Grounded, Cited Answers
RAG eliminates hallucinations by anchoring every response in your actual data — users get verifiable answers, not guesses.
Custom RAG Development Services
Custom RAG Development Services.
Production RAG systems that ground LLM responses in your proprietary data — accurate, cited answers from your knowledge base without fine-tuning.
- 100+
- RAG Systems Built
- 99.2%
- Retrieval Accuracy
- 10x
- Faster Retrieval
- 60%
- Cost Reduction
- NDA signed on day one
- Fixed-price quote within 48 hours
- Senior engineers only, in your timezone
- You own the code and IP
Get your custom project plan
Share your project details — a senior engineer responds within 4 hours.
NDA protected 24hr response Free consultation
Independently audited, certified and built to standards you can check
Custom RAG development services build systems that answer from your own documents rather than a model's training data. The system retrieves relevant passages at query time, generates a grounded answer and cites the source. It is the standard approach when answers must be verifiable, when information changes frequently, or when users are permitted to see different documents.
Clutch Top AI Company 2026LangChain Certified PartnerAWS ML CompetencySOC 2 Type II CertifiedISO 27001 CertifiedPinecone PartnerTop AI Development - GoodFirmsWeaviate Integration Partner
Why Enterprises Choose RAG Over Fine-Tuning
Update Without Retraining
Add new documents and the knowledge base updates instantly. No expensive retraining cycles or model drift.
Data Stays Yours
Run entirely on-premise or in your private cloud. RBAC controls which users see which documents.
Who Needs Custom RAG Development?
Enterprise Knowledge Management
Turn internal wikis, SOPs and document libraries into an intelligent search system for employees.
Customer Support Teams
AI agents grounded in product docs and FAQs with cited sources and escalation paths.
Legal & Compliance
Search and analyze contracts, filings and regulatory documents with source attribution.
Healthcare & Research
Medical literature search and clinical protocol retrieval with verifiable citations.
RAG Performance Metrics
99.2%
Retrieval Accuracy
Semantic search precision
100+
RAG Systems
Deployed to production
< 200ms
Query Latency
End-to-end response
60%
Cost Savings
vs fine-tuning approach
10x
Faster Search
vs keyword search
95%+
Citation Accuracy
Source attribution
RAG is the most practical way to make LLMs useful for your business. Codazz builds production systems with hybrid search, reranking pipelines and evaluation frameworks — sub-200ms responses at 99%+ retrieval accuracy.
What We Build
RAG Development Services Grounded AI at scale.
Production-grade retrieval-augmented generation systems for enterprise knowledge management, customer support, document analysis, and intelligent search.
Knowledge Base RAG
Transform your internal documents, wikis, and databases into an intelligent knowledge base that answers questions with cited sources.
Document IngestionSemantic SearchCitationsAccess Control
Customer Support RAG
AI support agents grounded in your product documentation, FAQs, and ticketing history. Accurate answers with human escalation paths.
Help Desk AIFAQ BotTicket AnalysisEscalation
Document Q&A
Chat with your documents — PDFs, contracts, research papers, financial reports. Ask questions in natural language and get precise, cited answers.
PDF AnalysisContract ReviewResearch PapersMulti-Doc
Hybrid Search
Combine semantic vector search with keyword BM25 search for superior retrieval accuracy. Re-ranking, filtering, and multi-index strategies.
Vector + BM25Re-RankingMulti-IndexFiltering
Agentic RAG
RAG systems that reason over multiple retrieval steps, query multiple data sources, and synthesize complex answers from diverse knowledge bases.
Multi-Step ReasoningMulti-SourceQuery PlanningSynthesis
RAG Evaluation & Optimization
Comprehensive RAG evaluation pipelines — retrieval accuracy, answer relevance, hallucination detection, and continuous quality monitoring.
RagasDeepEvalA/B TestingQuality Metrics
Why Codazz RAG
RAG Systems That Actually Work.
Every rag development engagement is scoped, priced and staffed the same way — so these hold on every project, not just the showcase ones.
99%+ Retrieval Accuracy
Hybrid search, smart chunking and reranking deliver precision across millions of documents.
Sub-200ms Responses
Optimized vector databases, caching and streaming for fast answers on large knowledge bases.
Source Citations
Every answer includes source documents and relevance scores so users can verify AI responses.
Trusted by teams building with
OpenAIAnthropicPineconeWeaviateQdrantLangChainLlamaIndexAWSGoogle CloudAzureMongoDBPostgreSQLRedisElasticsearchCohereHugging Face
By the numbers
RAG Development Results That Speak for Themselves.
100+RAG SystemsIn production
99.2%AccuracyRetrieval precision
< 200msLatencyEnd-to-end response
60%Cost Savingsvs fine-tuning
4.9★Client RatingAcross 60+ reviews
How we deliver rag development projects
One process, five stages, fixed milestones. You always know what is happening and what it costs.
- 01
Discovery
1–2 weeksWe map the business problem, the users and the constraints, then agree what success looks like in numbers.
Scope documentFixed-price quote - 02
Design & architecture
2–4 weeksFlows, interface design and a clickable prototype, so the hard decisions are settled before engineering starts.
Clickable prototypeTechnical architecture - 03
Build
8–16 weeksTwo-week sprints against a fixed scope. You see working software every fortnight, not a status report.
Sprint demosAutomated tests - 04
Launch
1–2 weeksLoad testing, security review, migration and a rollout plan — with someone from the build team on call.
Security reviewRollout plan - 05
Support & scale
OngoingMonitoring, iteration and a support SLA. Most clients keep building with us long after go-live.
MonitoringSupport SLA
Advanced technologies
RAG Development Technologies Built Into Every Pipeline.
We do not just build products — we engineer intelligent, connected, future-proof digital experiences.
Hybrid Search
Semantic + BM25 for superior retrieval accuracy
Re-Ranking
Cross-encoder re-ranking for precision at scale
Smart Chunking
Semantic, recursive, and parent-child chunking strategies
Multi-Modal RAG
Text, images, tables, and charts in unified retrieval
Graph RAG
Knowledge graph-augmented retrieval for complex queries
Streaming RAG
Token-level streaming for real-time response generation
RBAC Filtering
Document-level access control in vector search
Adaptive Retrieval
Query-aware chunk selection and expansion
RAG Evaluation
Automated quality scoring with Ragas and DeepEval
Semantic Cache
Cache semantically similar queries for faster responses
Real-Time Indexing
Continuous document ingestion and index updates
Citation Engine
Source attribution with page-level precision
Technology stack
RAG Development Stack. 30+ Vector & LLM Tools.
Best-in-class tools chosen for performance, reliability, and long-term maintainability.
Vector Databases
PineconeWeaviateQdrantChromapgvectorMilvusLLM Providers
GPT-4oClaude 4Gemini ProLlama 3CohereMistralOrchestration
LangChainLlamaIndexSemantic KernelHaystackEmbedding Models
OpenAI AdaCohere EmbedBGEE5JinaInfrastructure
AWS BedrockAzure OpenAIGoogle Vertex AIModalReplicateEvaluation
RagasDeepEvalLangSmithWeights & BiasesTruLens
Selection guide
How to Choose a RAG Development Company
Choosing the right RAG partner is critical — retrieval accuracy and data privacy determine whether your AI system is trusted or abandoned.
Proven Portfolio
Look for references with measurable results in enterprise search, document Q&A, and knowledge management systems.
Senior Engineers
8+ years avg experience. LangChain, LlamaIndex, Pinecone, vector databases, and LLM orchestration expertise.
Fixed-Price Quotes
No hourly surprises. Clear scope with retrieval accuracy benchmarks, latency SLAs, and evaluation milestones.
Post-Launch SLAs
Index maintenance, model upgrades, quality monitoring, and retrieval accuracy guarantees.
Security Certs
SOC 2, ISO 27001, HIPAA compliant. On-premise deployment, data encryption, and RBAC for sensitive documents.
Your Timezone
Dedicated PM, daily standups, sprint demos, and accuracy review checkpoints.
“We were struggling with a React Native app that kept crashing. The team rebuilt the entire architecture in 6 weeks — crash rate dropped to 0.01%. Absolute lifesaver.”
Priya K.CTO, EdTech Series A, Dubai
0.01%crash rate
Frequently asked questions
Answers to common questions about custom RAG development, vector databases, retrieval accuracy and enterprise knowledge systems.
Ask our teamCustom RAG development services build retrieval-augmented generation systems that answer from your own documents rather than a model's training data. At query time the system retrieves relevant passages, generates an answer grounded in them and cites the source — the standard approach when answers must be verifiable or when different users see different documents.
A basic proof-of-concept with one document collection takes 2–4 weeks. Production-grade RAG with hybrid search, reranking and monitoring takes 6–12 weeks. Enterprise deployments with multiple data sources and RBAC take 12–20 weeks. Working demos ship at each milestone.
RAG retrieves external knowledge at query time; fine-tuning permanently modifies model weights. RAG is better for changing data, factual accuracy, source citations and lower cost. Fine-tuning suits changing tone or format. Most enterprise use cases benefit more from RAG.
Pinecone for managed simplicity. Weaviate for hybrid vector + keyword search. pgvector if you already run PostgreSQL. Qdrant for filtering and multi-tenancy. We select based on scale, latency and budget during scoping.
Smart chunking, hybrid search, cross-encoder reranking, citation tracking, confidence scoring and answer validation against retrieved documents. Production systems typically reach 95%+ factual accuracy on domain-specific questions.
Yes. RAG systems can run entirely on your infrastructure with self-hosted LLMs and local vector databases so no data leaves your environment. RBAC ensures users only see authorized documents.
Cost depends on document volume, data source count, retrieval accuracy targets and security requirements (RBAC, on-premise). Ongoing infrastructure scales with index size and query volume. We scope a discovery phase first and issue a fixed-price quote.
From our blog
View all articlesRAG Development Insights & Guides.
Blog
RAG Architecture: The Complete Guide for 2026
End-to-end guide to building production-grade retrieval-augmented generation systems.
Read article BlogVector Database Comparison: Pinecone vs Weaviate vs Qdrant
Which vector database is right for your RAG system?
Read article BlogAdvanced RAG Techniques: Beyond Basic Retrieval
Hybrid search, re-ranking, and agentic RAG patterns for superior accuracy.
Read articleExplore
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Let’s build something worth keeping.
Tell us what you are trying to build. A senior engineer will come back within one working day with a scope, a timeline and a fixed price.