RAG Development Company in India

RAG Development That Answers From Your Data

A generic model does not know your policies, products, contracts or support history. RAG fixes that by retrieving the right passages from your own content at query time and grounding every answer in them.

We build the full pipeline: ingestion across PDFs, Word, spreadsheets, HTML and scanned documents; chunking and embedding; hybrid semantic and keyword search; citation rendering; and evaluation against real questions. The result is an assistant that answers accurately, shows its sources and improves as your knowledge base changes.

We also handle the unglamorous parts that decide whether RAG works in production: permissions, document freshness, retrieval quality, latency, cost and a feedback loop for bad answers.

Document ingestion across PDF, Word, HTML and scans
Chunking, embedding and metadata pipelines
Hybrid search (semantic + keyword)
Answer citations with source links
Permission-aware retrieval
Evaluation sets and retrieval quality dashboards

Why It Works

Answers you can verify

Every response cites the source document, so users can check the evidence.

Current without retraining

Update the knowledge base and the assistant uses the new content immediately.

Works with mixed formats

Normalise PDFs, office files, web pages and scans into one searchable index.

Measured retrieval quality

Build an evaluation set and track whether the right passages are being found.

How We Work

Content and access audit

Inventory the documents, formats, permissions and freshness rules that matter.

Pipeline build

Ingest, chunk, embed and index the content; add hybrid retrieval and citations.

Evaluate retrieval

Test against real questions, measure recall and fix the pipeline before launch.

Launch and monitor

Deploy with feedback capture, usage analytics and a refresh process for new content.

Tools & Platforms We Use

OpenAIAnthropic ClaudeCoherePineconeWeaviateFAISSpgvectorLangChainLlamaIndexPythonTypeScriptCloudflare Workers

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Frequently Asked Questions

How is RAG different from fine-tuning a model on our data?

Fine-tuning bakes knowledge into the model's weights, which is expensive and hard to update. RAG retrieves fresh information from your documents at query time, so updating your knowledge base updates the AI's answers immediately, with no retraining needed.

Can you show sources or citations for every answer?

Yes — grounding every answer in retrieved documents with citations is core to how we build RAG systems, so users, and you, can verify where an answer came from.

What if our documents are in multiple formats like PDF, Word, and spreadsheets?

We build ingestion pipelines that handle mixed formats — PDFs, Word docs, spreadsheets, even scanned documents via OCR — normalising them into a searchable index.

How long does a RAG system take to build?

A focused internal assistant typically takes 3-6 weeks; customer-facing deployments with permissions, evaluation and monitoring usually take 6-10 weeks.

Can you improve an existing RAG system?

Yes. We start with an evaluation set, measure retrieval and answer quality, then fix the pipeline rather than guessing.

Related Case Studies

Multi-Clinic Network Reduced Support Tickets by 70…E-Commerce Brand Resolved 80% Queries Automaticall…

RAG Development