Question 1

What is RAG and why does it matter?

Accepted Answer

Retrieval-Augmented Generation — instead of letting the LLM guess, we first retrieve relevant chunks from your verified docs, then generate the answer based on those. Result: zero hallucinations, full source citations.

Question 2

How big can my dataset be?

Accepted Answer

We've shipped systems with 200K+ documents totaling 50M+ tokens. There is no practical limit — pricing scales with corpus size.

Question 3

Can it handle scanned PDFs and handwritten docs?

Accepted Answer

Yes. We use Azure Document Intelligence + Tesseract OCR for scanned/handwritten content before embedding.

Question 4

How do you guarantee 90%+ accuracy?

Accepted Answer

We build an eval set of 100–300 real questions with you, then tune embeddings + reranker + prompts until the system hits 90% on that set. If it can't, we iterate at no extra cost.

Question 5

Can the system stay updated as my docs change?

Accepted Answer

Yes. We set up scheduled re-indexing (daily/weekly) or webhook-triggered updates from Notion, Drive, Confluence, etc.

Question 6

Is my data safe?

Accepted Answer

Vectors stored in your private Pinecone/Qdrant cluster (your account). LLM calls can be routed through Azure OpenAI or AWS Bedrock for enterprise compliance. Nothing is shared with model providers for training.

Question 7

Pricing?

Accepted Answer

One-time build (₹50K–₹3L based on complexity) + monthly hosting (₹5K–₹50K based on volume). Exact quote in the strategy call.

Question 8

Can it answer in multiple languages?

Accepted Answer

Yes. Query in Hindi, get an answer from English source docs (or vice versa). Tested across 12+ languages.

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