Shazra Labs
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.

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Grounded & cited Eval-driven Guardrailed
vectors LLM
The pipeline Ingest · Embed · Retrieve · Generate Measured with evals Says "I don't know" safely
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.

01

Ingestion

Your data, cleanly in.

  • PDF, Office, Markdown, HTML
  • Wikis, DBs, CRMs, tickets
  • Sync & freshness pipeline
02

Chunking & embeddings

Retrievable, not random.

  • Structure-aware chunking
  • Embedding model selection
  • Metadata & permissions
03

Retrieval & reranking

The right context, every time.

  • Vector + keyword hybrid search
  • Rerankers & query rewriting
  • Permission-aware filtering
04

Generation

Grounded, cited answers.

  • Prompt & citation design
  • Model selection & routing
  • Streaming & structured output
05

Evaluation

Accuracy you can measure.

  • Retrieval & faithfulness evals
  • Regression suite
  • Continuous improvement loop
06

Guardrails

Safe by default.

Stack

What we build with

Models & frameworks
ClaudeGPTOpen modelsLangChainLangGraphLlamaIndex
Vector & infra
pgvectorPineconeWeaviateQdrantRerankers
The process

Data to grounded answers.

Eval-driven from day one. Fixed quote.

01Data & eval set
02Ingestion
03Retrieval
04Generation
05Eval & tune
06Ship + monitor
We build the evaluation set with you first, so "good" is defined before we start. Then we wire ingestion and retrieval, tune against the evals, add guardrails, and ship with monitoring. If you need an agent on top — tools, multi-step workflows — that lives in our AI agent practice.

What does a RAG system cost?

It depends on the data sources, volume, accuracy bar, and whether you need an agentic layer. We give a fixed quote. The model, infra, and tooling choices that move the number are in our AI Agent Development Cost guide.

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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.
From the blog

AI field notes

What an AI build costs, and the security checklist we run before shipping one.

Cost11 min read

AI Agent & Chatbot Development Cost in 2026

The models, RAG, tools, guardrails, and infra that move the number.
Security9 min read

AI Agent Security Checklist

Prompt injection, exposed MCP servers, and the real attack surface of an AI system.
RAG Vector search Evals Guardrails

Need answers from your own data?

Tell us your data sources and the questions you need answered. We'll come back with an approach, a scope, and a fixed quote within a day.

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