What you get
- AI answers grounded in your real data with verifiable citations
- 90%+ retrieval accuracy on representative query patterns
- A knowledge base that stays current as your docs and data change
Build
RAG development
Your AI answers generic questions. Your customers ask specific ones.
15+
RAG systems built
92%
Retrieval accuracy
12
Weeks to launch
Trusted by teams at
The Problem
What problem does this service solve?
Your AI features hallucinate because they lean on training data instead of your real documents. You need answers grounded in your knowledge base with citations a user can verify.
One hallucinated answer in a regulated industry can cost more than the entire project. Even in low-stakes settings, users who catch your AI making things up stop trusting it for good.
Overview
What is RAG development?
The difference between an AI that invents answers and one your team trusts is retrieval. We build the plumbing that connects your AI to your real knowledge - documents, databases, institutional memory - so every answer comes with a receipt.
RAG is the difference between an AI that invents answers and one that pulls them from your data with citations. Production RAG is harder than it looks - chunking, embedding model choice, retrieval ranking, and answer synthesis all have to work together.
We build RAG systems end to end - document ingestion, intelligent chunking, embedding generation, vector storage, retrieval with reranking, and answer generation with source attribution. Every piece gets tuned to your real data types and accuracy targets.
You get an AI that answers accurately from your knowledge base, cites its sources, and admits when it doesn't know - instead of generating confident nonsense.
Experience Signal
Deployed RAG systems processing millions of documents across healthcare, legal, fintech, and enterprise SaaS with 90%+ retrieval accuracy. 12 weeks is our default, not our stretch goal.
What we build
RAG development services we deliver
Document processing pipelines
Ingestion and parsing for PDFs, Word docs, HTML, Markdown, Notion, Confluence, and structured databases with format-specific chunking strategies.
Vector search infrastructure
Production vector stores (Pinecone, Weaviate, pgvector) with embedding indexes, metadata filters, and hybrid search for the queries pure vector search misses.
Retrieval with reranking
Multi-stage retrieval with cross-encoder reranking that turns top-50 recall into top-5 precision - the single biggest accuracy lever in production RAG.
Answer generation with citations
Grounded response generation with inline source attribution, confidence scoring, and a clean fallback when retrieval comes up empty.
Knowledge base construction
Scratch-built knowledge bases from scattered docs, wikis, and databases - with auto re-indexing as content changes.
Hybrid RAG with structured data
RAG systems that combine document retrieval with live SQL queries for product catalogs, customer records, and analytics data.
Accuracy evaluation harnesses
Eval suites with representative queries and ground truth, plus live drift monitoring so quality regressions get caught the day they happen.
RAG performance tuning
Chunking strategy experiments, embedding model benchmarks, reranker tuning, and latency optimization against real production traffic.
Fit
Is this service right for you?
Good fit
- SaaS companies building AI features that reference customer-specific or product-specific data
- Orgs with large document collections that users can't search effectively
- Legal, healthcare, and financial teams that need cited, auditable AI responses
- Teams building knowledge assistants, search systems, or Q&A features over internal data
Not the right fit
- Use cases where general-knowledge AI responses are fine
- Teams without a meaningful document collection to ground answers in
- Projects where simple keyword search already hits accuracy and usability targets
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 pick the chunking strategy, embedding model, and retrieval architecture that fits your accuracy targets.
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, stand up the vector store, and validate retrieval quality on representative queries before touching the generation layer.
Deliverables
- Document processing pipeline with intelligent chunking
- Vector store with embedding index and metadata filters
- Retrieval quality validation on a real query set
3
Phase 3· Week 4-9
Generation layer and accuracy tuning
We build the answer generation system with source attribution, add a reranker for precision, and tune the full pipeline against accuracy benchmarks every week.
Deliverables
- Answer generation with source citations
- Reranking layer for retrieval precision
- Accuracy benchmarks on a representative query set
4
Phase 4· Week 9-12
Production deployment and monitoring
We deploy to production, wire up accuracy monitoring, and set up the continuous update pipeline so the knowledge base never goes stale.
Deliverables
- Production deployment with accuracy monitoring
- Knowledge base update pipeline for new documents
- Operational runbook for accuracy maintenance
Outcomes
- AI answers grounded in your real data with verifiable citations
- 90%+ retrieval accuracy on representative query patterns
- A knowledge base that stays current as your docs and data change
- A clean eval harness your team can reuse on the next RAG feature
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 a ground truth eval set
- Response latency from query to cited answer
- Knowledge base coverage and freshness
- Citation correctness - do the sources actually support the answer
Engagement models
12-week end-to-end delivery of one production RAG system from data assessment through deployment and accuracy validation.
Best forTeams building their first RAG feature or replacing a low-accuracy search system.
AI models we work with
GPT-5
OpenAI
Answer synthesis over retrieved passages with strong instruction following and citation formatting.
Claude Opus 4.6
Anthropic
Long-context answer generation when retrieved passages are big, messy, or span many documents.
Claude Sonnet 4.6
Anthropic
High-volume production RAG where cost per answer has to stay predictable.
text-embedding-3-large
OpenAI
The default embedding model for general-purpose retrieval over mixed document types.
Cohere Rerank 3
Cohere
Cross-encoder reranking that turns wide recall into precise top-5 results - the single biggest accuracy lift in production RAG.
Llama 3.3
Meta
Self-hosted RAG for healthcare, legal, and finance where documents can't leave your perimeter.
Use Cases
Common use cases for RAG development
Legal research assistant
A legal firm holds 500,000+ documents across case law, contracts, and regulatory filings. Associates burn 3-4 hours per research query cross-referencing by hand.
How we build it
We build a RAG system that indexes every document type, handles legal citation formats, retrieves 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 summaries with source links instead of searching from scratch.
Product knowledge base for SaaS
A SaaS company's docs, API references, and release notes are scattered across multiple systems. Customers and support agents can't find accurate answers quickly.
How we build it
We build a RAG-powered search and Q&A system that indexes all product content, handles version-specific questions, and returns answers with links to the exact doc section.
Outcome
Support ticket deflection climbs 35%. Self-service resolution improves because answers cite the right product version.
Clinical decision support
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 - confidence scoring and mandatory source citation on every response.
Outcome
Clinicians access relevant guidelines in seconds. Zero hallucinated medical recommendations thanks to strict grounding controls.
What clients say
Real feedback from real teams
I spent years at Amazon fighting static surveys. RaftLabs built a working prototype in four days that already outperformed every survey tool I'd used. Twelve weeks later we had a full SaaS that product teams actually want to use.
Founder
Ex-Amazon PM - Perceptional
We went from text surveys that nobody finished to AI phone interviews that people actually enjoy. The voice agents handle the whole conversation, and the analytics tell us what we need to know without reading a single transcript.
Cherian Koshy
Behavioral Strategist - USA Today Bestselling Author
Proof
Recent rag development work
BuildRAG-powered survey chatbot for Perceptional
4x
Deeper insights
12 weeks
Concept to launch
“Working prototype in four days. Full SaaS in twelve weeks.”
Read case studyBuildVoice AI with grounded answers for Cherian Koshy
12 weeks
To production
Global
Call reach
“Text surveys nobody finished became phone interviews people enjoy.”
Read case studyIndustries
RAG development for your industry
Healthcare
Clinical guideline search, patient intake, and regulated document retrieval with the guardrails the industry demands.
ExploreFintech
Policy lookup, compliance Q&A, and customer support RAG systems built to pass audit.
ExploreInsurance
Claims guideline retrieval, underwriting reference, and policy Q&A systems grounded in your real documents.
ExploreSaaS
Product documentation, API reference, and release-note RAG for support and in-product help.
ExploreEdTech
Curriculum-grounded learner assistants and instructor research tools that never make up a citation.
ExploreRetail
Product catalog Q&A, store policy lookup, and hybrid RAG over structured inventory data.
ExploreFrequently asked questions about RAG development
Retrieval-augmented generation combines search with AI generation. Instead of relying on what a model learned in training, RAG retrieves relevant info from your actual data and uses it to generate accurate, cited answers. It's 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 answers they can verify, not confident hallucinations.