Our Capabilities
Generative AI & LLM Services
Everything needed to take GenAI from idea to secure production.
LLM Fine-Tuning
Teach models your vocabulary, formats and style.
- Supervised fine-tuning and LoRA/QLoRA
- Instruction and preference tuning
- Synthetic training-data generation
- Before/after evaluation reports
Enterprise Knowledge Assistants
Answer questions from your documents with citations.
- RAG over policies, manuals and records
- Role-based access to content
- Multilingual Q&A
- Source-cited answers
GenAI Content & Report Generation
Automate drafting at scale.
- Proposal, report and letter drafting
- Meeting and document summarisation
- Translation into Indian languages
- Template-driven document generation
Small Language Models
Fast, low-cost models for focused tasks.
- Distillation of large models
- Classification and extraction SLMs
- Edge and CPU deployment
- Cost-per-query optimisation
GenAI Governance & Evaluation
Ship GenAI you can trust.
- Hallucination and toxicity testing
- Prompt-injection defences
- Usage and cost monitoring
- Model cards and audit trails
Why Private and Sovereign LLMs Are Trending
Early generative AI projects relied on public APIs. As adoption grows, enterprises want more control: over where data is processed, which model version runs, how much each query costs and how the model behaves. Open-weight models have closed much of the quality gap, making private deployment practical.
India's push for sovereign AI including indigenous multilingual models released under the IndiaAI Mission adds another reason. Public-sector bodies and regulated companies increasingly prefer models that can be hosted in India and that understand Indian languages.
Our generative AI development services help you choose wisely between public APIs, private open models and hybrids. We benchmark candidate models on your own tasks, then build the architecture that balances accuracy, latency, cost and compliance.
Choosing the right model is a trade-off between quality, speed, cost and control. A 7–14 billion-parameter model fine-tuned for one task can outperform a much larger general model on that task while running on a single GPU. Larger reasoning models remain valuable for complex analysis. Our benchmarks on your real data show exactly where each option fits, so you invest only where it pays off.
Which model fits?
Illustrative guidance
Public AI APIFrontier model via APIBest fit
Private 7–14B fine-tuned modelOne GPU, tuned to one taskBest fit
Hybrid: private RAG + reasoning modelSensitive data stays localBest fit
Indic open model, on-premMultilingual, India-hostedBest fit
Best fit: Private 7–14B fine-tuned model sensitive data stays inside your network.
Public AI APIs vs Private LLMs: Which Is Right for You?
A side-by-side view of what changes.
| Factor | Public AI API | Private / on-premise LLM |
|---|---|---|
| Data location | Processed by the provider | Stays in your environment |
| Cost model | Per-token, grows with usage | Mostly fixed infrastructure cost |
| Customisation | Prompting, limited tuning | Full fine-tuning and control |
| Model changes | Provider can update or retire models | You choose when to upgrade |
| Best for | Fast prototypes, low-sensitivity data | Regulated, high-volume or sovereign use cases |
Benefits of Private Generative AI
What enterprises gain by owning their GenAI stack.
Data privacy
Prompts and documents never leave your controlled environment.
Domain accuracy
Fine-tuned models speak your industry's language.
Cost control
Fixed infrastructure cost instead of unpredictable per-token bills.
Vendor independence
Swap or upgrade models without rebuilding applications.
Regulatory alignment
Supports data-residency and audit requirements.
Multilingual reach
Serve users in Hindi and other Indian languages.
READY TO GET STARTED ?
Custom AI solutions designed to empower your business growth.
How It Works
From Model Selection to Secure Production
A disciplined path that turns a foundation model into a trusted enterprise asset.
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01
Select
Benchmark candidate models on your real tasks.
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02
Adapt
Fine-tune and add RAG for your domain knowledge.
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03
Guard
Add guardrails, filters and access controls.
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04
Deploy
Serve efficiently on your GPUs or private cloud.
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05
Monitor
Track quality, cost and drift continuously.
Generative AI Use Cases by Industry
GenAI applications we tailor to each sector's data and rules.
Government & PSUs
- Policy and circular assistants
- File noting and summary drafting
- Citizen-service chat in Indian languages
- Tender-document analysis
BFSI
- Credit-memo drafting
- Regulatory research copilots
- Customer-communication generation
- Fraud-case summarisation
Healthcare
- Clinical-note summarisation
- Discharge-summary drafting
- Medical-literature Q&A
- Patient-education content
Manufacturing
- SOP and manual copilots
- Quality-report generation
- Maintenance troubleshooting
- Supplier-document analysis
Legal & Professional Services
- Contract drafting and review
- Case-law research
- Proposal generation
- Knowledge management
Education
- Tutoring assistants
- Question-paper generation
- Content translation
- Student-query bots
Our Generative AI Development Process
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Use-Case & Risk Workshop
We prioritise GenAI opportunities and identify data and compliance constraints.
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Model Benchmarking
We test several LLMs on your tasks for accuracy, latency and cost.
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Data Preparation
We clean, structure and label data for RAG and fine-tuning.
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Build & Fine-Tune
We develop the application, tune the model and add guardrails.
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Deploy & Harden
We deploy on your infrastructure with security testing and access control.
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Operate & Improve
We monitor quality and cost, and refresh models as better ones appear.
Generative AI Technology Stack
- Llama
- Mistral
- Qwen
- Gemma
- DeepSeek
- Indic LLMs
Open Models
- ChatGPT
- Anthropic Claude
- Google Gemini
- Azure OpenAI
Commercial Models
- Hugging Face
- PEFT / LoRA
- Unsloth
- Axolotl
- DPO
Fine-Tuning
- vLLM
- TensorRT-LLM
- Ollama
- Triton
- llama.cpp
Serving
- LangChain
- LangGraph
- LlamaIndex
- DSPy
- Haystack
Frameworks
- NVIDIA GPUs
- Kubernetes
- AWS
- Azure
- GCP
- On-premise
Infrastructure
Trending Now
Generative AI Trends to Watch
The shifts shaping how organisations invest in this area.
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Sovereign and Indic LLMs
India-built multilingual models are giving public and regulated sectors local, compliant options.
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Small language models
Compact models fine-tuned for one task deliver speed and low cost on modest hardware.
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Reasoning models
Models that plan and check their own work are improving accuracy on complex tasks.
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LLMOps maturity
Evaluation, monitoring and cost controls are becoming standard parts of GenAI delivery.
Why Choose AI Technocrats for Generative AI
Private-by-default
Experience deploying LLMs inside PSU and enterprise networks.
Model-agnostic advice
We recommend what fits you, not one vendor.
Indian-language expertise
Hindi and regional-language GenAI built and tested.
Full lifecycle
From benchmarking to fine-tuning, deployment and LLMOps.
FAQ
Generative AI Development: Frequently Asked Questions
Can't find what you're looking for? Talk to our AI specialists.
What are generative AI development services?
Generative AI development services cover designing, building and deploying applications powered by large language models and other generative models. This includes model selection, RAG, fine-tuning, guardrails, integration and ongoing operations.
What is a private LLM?
A private LLM is a large language model hosted in an environment you control, such as your own servers or private cloud. Prompts and data are processed there instead of on a third-party public service.
Should we fine-tune a model or use RAG?
Use RAG when the model needs access to your changing knowledge. Use fine-tuning when you need a specific tone, format or domain vocabulary. Many solutions combine both; we benchmark to decide.
Can generative AI work in Hindi and other Indian languages?
Yes. We use multilingual and Indic-language models and evaluate them on your content in each target language before deployment.
What hardware do we need for an on-premise LLM?
It depends on model size and users. Smaller models can run on a single modern GPU, while larger models need multi-GPU servers. We size hardware based on your expected load.
How do you keep generative AI outputs accurate and safe?
We ground answers in your data, add output validation and content filters, test for hallucinations and prompt injection, and monitor production usage continuously.
How long does a generative AI project take?
A proof of concept typically takes 3 to 6 weeks. A secure production deployment with fine-tuning, integrations and monitoring usually takes 8 to 16 weeks.
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