RAG Development Company

How does a RAG pipeline work, step by step?

A RAG system has two halves. Indexing runs ahead of time; answering runs on every question.

• Ingestion and cleaning: connectors pull files from your sources, parsers keep tables and headings, boilerplate and duplicates are dropped
• Chunking: documents are split along their own sections, and each chunk keeps its source, page, version and access list
• Embeddings: each chunk becomes a vector and is stored in a vector database next to a keyword index
• Retrieval: a question runs vector and keyword search together, limited to chunks the user may read, then a reranker keeps the best few
• Generation: the model answers only from those passages and cites each one, or says the sources do not cover it

RAG or fine-tuning - which one does my use case need?

Choose RAG when the knowledge changes, when users need to see where an answer came from, or when different people may read different documents. Updating a RAG system means re-indexing a file; updating a fine-tuned model means another training run, and it still cannot tell you its source or hide one department's data from another.

Fine-tuning is the better choice when the problem is behavior rather than knowledge: a fixed output format, a house writing style, or domain shorthand that prompting keeps getting wrong. The two combine well - a fine-tuned model that writes in your format, fed by RAG with current facts. If you need that wider application work, it sits with our LLM development team.

Which vector database should we use: pgvector, Pinecone, Qdrant or Weaviate?

Start with what you already run. If your data lives in PostgreSQL, pgvector keeps vectors, metadata and access rules in one database with one backup, and handles most company knowledge bases comfortably. Pinecone suits teams that want a fully managed index with no servers to operate. Qdrant is open-source, fast at filtering on metadata such as tenant and access group, and can be self-hosted. Weaviate ships hybrid keyword and vector search built in.

The choice matters less than people expect: retrieval quality is decided by parsing, chunking and reranking. We keep the store behind one interface in the code, so changing it later means re-loading the index, not rewriting the application.

How do you stop users seeing answers from documents they may not open?

Permissions have to be enforced at retrieval, not at the answer. Every chunk is stored with the access list of the document it came from, taken from the source system or your identity provider through SSO groups. At question time the search itself is filtered to the groups the user belongs to, so a restricted passage is never fetched and can never leak into an answer or a citation.

Access changes are synced like content changes: when someone leaves a team or a file is re-shared, the next sync updates the chunk's access list. Before launch we run leak tests with users from each group asking questions whose answers sit in files they must not see.

How do you keep a RAG system accurate and up to date after launch?

Accuracy is measured, not assumed. Before launch we agree a golden set of real questions with approved answers and their source passages. Retrieval is scored separately (did the right passage come back) from generation (does the answer stay faithful to what was retrieved), so a failure can be traced to search or to the model. A release that drops below the agreed bar does not ship, and the same set is re-run when you change the embedding model or the LLM.

Freshness is an engineering job too. Connectors re-index only files that changed, remove deleted ones the same day, and keep version labels so an answer about release 5 never quotes the release 4 guide.

What does RAG development cost to build and to run?

Building: a RAG system on our pipeline base starts from $3,699 and takes 2-8 weeks. A custom RAG system typically runs $8,000-$25,000, also 2-8 weeks, with larger scopes quoted in writing first. If you need a full chat product as the front end, our ChatGPT clone ships in 6 working days from $2,799, with retrieval added as custom work.

Running: you pay once to embed each document (and again for changed ones), for vector database storage, and for model tokens on every answer. Sending five reranked passages instead of twenty, caching repeated questions and routing simple ones to a smaller model are the three levers we use to hold cost and latency down.

How do you evaluate a RAG development company?

Ask how they would measure your system, not how clever the demo is. A serious vendor will ask for sample documents and real questions before quoting, explain how permissions are enforced inside the search, show a retrieval and faithfulness report from a test set, and tell you what happens when a file is deleted.

Also check you are buying the right thing. RAG is the retrieval layer. For the whole LLM application around it, see LLM development; for scripted conversation flows, see chatbot development; to plug AI into systems you already run, see AI integration services; and if you are still deciding where AI fits at all, start with AI consulting.