What you get
- AI responses grounded in your actual data with verifiable source citations
- 90%+ retrieval accuracy on representative query patterns
- A knowledge base that stays current as your documents and data change
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RAG Development
Your AI answers generic questions. Your customers ask specific ones.
Our RAG development services build retrieval-augmented generation systems - vector search, document processing pipelines, knowledge base construction, and accuracy optimization for AI that answers from your data, not its training set.
15+
RAG systems built
92%
Retrieval accuracy
8
Weeks to production
The Problem
What problem does this service solve?
Your AI features hallucinate because they rely on model training data instead of your organization's actual knowledge. You need AI responses grounded in your documents, databases, and institutional knowledge with verifiable accuracy.
One hallucinated answer in a regulated industry can cost more than the entire AI project. Even in low-stakes settings, users who catch your AI making things up stop trusting it permanently.
Overview
What is RAG Development?
The difference between an AI that makes things up and one your team trusts is retrieval. Our RAG development services build the plumbing that connects your AI to your actual knowledge - documents, databases, institutional memory - so every answer comes with a receipt.
RAG is the difference between an AI that makes things up and one that answers from your data with citations. But production RAG is harder than it looks - chunking strategy, embedding model selection, retrieval ranking, and answer synthesis all need to work together.
We build RAG systems as end-to-end pipelines: document ingestion, intelligent chunking, embedding generation, vector storage, retrieval with reranking, and answer generation with source attribution. Every component is optimized for your specific data types and accuracy requirements.
You get an AI system that answers accurately from your knowledge base, cites its sources, and admits when it does not know - instead of confidently generating wrong information.
Experience Signal
Deployed RAG systems processing millions of documents across healthcare, legal, fintech, and enterprise SaaS with 90%+ retrieval accuracy.
Fit
Is this service right for you?
Good fit
- SaaS companies building AI features that need to reference customer-specific or product-specific data
- Organizations with large document collections that users struggle to search effectively
- Legal, healthcare, and financial services teams that need cited, auditable AI responses
- Teams building knowledge assistants, search systems, or Q&A features powered by internal data
Not the right fit
- Use cases where general knowledge AI responses without data grounding are acceptable
- Teams without a meaningful document collection or knowledge base to build from
- Projects where simple keyword search already meets accuracy and usability needs
Process
How does RAG Development delivery work?
1
Phase 1· Week 1-2
Data Assessment and Retrieval Strategy
We audit your data sources, document types, and query patterns. We define the chunking strategy, embedding model, and retrieval architecture based on your accuracy requirements.
Deliverables
- Data source audit with document type classification
- Chunking and embedding strategy with model benchmarks
- Retrieval accuracy targets and evaluation methodology
2
Phase 2· Week 2-4
Pipeline Architecture and Ingestion
We build the document processing pipeline, implement the vector store, and validate retrieval quality on representative queries before building the generation layer.
Deliverables
- Document processing pipeline with intelligent chunking
- Vector store with embedding index and metadata
- Retrieval quality validation on test query set
3
Phase 3· Week 4-8
Generation Layer and Accuracy Optimization
We build the answer generation system with source attribution, implement reranking for retrieval precision, and optimize the full pipeline against accuracy benchmarks.
Deliverables
- Answer generation with source citations
- Reranking layer for retrieval precision
- Accuracy benchmarks on representative query set
4
Phase 4· Week 8-10
Production Deployment and Monitoring
We deploy to production, set up accuracy monitoring, and establish the pipeline for continuous knowledge base updates as your data changes.
Deliverables
- Production deployment with accuracy monitoring
- Knowledge base update pipeline for new documents
- Operational guide for accuracy maintenance
Outcomes
- AI responses grounded in your actual data with verifiable source citations
- 90%+ retrieval accuracy on representative query patterns
- A knowledge base that stays current as your documents and data change
Deliverables
- Document processing pipeline with format handling and intelligent chunking
- Vector search infrastructure with embedding index
- Retrieval pipeline with reranking and relevance scoring
- Answer generation layer with source attribution
- Accuracy monitoring dashboard and evaluation suite
Success Metrics
- Retrieval accuracy: relevant documents in top-k results
- Answer accuracy against ground truth evaluation set
- Response latency from query to cited answer
- Knowledge base coverage and freshness
Engagement models
8-10 week delivery for a production RAG system from data assessment through deployment and accuracy validation.
Best forTeams building their first RAG-powered feature or replacing a low-accuracy search system.
Core technology stack
P
PineconeW
WeaviateOpenAI
Anthropic
LangChain
Python
TypeScript
PostgreSQL
Use Cases
Common use cases for RAG Development
Legal Research Assistant
A legal services firm has 500K+ documents across case law, contracts, and regulatory filings. Associates spend 3-4 hours per research query manually searching and cross-referencing.
How we build it
We build a RAG system that indexes all document types, handles legal citation formats, retrieves relevant passages with jurisdictional context, and generates research summaries with precise source references.
Outcome
Research query time drops from 3-4 hours to 15 minutes. Associates review AI-generated summaries with source links instead of searching from scratch.
Product Knowledge Base for SaaS
A SaaS company's product docs, API references, and release notes are spread across multiple systems. Customers and support agents cannot find accurate answers quickly.
How we build it
We build a RAG-powered search and Q&A system that indexes all product content, understands version-specific questions, and returns answers with links to the exact documentation section.
Outcome
Support ticket deflection increases by 35%. Customer self-service resolution improves because answers reference the right product version.
Clinical Decision Support for Healthcare
A healthcare platform needs to surface relevant clinical guidelines, drug interactions, and treatment protocols based on patient context without hallucinating medical information.
How we build it
We build a RAG system with medical document processing, clinical terminology handling, and strict accuracy controls including confidence scoring and mandatory source citation for every response.
Outcome
Clinicians access relevant guidelines in seconds instead of manual lookup. Zero hallucinated medical recommendations due to strict grounding controls.
Frequently asked questions about RAG Development
Retrieval-augmented generation combines search with AI generation. Instead of relying on what the AI model learned during training, RAG retrieves relevant information from your actual data and uses it to generate accurate, cited responses. It is the most practical way to make AI answer from your knowledge base.
Related Services
Next Step
Tired of your AI making things up?
We build RAG systems that answer from your actual data with source citations - so your users get accurate responses they can verify, not confident hallucinations.