Home›AI Solutions›RAG & Fine-tuning
⚙️

RAG & Fine-tuning

Give LLMs Your Business Knowledge. Get Expert-Level Answers.

Retrieval-Augmented Generation and fine-tuning services that transform generic LLMs into domain-expert AI systems trained on your proprietary data — with accuracy, privacy, and full control.

95%Retrieval Accuracy
30+RAG Systems Built
70%Support Ticket Reduction
100%Data Privacy Guaranteed

Use Cases & Applications

📚

Knowledge Base AI

AI that answers questions from your internal docs, SOPs, and knowledge bases.

📄

Document Q&A

Upload PDFs, contracts, reports — get instant, cited answers from any document.

🏥

Domain Expert AI

Medical, legal, financial, and technical AI tuned on specialised domain data.

🔒

Private GPT

Fully on-premise or VPC-deployed LLM — no data leaves your infrastructure.

🎧

Customer Support AI

Support bot trained on your product documentation, FAQs, and support history.

🔄

Workflow Automation

AI agents that read, classify, and route documents and emails automatically.

Our Delivery Process

1

Data Audit

Identify, clean, and chunk your knowledge sources for vector storage.

2

RAG Architecture

Design retrieval pipeline, embedding strategy, and relevance ranking.

3

LLM Selection

Choose base model (GPT-4o, Llama 3, Mistral) based on cost, privacy, and accuracy needs.

4

Fine-tuning (if needed)

Supervised fine-tuning on domain data for tasks where RAG alone is insufficient.

5

Evaluation & Deploy

Ragas/BLEU evaluation, hallucination testing, and production deployment.

Tools & Technologies We Use

LangChainLlamaIndexOpenAI APILlama 3MistralPineconeWeaviateChromaFAISSAWS Bedrock

Frequently Asked Questions

❓ When should I use RAG vs fine-tuning?

RAG is best for grounding answers in up-to-date documents. Fine-tuning is better for teaching a model a specific style, format, or specialised reasoning pattern. Most production systems combine both.

❓ Is my data safe?

Yes. We can deploy entirely on your own cloud (AWS, GCP, Azure) or on-premise. No proprietary data is sent to third-party AI providers unless you choose cloud APIs.

❓ What accuracy can we expect?

Well-implemented RAG systems typically achieve 85-95% answer relevance on domain-specific tasks. We benchmark every deployment against your acceptance criteria.

Ready to Build RAG & Fine-tuning?

Get a free consultation and detailed proposal within 24 hours — no commitment required.

Start Your AI Project →View All AI Solutions
Ask anything to Radhika, AI Assistant