Services

RAG Development

Connect your LLMs to your actual data.

Overview

Your Data, Actually Useful

An LLM without your data is just a chatbot. With RAG, it becomes an expert on your business. We build retrieval pipelines that pull the right information from your documents, databases, and internal systems — giving accurate, grounded answers instead of hallucinations.

Data Pipeline & Ingestion

Chunking, embedding, metadata extraction — kept in sync as your data changes.

Retrieval Architecture

Vector search, hybrid retrieval, reranking, and filtering tuned to your data.

Response Quality Tuning

Prompt optimization and citation generation for accurate, traceable answers.

Production-Ready Interface

Chat UI or API endpoint with conversation history, citations, and feedback built in.

Process

How It Works

Every RAG system is different because every dataset is different. Here's how we get from raw data to reliable answers.

Data Audit & Strategy

We review your data sources, design the retrieval architecture, and pick the right vector DB and embedding model.

Pipeline Development

Ingestion pipeline, chunking strategies, and hybrid retrieval — each component tested against your actual questions.

Optimization & Launch

Systematic evals on retrieval accuracy, answer quality, and latency. We iterate until it hits your quality bar.

AI Assistant

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