ZENKEI
Custom retrieval-augmented generation that answers from your own documents with citations, built on your data and stack, with private deployments available.
Rated 4.9 out of 5 by clients across AI, software and quantitative projects.
RAG development is the work of connecting a language model to your own documents so it answers from your knowledge base with citations, instead of guessing. A retrieval-augmented generation system retrieves the passages that matter for each question, then has the model answer grounded in those sources. ZenkeiX builds custom RAG systems on your data and your stack, for production.
Whether you need answers over contracts, manuals, a support archive or an internal wiki, we handle the whole pipeline: ingestion, chunking, embeddings, vector search, grounded answers with references, and the evaluation and access control around it. When your data cannot leave the building, the entire RAG system runs on a fully private, on-premise deployment.
Your documents, turned into an expert that cites its sources.
Pipelines that parse, clean and chunk your PDFs, contracts, wikis and databases so retrieval has something precise to work with.
Embeddings and a vector database tuned for relevance and recall, with hybrid and re-ranking strategies where they earn their keep.
The model answers only from retrieved passages and shows its references, so every answer can be traced back to the source.
Retrieval and answer evals, hallucination checks and access control, so the system stays accurate as your documents grow.
One working session on your documents and the questions people actually ask. We leave with the use case that moves real numbers.
Production-grade from day one: your data, your stack, tuned retrieval and grounded answers, with evals proving it works before launch.
Deployed, monitored, improved as your documents change. The system earns its place in your operation or we keep working.
RAG connects a language model to your own documents so it retrieves the relevant passages and answers from your knowledge base with citations, instead of guessing. It's how you get accurate, source-backed answers on private data.
Building a RAG system means ingesting and chunking your documents, embedding them into a vector database, retrieving the right passages for each question, and having the model answer with citations, plus evaluation and access control around it.
Fine-tuning changes how a model writes and reasons. RAG changes what it knows, by giving it your current documents at answer time. RAG is usually faster to update, cheaper and easier to keep accurate, and the two can be combined.
Yes. By grounding answers in retrieved passages and showing citations, a well-built RAG system cuts hallucinations sharply and lets users verify every answer against the source.
Yes. When your data can't leave the building, we deploy the models and vector database locally or in your private cloud, so nothing is sent to a third-party API.
We're stack-agnostic, working with vector databases such as pgvector, Qdrant, Pinecone, Weaviate and Milvus, and with models from Claude, GPT, Gemini and open-weight families, chosen for the job rather than the hype.
Thirty minutes on your knowledge base. No slides, no fluff.