When should we use RAG vs fine-tuning?

RAG is best when your knowledge changes frequently, you need source citations, or you have a large document corpus. Fine-tuning is better for style/tone consistency or when you need the model to learn a specific reasoning pattern. Most enterprise use cases benefit from RAG first.

How do you measure RAG accuracy?

We use automated evaluation pipelines that measure retrieval relevance (are the right chunks being fetched?), answer faithfulness (is the answer grounded in retrieved context?), and answer correctness (does it match expected output?). We target 90%+ on all three metrics.

What document types do you support?

PDFs, Word docs, HTML pages, Markdown, Confluence/Notion exports, Slack archives, code repositories, and structured data (CSV, JSON). We build custom parsers for proprietary formats.

What does a RAG system cost to run monthly?

Typical production costs: $200-800/month for vector database hosting, $500-2000/month for LLM API calls (depends on volume), plus standard cloud infrastructure. We optimize for cost from day one — chunking strategy and retrieval quality directly impact LLM spend.

Can the RAG system run on our private infrastructure?

Yes. We deploy on-premise or in your VPC with open-source LLMs (Llama, Mistral) and self-hosted vector databases (Qdrant, Milvus). No data leaves your network.

When is RAG NOT the right solution?

RAG struggles with complex multi-hop reasoning across many documents, real-time data that changes every second, or when the answer requires deep mathematical computation. We'll tell you honestly if a different approach (knowledge graphs, fine-tuning, or traditional search) is better.

RAG Engineering — Bengaluru

We Build Production RAG Pipelines — Not Chatbot Demos That Hallucinate

From document ingestion to citation-aware answers. Vector + hybrid retrieval, reranking, guardrails, and evaluation pipelines. 20+ RAG systems in production.

Get a RAG Architecture Plan

✓ Citation-grounded✓ NDA-ready✓ Evaluation pipelines included

RAG application development — retrieval augmented generation pipeline architecture

RAG Development — BengaluruGet RAG Architecture Plan

Enterprise and Startup Teams Across Bengaluru

Why Our RAG Approach

Three Pillars of Production RAG

Retrieval Quality, Not Just Embeddings

Embeddings are table stakes. We combine vector search with BM25, cross-encoder reranking, and query expansion to get the right chunks — not just similar ones.

Citation-Grounded Answers

Every answer includes source references your users can verify. No black-box responses. Confidence scores flag when the system isn't sure.

Evaluation-Driven Development

We build evaluation pipelines from day one — not as an afterthought. Retrieval relevance, answer faithfulness, and hallucination detection run in CI.

What We Build

Real RAG Systems Running in Production

Enterprise Knowledge Base

10K+ internal documents indexed with hybrid retrieval. 92% answer accuracy with citation links. Replaced a legacy search system that returned irrelevant results 40% of the time.

Vector SearchHybrid RetrievalClaude

Legal Research Assistant

Case law retrieval across 50K documents. Semantic search with BM25 reranking. Lawyers find relevant precedents in seconds instead of hours.

Legal NLPRerankingCitation Engine

Customer Support RAG

Answers from product docs, knowledge base articles, and past tickets. Reduces ticket volume by 60%. Escalates gracefully when confidence is low.

Multi-Source RAGConfidence ScoringEscalation

"Cartoon Mango was great to work with. They improvise and provide 24X7 support."

— Gaurav Saxena, Media Manager, BCCI

Architecture

Our RAG Stack

Layer 1

Ingestion

Smart chunking strategies (semantic, recursive, parent-child). Metadata extraction for filtering. Support for PDF, DOCX, HTML, Markdown, Confluence, and custom formats.

Layer 2

Retrieval

Vector search (OpenAI, Cohere embeddings) + BM25 hybrid retrieval. Cross-encoder reranking for precision. Query expansion and HyDE for recall improvement.

Layer 3

Generation

Claude/GPT with citation-grounded prompts. Guardrails for hallucination prevention. Structured output with source references and confidence scores.

Layer 4

Evaluation

Automated relevance scoring, faithfulness checks, and hallucination detection. Continuous monitoring with human-in-the-loop feedback. Regression testing in CI.

20+

RAG Systems

92%

Answer Accuracy

across production deployments

60%

Fewer Support Tickets

with RAG-powered self-service

<3s

Response Time

end-to-end retrieval + generation

Our Process

From Corpus Audit to Production in 8 Weeks

Week 1-2

Corpus Audit & Design

Analyze your document corpus, define chunking strategy, design retrieval architecture. Build evaluation dataset with your team.

→ RAG Architecture Plan

Week 3-5

Pipeline Development

Build ingestion pipeline, vector store, retrieval chain, and generation layer. Weekly accuracy demos with your evaluation dataset.

→ Working RAG Pipeline

Week 6-7

Optimization & Integration

Tune retrieval quality, add guardrails, integrate with your existing systems. Load testing and edge case handling.

→ Production-Ready System

Week 8

Deploy & Monitor

Production deployment with monitoring dashboards, alerting, and evaluation pipelines. 30-day support included.

→ Live Deployment

Investment

Transparent Pricing

Most agencies hide pricing. We don't. Exact costs depend on corpus size and retrieval complexity — we provide a detailed estimate after the architecture audit.

PoC / Pilot

₹2-5L3-5 weeks

Single-source RAG pipeline with evaluation. Prove accuracy on your corpus before committing to production build.

Production System

₹8-18L8-12 weeks

Multi-source RAG with hybrid retrieval, reranking, guardrails, evaluation pipelines, and production deployment.

Enterprise

On RequestScoped per engagement

Multi-tenant RAG platform with on-premise deployment, custom security, team training, and long-term support.

Why Us

Built for RAG That Actually Works

Retrieval tuning expertise

We've tuned retrieval for 20+ production RAG systems. We know the difference between "demo accurate" and "production accurate."

Evaluation pipelines from day one

Every RAG system we build ships with automated evaluation — retrieval relevance, answer faithfulness, and hallucination detection in CI.

Honest about RAG limitations

RAG isn't magic. We'll tell you upfront if your use case needs a knowledge graph, fine-tuning, or traditional search instead.

FAQ

Common Questions

RAG is best when your knowledge changes frequently, you need source citations, or you have a large document corpus. Fine-tuning is better for style/tone consistency or when you need the model to learn a specific reasoning pattern. Most enterprise use cases benefit from RAG first.

We Have Delivered 100+ Digital Products

Sports and Gaming

IPL Fantasy League

Innovation and Development Partners for BCCI's official Fantasy Gaming Platform

Banking and Fintech

Kotak Mahindra Bank

Designing a seamless user experience for Kotak 811 digital savings account

News and Media

News Laundry

Reader-Supported Independent News and Media Organisation

Client Testimonials

What Our Partners Say