Ingestion
Your data, cleanly in.
- PDF, Office, Markdown, HTML
- Wikis, DBs, CRMs, tickets
- Sync & freshness pipeline
AI · retrieval practice
RAG that answers from your data. Not a guess.
Shazra Labs builds RAG (retrieval-augmented generation) systems that ground an LLM in your own documents, so it answers with citations instead of hallucinating. We handle the whole pipeline: ingestion, chunking, embeddings, vector search, reranking, evals, and guardrails. And we measure accuracy before we call it done. Production-ready, fixed quote.
What it is
What RAG development involves
A RAG system retrieves the right pieces of your own data and hands them to an LLM, so answers are grounded and citable instead of made up. The demo is easy; production is where it gets hard: messy documents, permissions, retrieval that misses, and answers that drift. We build the whole pipeline and measure it with an evaluation set, so accuracy is a number you can see, not a vibe.
What we build
The whole pipeline, production-grade.
Every layer that stands between a question and a correct, cited answer.
Chunking & embeddings
Retrievable, not random.
- Structure-aware chunking
- Embedding model selection
- Metadata & permissions
Retrieval & reranking
The right context, every time.
- Vector + keyword hybrid search
- Rerankers & query rewriting
- Permission-aware filtering
Generation
Grounded, cited answers.
- Prompt & citation design
- Model selection & routing
- Streaming & structured output
Evaluation
Accuracy you can measure.
- Retrieval & faithfulness evals
- Regression suite
- Continuous improvement loop
Guardrails
Safe by default.
- "I don't know" over hallucination
- PII & prompt-injection defense
- Security checklist
Stack
What we build with
ClaudeGPTOpen modelsLangChainLangGraphLlamaIndexpgvectorPineconeWeaviateQdrantRerankers
The process
Data to grounded answers.
Eval-driven from day one. Fixed quote.
Data & eval set
Ingestion
Retrieval
Generation
Eval & tune
Ship + monitor
RAG development FAQ
People also ask
What is RAG development?
Building a system that retrieves relevant chunks from your own data and feeds them to an LLM so its answers are grounded in your content rather than guessed. It covers ingestion, chunking, embeddings, a vector store, retrieval and reranking, the generation prompt, evaluation, and guardrails.
When should I use RAG instead of fine-tuning?
Use RAG when answers must be grounded in changing or proprietary documents and you need citations and freshness. Fine-tuning changes style or behavior but doesn't give the model new facts reliably. Most production systems use RAG, sometimes with light fine-tuning on top. We help you choose based on your data and accuracy needs.
What data sources can you connect?
Documents (PDF, Office, Markdown), wikis and knowledge bases, databases and data warehouses, ticketing and CRM systems, and web content. We build the ingestion and sync pipeline, handle permissions, and keep the index fresh.
How do you measure and improve RAG accuracy?
We build an evaluation set and measure retrieval quality and answer faithfulness, then improve chunking, embeddings, reranking, and prompts against it. We also add guardrails so the system says "I don't know" instead of hallucinating.
How much does a RAG system cost to build?
It depends on the data sources, volume, accuracy bar, and whether you need an agentic layer on top. We give a fixed quote up front. The choices that move the number are in our AI Agent Development Cost guide.
Ready when you are
Need answers from your own data?
Real reply within a day, from someone who'll be writing the code. No sales desk in between.
NDA-friendly · Fixed quotes · Reply within 24 hours
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