For organizations that need data sovereignty, cost control, or IP protection, we deploy and enrich open-source models in your own environment — giving you the capability of commercial AI without sending sensitive data to a third party.
What’s Included
- Open-source LLM evaluation and selection (Llama, Mistral, Qwen, DeepSeek, etc.)
- On-prem/VPC-hosted model deployment for data-sensitive environments
- Fine-tuning and domain enrichment using proprietary/internal data
- Retrieval and knowledge-base enrichment (vector DB setup, embeddings strategy)
- Model quantization/optimization for cost-efficient inference
- Guardrail and safety-layer implementation for self-hosted models
Meruksha’s Differentiators
- Security-first hosting architecture — informed by direct OT/ICS and industrial security experience, where isolation and data control are non-negotiable
- We treat model enrichment as a data governance exercise, not just a technical one — proprietary data never leaves client control
- Particularly suited to life sciences clients where IP protection and data residency requirements rule out public API-based models
See it in action
Illustrative engagement (Industrial Manufacturing): Read the case study