LLM.co · Private AI & Custom AI DevelopmentPrivate AI knowledge search

Enterprise RAG development that stays on your network

Retrieval-augmented generation, or RAG, pairs a search index with a language model. A user asks a question, the system retrieves the most relevant passages from your documents, and the model writes an answer from those passages only. Enterprise RAG development is the work of making that reliable across thousands or millions of real files with real access rules.

LLM.co builds RAG as part of its custom AI development practice and runs it as private AI. Ingestion, embeddings, the vector index, the model and the interface all sit inside your environment. Your documents and the index built from them never leave your disks.

When private RAG beats the alternatives

Built-in assistants in a document platform work well when all your knowledge lives in that one platform. Private RAG fits when answers span several repositories, when documents include scans and tables, or when the content is privileged, regulated or confidential.

Fine-tuning is the other common option. It teaches a model a format or vocabulary but does a poor job of storing facts that change. For questions about current policies, contracts and procedures, retrieval is usually the right foundation, and fine-tuning can be added later for tone or structure.

Architecture choices that decide answer quality

Most RAG quality problems start in ingestion. We tune OCR, table extraction and layout-aware chunking on your actual documents, so a clause or a table row stays intact. We then combine keyword and vector search, apply metadata filters such as date, matter or department, and re-rank results before the model sees them.

• Embedding and generation models chosen by bake-off on questions from your staff.
• Permissions mirrored from each source and enforced at retrieval time.
• Incremental sync so edits and deletions reach the index promptly.
• Answers tied sentence by sentence to a cited page and passage.

How we measure enterprise knowledge search

Before launch we build an evaluation set of real questions with known correct sources. We score whether the right passage was retrieved, whether the answer is supported by it, and whether the system declines when the documents do not hold the answer. Those scores are rerun whenever the model, the parser or the index changes.

Good enterprise RAG development also tracks speed and cost per question, plus the questions that went unanswered. That list shows which sources are missing or poorly parsed, and it guides the next round of work after launch.

What you receive

You receive the ingestion pipeline, connectors, index configuration, prompts, the search interface or API, the evaluation set and its reports, and deployment code for your infrastructure. The vector index is yours and can be rebuilt from source at any time. Runbooks cover adding sources, re-indexing and model upgrades.

When you compare vendors for enterprise RAG development, ask to see their evaluation method, how they enforce source permissions, and whether the index can run with no outside API calls. Those three answers predict most production problems.