consultance.ai

Services · Last updated May 2026

RAG systems that make scattered knowledge usable.

Your team is asking the same questions twice a day because the answer lives in a PDF, a Notion page, and a Slack thread. We build the retrieval system that finds it once, cites the source, and stops the repeats.

What we build

Document ingestion and chunking strategy
Vector search, hybrid retrieval, and reranking
Source-grounded answers with citations
Permission-aware retrieval for private business data
RAG quality evaluation and monitoring

Why it matters

A useful RAG system is not a chatbot wrapper. It needs clean ingestion, search quality, citations, permissioning, evals, and feedback loops. We build the whole pipeline.

  • Find answers across scattered knowledge
  • Give teams faster access to policies, contracts, procedures
  • Cut repetitive internal questions

Questions about rag systems

What is a RAG system?

A pipeline that connects a language model to your private data so answers are grounded in real documents, not model memory.

Can RAG handle messy documents?

Yes, but quality depends on ingestion, cleaning, metadata, retrieval strategy, and evals. We design the full pipeline.

Ready to build this?

Book a call and we will map the first high-leverage workflow.

Book an AI audit

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