Which vector database should I use for my RAG agent?

It depends on your scale, budget, and requirements. Pinecone is excellent for managed, serverless deployments with minimal ops overhead. Weaviate offers built-in hybrid search and multi-tenancy. Qdrant provides high performance with flexible filtering. pgvector is ideal if you want to keep everything in PostgreSQL. We evaluate your specific needs and recommend the best fit.

What chunking strategy works best for my documents?

There is no one-size-fits-all strategy. For structured documents (legal contracts, technical docs), we use markdown-aware and section-based chunking that preserves hierarchy. For unstructured text, recursive character splitting with overlap works well. We often implement parent-child chunking where small chunks are retrieved but larger parent chunks are sent to the LLM for context. We test multiple strategies against your actual queries to find the optimal approach.

How do you handle document updates in the knowledge base?

We build automated ingestion pipelines that detect changes in your source systems (file storage, Confluence, databases) and trigger re-processing. Updated documents are re-chunked, re-embedded, and the old vectors are replaced in the index. For time-sensitive content, we support near-real-time updates. We also implement versioning so you can track what the agent knew at any point in time.

How do you measure RAG retrieval accuracy?

We use the RAGAS evaluation framework with four key metrics: faithfulness (does the answer match retrieved documents), answer relevance (does it address the question), context precision (are retrieved chunks relevant), and context recall (were the right chunks found). We create ground-truth QA evaluation datasets from your domain and run automated evaluations after every pipeline change.

Can the RAG agent handle multiple document formats?

Yes. We build ingestion pipelines that handle PDFs (including scanned documents with OCR), Word documents, PowerPoint slides, Excel spreadsheets, HTML pages, Markdown files, Notion exports, Confluence pages, emails, and even audio/video transcripts. Each format has specialized parsers that extract text, tables, images, and metadata for optimal chunking.

How much do vector embeddings cost in production?

Embedding costs depend on the model and volume. OpenAI's text-embedding-3-small costs about $0.02 per million tokens — meaning 100,000 document pages cost roughly $2 to embed. For high-volume use cases, we offer open-source embedding models (BGE, E5) that run on your infrastructure for zero per-token cost. The vector database hosting is typically the larger ongoing cost, ranging from free tier to $70+/month depending on scale.

What is the difference between RAG and fine-tuning?

RAG retrieves relevant information at query time and includes it in the LLM's context — ideal for factual knowledge that changes frequently. Fine-tuning modifies the model's weights to encode knowledge and behavior — better for style, tone, and domain-specific reasoning patterns. We often combine both: fine-tuning for the agent's personality and reasoning, and RAG for factual grounding. RAG is generally the right starting point for most knowledge-intensive applications.

How do you reduce hallucinations in RAG agents?

We implement multiple layers of hallucination prevention: high-quality retrieval with hybrid search and re-ranking ensures the right context is provided, constrained generation prompts instruct the LLM to only answer from retrieved documents, confidence scoring flags uncertain responses for human review, and citation validation cross-references answers against source documents. Our RAG agents typically achieve under 3% hallucination rates on production queries.

RAG Agent

RAG Agent Development Services

Build AI agents grounded in your data using Retrieval-Augmented Generation. We design RAG pipelines with vector databases, hybrid search, intelligent chunking, and citation attribution — so your agent answers from facts, not hallucinations.

Schedule a Call

INR 2000

Per Hour

50+

AI Agents Built

4.9/5

Client Rating

<2 Weeks

MVP Delivery

RAG Agent Capabilities

End-to-end RAG pipeline engineering for knowledge-grounded AI agents

Vector Database Setup

Expert deployment and optimization of Pinecone, Weaviate, Qdrant, ChromaDB, or pgvector based on your scale, latency, and cost requirements. We handle indexing, metadata filtering, and namespace management for production workloads.

Intelligent Document Chunking

Go beyond naive text splitting. We implement semantic chunking, recursive character splitting, markdown-aware parsing, and parent-child chunk strategies that preserve context and maximize retrieval accuracy for your specific document types.

Hybrid Search (Semantic + Keyword)

Combine dense vector search with sparse keyword matching (BM25) for the best of both worlds. Hybrid search catches exact terms that semantic search misses and understands meaning that keyword search cannot — delivering consistently higher recall.

Multi-Source Knowledge Ingestion

Ingest knowledge from PDFs, Word documents, Confluence pages, Notion databases, Google Drive, Slack messages, web pages, and databases. We build automated pipelines that keep your knowledge base current across all sources.

Citation & Source Attribution

Every answer includes citations linking back to the original source document, page number, and section. Users can verify claims instantly, building trust and enabling audit trails for compliance-critical applications.

Continuous Knowledge Updates

Automated pipelines that detect document changes, re-chunk updated content, refresh embeddings, and update vector indexes — ensuring your RAG agent always answers based on the latest information without manual intervention.

Our RAG Agent Development Process

A proven 5-step process from knowledge base audit to production RAG deployment

Discovery & Architecture

We audit your knowledge sources, analyze document types and volumes, select the optimal vector database, plan the chunking strategy, and design the retrieval pipeline architecture — including embedding model selection and search strategy.

Prompt Engineering & Prototyping

Build the initial RAG pipeline with your documents, test different chunking strategies, tune retrieval parameters (top-k, similarity threshold), and deliver a working prototype with citation support that you can test against real queries.

Integration & Development

Connect all knowledge sources, implement multi-source ingestion pipelines, build hybrid search, add metadata filtering, configure re-ranking models, and integrate the RAG agent into your application with full error handling.

Testing & Evaluation

Evaluate retrieval accuracy with ground-truth QA pairs, test faithfulness and relevance scoring, benchmark latency, measure hallucination rates, and run RAGAS evaluation framework across diverse query patterns to ensure production quality.

Deployment & Monitoring

Production deployment with retrieval dashboards, embedding cost tracking, index health monitoring, query analytics, and automated document update pipelines. 30-day post-launch support with continuous retrieval optimization.

RAG Agent Development Pricing

Transparent pricing for RAG pipeline and agent projects

Starter Agent

INR 50,000starting

Basic RAG agent with single knowledge source

Single vector database (Pinecone or Qdrant)
Up to 1,000 documents indexed
Semantic search with top-k retrieval
Basic citation support
Single channel deployment
Source code handover
14-day post-launch support

Get Started

What Our Clients Say

"Edesy built a RAG agent that searches our 25,000-page regulatory knowledge base and answers compliance questions with cited sources. Our legal team went from spending 2 hours per query to getting accurate, sourced answers in under 10 seconds. The hybrid search catches exact regulation numbers that pure semantic search missed."

Chief Compliance Officer

Compliance at Insurance Company

"Our customer support RAG agent searches across product docs, FAQs, and past tickets to give accurate answers. Hallucination rate dropped from 18% with a basic chatbot to under 2% with the RAG pipeline. The automatic knowledge update pipeline ensures the agent always reflects our latest product changes."

Director of Customer Experience

Customer Success at SaaS Platform (10K+ Users)

"We needed a research assistant that could search across 500,000 medical papers and provide cited answers. Edesy set up a Qdrant cluster with hybrid search and a re-ranking model. Retrieval precision hit 97% on our evaluation set, and researchers now find relevant papers in seconds instead of hours."

Head of Research

Research at BioTech Research Lab

RAG Agent

RAG Agent Development Services

Schedule a Call

INR 2000

Per Hour

50+

AI Agents Built

4.9/5

Client Rating

<2 Weeks

MVP Delivery

Trusted by businesses worldwide

ShopifyAmazonStripeSlackNotionVercel

RAG Agent Capabilities

End-to-end RAG pipeline engineering for knowledge-grounded AI agents

Vector Database Setup

Intelligent Document Chunking

Hybrid Search (Semantic + Keyword)

Multi-Source Knowledge Ingestion

Citation & Source Attribution

Continuous Knowledge Updates

5M+

Documents Indexed

96.3%

Retrieval Accuracy

85%

Hallucination Reduction

50+

Knowledge Sources Supported

Our RAG Agent Development Process

A proven 5-step process from knowledge base audit to production RAG deployment

Discovery & Architecture

Prompt Engineering & Prototyping

Integration & Development

Testing & Evaluation

Deployment & Monitoring

RAG Agent Development Pricing

Transparent pricing for RAG pipeline and agent projects

Starter Agent

INR 50,000starting

Basic RAG agent with single knowledge source

Single vector database (Pinecone or Qdrant)
Up to 1,000 documents indexed
Semantic search with top-k retrieval
Basic citation support
Single channel deployment
Source code handover
14-day post-launch support

Get Started

What Our Clients Say

Chief Compliance Officer

Compliance at Insurance Company

Director of Customer Experience

Customer Success at SaaS Platform (10K+ Users)

Head of Research

Research at BioTech Research Lab

Frequently Asked Questions

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Resources to help you evaluate and implement

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Get a free consultation and detailed project estimate. Our RAG specialists will help you design the optimal retrieval pipeline for your knowledge base and use case.

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