RAG Systems
Connect proprietary data to LLMs with grounding, retrieval quality, and latency measured from day one.
Reliable answers grounded in your data
We build RAG systems that go beyond basic vector search. Our pipelines include advanced retrieval techniques, reranking, and rigorous evaluation to ensure every answer is grounded in your facts.
Advanced Chunking
Custom parsing and semantic chunking strategies tailored to your document types (PDFs, docs, code).
Hybrid Retrieval
Combining vector search with keyword search and reranking models for maximum accuracy.
RAG Evaluation
Continuous monitoring of faithfulness, relevance, and answer quality against curated eval sets.
The RAG Lifecycle
Ingestion
Automated pipelines for data parsing and cleaning.
Embedding
Optimizing vector representations for your specific domain.
Retrieval
Fine-tuning search parameters and reranking logic.
Serving
Production deployment with caching and latency controls.
Why teams bring Ethix into production
We start with the business problem, ship in milestones, and leave behind systems your team can own after launch.