Frequently Asked Questions
Everything you need to know about partnering with Sanjeevi Technology Solutions for your enterprise AI initiatives.
How does Sanjeevi Technology Solutions ensure data security when using Large Language Models (LLMs)?
We prioritize data sovereignty by deploying private, locally-hosted open-source models (like Llama 3 or Mistral) directly within your secure VPC. If proprietary models are used, we implement strict zero-retention API agreements and PII redaction layers to guarantee your data is never used for training.
What is a Retrieval-Augmented Generation (RAG) pipeline and why do I need it?
A RAG pipeline allows a general AI model to securely search your private company documents to formulate answers. Instead of retraining the model on your data (which is expensive and slow), RAG pulls the exact relevant context in real-time, drastically reducing hallucinations and ensuring up-to-date accuracy.
Can you fine-tune open-source AI models for our specific industry?
Yes. We specialize in parameter-efficient fine-tuning (PEFT) and LoRA techniques to adapt base models to your specific domain vocabulary, style, and logic, providing better performance than off-the-shelf solutions at a fraction of the inference cost.
How long does it take to deploy a custom AI agent?
Development timelines vary based on complexity. A standard RAG-based knowledge assistant can be deployed to staging within 4-6 weeks. Highly complex autonomous agents requiring multi-tool integration and custom fine-tuning typically take 8-12 weeks.
Do you provide ongoing inference hosting and maintenance?
Absolutely. We provide end-to-end MLOps services, managing GPU clusters, monitoring model drift, scaling inference endpoints, and ensuring 99.99% uptime for your production AI systems.