RAG Development Services
Retrieval-augmented generation systems that ground AI responses in your data
What We Deliver
Most LLM applications need access to proprietary data that was not part of the model's training set. Retrieval-augmented generation (RAG) solves this by retrieving relevant documents from your knowledge base at query time and feeding them to the LLM as context. The result: accurate, grounded responses with source citations instead of hallucinated answers.
We build production RAG systems end to end. That includes document ingestion pipelines that handle PDFs, Word files, HTML, and structured data. Vector database setup and optimization for fast, accurate retrieval. Hybrid search that combines semantic and keyword matching. And citation tracking so users can verify every claim against the original source.
Our RAG implementations are designed for enterprise scale: millions of documents, sub-second query latency, role-based access controls, and continuous evaluation to catch quality regressions before users notice them.
Key Deliverables
- Document Ingestion Pipeline
- Configured & Optimized Vector Database
- Hybrid Search Implementation
- Citation & Source Attribution Layer
- RAG Evaluation Framework & Benchmark Results
- Production Monitoring & Quality Dashboard
How We Help
Document Ingestion Pipelines
Parse, chunk, embed, and index documents from PDFs, Word files, HTML, Confluence, and databases into vector stores.
Vector Database Setup
Select, configure, and optimize vector databases for your scale, latency, and cost requirements.
Hybrid Search
Combine semantic vector search with keyword (BM25) search for higher retrieval accuracy across diverse query types.
Citation & Source Tracking
Surface the exact source documents and passages behind every generated answer so users can verify claims.
Multi-Modal RAG
Retrieve and reason over images, tables, and charts alongside text for complete document understanding.
Evaluation & Testing
Automated quality benchmarks measuring retrieval precision, answer faithfulness, and citation accuracy.
How We Work
Data Audit & Architecture Design
We inventory your document sources, evaluate data quality, and design the RAG architecture: chunking strategy, embedding model selection, vector database, and retrieval pipeline.
Ingestion Pipeline Development
Build automated pipelines to parse, clean, chunk, and embed your documents. Handle edge cases like scanned PDFs, complex tables, and multi-format sources.
Retrieval Optimization & Search Tuning
Tune retrieval parameters, implement hybrid search, add re-ranking, and optimize chunk sizes for your specific query patterns and data types.
Integration, Evaluation & Production Launch
Integrate with your application, run evaluation benchmarks against golden datasets, implement access controls, and deploy with monitoring and quality dashboards.
Tools & Technologies
Talk to us about your AI project
Tell us what you're working on. We'll give you a honest read on what's realistic and what the ROI looks like.
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