Most platforms have basic RAG built in. Tuning it for accurate retrieval is the hard part. We do document ingestion, embedding selection, retrieval tuning, citation tracking, and multilingual support — so your voice agent answers questions reliably.
From ₹40,000
Flat one-time price
1–2 weeks
Standard delivery
1000+ docs
Advanced tier capacity
Multilingual
Hindi + regional
Two packages depending on document volume and retrieval complexity. 40% deposit, 60% on delivery.
Production RAG isn't just upload-and-go. Every piece needs tuning for your specific corpus.
PDFs, HTML pages, Notion exports, Confluence pages, custom databases. We handle parsing, OCR for scanned PDFs, table extraction, metadata preservation.
Default chunk size rarely works. We test 256/512/1024 token chunks against your real queries to find what gives best retrieval recall + precision.
OpenAI ada-002, Cohere multilingual v3, Voyage AI, BGE — different embeddings work better for different corpora. We benchmark + choose.
Pinecone, Weaviate, Qdrant, pgvector, or your voice agent platform's native store. We deploy + configure for your scale.
Vector search alone misses keyword-exact queries. We combine vector + BM25 + metadata filtering for higher recall on production queries.
Top-10 vector results re-ranked by Cohere Rerank or Voyage Rerank. Pushes top-3 precision from 60% to 85%+ typically.
Voice agent says 'According to your refund policy...' and the source document is logged for compliance + transparency.
Documents in English, knowledge accessed in Hindi/regional via multilingual embeddings. Critical for Indian customer bases.
Retrieval hit rate, hallucination flags, low-confidence queries logged for review. Dashboard for ongoing tuning.
Most teams build basic RAG in a week, then discover problems in production:
Voice agent gives confidently wrong answers (hallucinations from irrelevant chunks)
Customer asks in Hindi, knowledge is in English — generic embeddings fail
Tables and structured data lost during ingestion (PDF tables → meaningless text)
Top-3 retrieval returns the same document section 3 times (diversity problem)
Knowledge base updated, but agent still cites old answers (stale embeddings)
Citation says 'document.pdf page 42' but customer can't access the source
Long-form documents chunked mid-sentence, breaking context
Customer support FAQ bots (refund policies, shipping, warranty)
Healthcare patient education (procedure prep, medication info)
EdTech course inquiries (curriculum, fees, scholarships)
BFSI policy info (loan eligibility, KYC requirements, terms)
Real estate project details (amenities, pricing, RERA info)
E-commerce product info (specs, compatibility, returns)
Internal IT helpdesk (password resets, software access, policies)
Predictable 5-step process from kickoff to deployment
Discovery call
30-min call: document sources, languages, use case, expected query volume. We get sample documents + query examples.
Fixed-price quote
Scope document with exact deliverables, document count, retrieval architecture, and price. Two days to deliver.
40% deposit, build begins
Within 2 business days. We get access to source documents and your voice agent platform.
Ingest + tune + benchmark
Documents chunked + embedded, vector store deployed, retrieval tuned against benchmark queries, voice agent wired to RAG endpoint.
Deploy + handoff
Production deployed, monitoring set up, final demo + documentation, 60% balance, maintenance retainer starts.
Need the voice agent itself? We build production agents on every major platform.
Already have an agent? Start with an audit before adding RAG.
Sync voice agent + knowledge base events to your CRM.
We run RAG-powered agents end-to-end as a managed service.
Send sourced answers via WhatsApp after the call.
Our own platform — native knowledge base support included.
Book a 30-min scoping call. We'll send a fixed-price quote within one business day.