RAG as a Service: Chat With Your Documents
Ground your AI in your own data — accurately, and without retraining a model
Retrieval-Augmented Generation (RAG) lets an AI answer questions using your actual documents, policies, and databases — instead of guessing from general training data. Avinashi builds custom enterprise RAG systems so your team, or your customers, can ask questions in plain English and get answers grounded in your real content, with sources cited and hallucinations sharply reduced.
RAG vs. Fine-Tuning
| Capability | Fine-Tuning | RAG (Retrieval-Augmented Generation) |
|---|---|---|
| Update knowledge without retraining | No — requires a new training run | Yes — just update the document source |
| Cites sources for its answers | No | Yes |
| Cost to keep current | High (retraining cost + time) | Low (re-index documents) |
| Reduces hallucinations on your specific data | Partially | Directly — grounds answers in retrieved content |
| Best fit | Teaching a model a new skill or style | Answering questions from a large, changing knowledge base |
What We Build
Chat With Your Documents
Ask plain-English questions across contracts, policies, and internal knowledge bases.
Custom Enterprise RAG
Retrieval pipelines built for your data sources, not a generic off-the-shelf template.
Grounded, Cited Answers
Every answer can point back to the exact source document — no black box.
Enterprise AI Search
Search that understands meaning, not just keywords, across your knowledge base.
Frequently Asked Questions
What is RAG (Retrieval-Augmented Generation)?
RAG is an approach where an AI system retrieves relevant information from your own documents or database before generating an answer, so its responses are grounded in your actual content instead of relying only on what it learned during training.
How is RAG different from fine-tuning a model?
Fine-tuning retrains a model on new examples, which is costly and needs to be repeated as your data changes. RAG instead retrieves current information at answer-time, so you can update your knowledge base without retraining anything — and RAG answers can cite their sources, which fine-tuned models generally can't.
Can RAG reduce AI hallucinations?
Yes — by grounding every answer in retrieved content from your actual documents, RAG sharply reduces the chance of an AI confidently stating something that isn't true, and lets you show exactly which source an answer came from.
What does 'chat with your documents' actually mean?
It means your team or customers can ask questions in plain English — like 'what's our refund policy for enterprise customers?' — and get an accurate answer pulled directly from your real documents, instead of manually searching through files.
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