Service
RAG systems — multilingual ready
Retrieval that answers from your data — grounded in citations, evaluated, and safe to put in front of users.
pgvector schemas, chunking and retrieval policy tuned to your corpus, hybrid search with re-ranking, citations, and an eval set that scores answer quality and hallucination — including multilingual content with the real constraints of right-to-left languages.
What's included
- pgvector schema, chunking & retrieval policy tuned to your corpus
- Hybrid search + re-ranking, citations & source tracking
- Self-hosted embeddings option so your data stays in your VPC
- Multilingual retrieval, incl. Hebrew (RTL, morphology, tokenisation)
- Eval set + hallucination scoring; per-tenant access control
Proof
A natural-language assistant answering over a 401K+ record corpus with pgvector and Groq tool-calling, live in production.
RAG & Hebrew / multilingual
How do you stop a RAG system from hallucinating?
Answers are grounded in your documents with citations, critical facts are validated before they're shown, and an eval set scores answer quality and hallucination rate continuously. When a wrong answer appears, the retrieval/model/data split is diagnosable rather than a black box.
Does Hebrew actually work in RAG?
Yes, with care. Hebrew brings real constraints — RTL, morphology, and tokenizer coverage that inflates cost and degrades quality — that break naive pipelines. I choose embeddings and parsing for the language and validate retrieval and answer accuracy on your own Hebrew content before deploying.
Where does my data live?
Wherever you need it to. The vector database, embeddings and retrieval can run entirely inside your VPC or on-prem, with private endpoints and your own encryption keys — nothing shipped to a third-party API. Per-tenant access control keeps users from retrieving documents they shouldn't see.
Need rag systems in production?
Book a 15-minute call and we'll scope it properly.