RAG Development Services: AI That Answers From Your Own Documents

RAG development services build and run Retrieval-Augmented Generation systems that connect an AI model to your own documents, databases, and knowledge bases, so answers are grounded in your data and cite their sources. At Bitontree that covers the whole pipeline: data ingestion, retrieval engineering, evaluation before launch, and the ongoing work of keeping retrieval accurate as your content changes. It is engineering plus operation, not a one-off build.

Because test questions have answers and real questions often do not. A pilot is usually evaluated on a set of questions someone wrote while looking at the documents, so retrieval always finds something relevant. In production, users ask about things the corpus does not cover, and a system with no abstention path passes the irrelevant passages to the model anyway, which answers from training data instead. The second cause is chunking: if a rule and its exception get split into separate chunks, retrieval returns half a rule, and half a rule reads exactly like a whole one. Neither failure shows up in a demo.

You set a relevance threshold on the retrieval score and treat falling below it as a valid outcome, not an error. Below the threshold the system says it does not have the answer and routes the user onward, rather than sending weak passages to the model and hoping. This one change removes most production hallucinations in RAG, because in a grounded system the model rarely invents facts unprompted. It invents them when it is handed passages that do not answer the question and asked to answer anyway.

For most corpora, pgvector on the Postgres you already run is enough, and it keeps your content and your embeddings in one system with one backup and one access model. A dedicated vector store earns its place when scale, filtering or hybrid search demands it, or when you need index rebuilds that do not compete with production database load. The honest version is that the vector store is rarely what decides whether a RAG system works. Chunking, retrieval strategy and evaluation decide that, and teams often spend their first month choosing a database instead.

Chunk on the document's own structure rather than a fixed token count. Fixed-size chunking is the default in every tutorial and it is where a lot of production inaccuracy starts, because it splits a clause from its qualifier and a heading from the table it describes. Split on sections, clauses or question boundaries, keep the parent heading with the chunk so it retains context, and let the size vary. If you must start from a number, start around 500 to 800 tokens with overlap, then tune against a real evaluation set rather than treating it as settled.

When recall is fine but precision is not, which you can tell apart by measuring them separately. If the right passage is usually somewhere in the top twenty but rarely in the top three, a reranker is the correct fix and typically costs a few hundred milliseconds. If the right passage is not in the top twenty at all, a reranker cannot help you, because it only reorders what retrieval already found. That is a chunking or retrieval-strategy problem and reranking will just reorder the wrong candidates faster.

Separately from the model, and against a fixed set of questions with known-good source passages. Retrieval is measured on whether the correct passage appears in the results at all and how highly it ranks. Generation is measured on whether the answer is supported by the passages it was given. Keeping the two apart matters because they have different fixes: a wrong answer from the right passage is a prompting problem, and a fluent answer from the wrong passage is a retrieval problem. Without that evaluation set every tuning decision is a guess and regressions ship unnoticed.

It matters as soon as your content contains identifiers. Part numbers, policy codes, SKUs, drug names and error codes are precisely where embeddings are weakest, because semantic similarity does not distinguish one code from a near-identical one. Keyword matching does. Hybrid retrieval runs both and merges the results, and on technical or regulated corpora it is usually the single largest accuracy gain available. On prose-heavy content with no identifiers, vector search alone is often fine.

Yes, most RAG accuracy problems trace back to retrieval, not the model, and that is fixable. When the right context is not found, the model fills the gap and hallucinates, so we start by auditing retrieval precision on your real queries, then tune chunking, embeddings, ranking, and evaluation until answers are grounded and cited. We can take over a stalled pilot, get retrieval quality back on track, and keep monitoring it so accuracy holds as your data changes.

A RAG application answers from your actual documents and cites the source, while a plain chatbot answers from whatever the model memorized during training. That grounding is the whole point: a plain LLM chatbot guesses when it does not know and cannot show where an answer came from, whereas a RAG system retrieves verified passages first and can be traced back to them. For anything where a wrong answer has a real cost, the citation trail is what makes it usable.

Yes, a RAG application integrates with the systems your knowledge already lives in, including CRMs, ERPs, SharePoint, cloud storage, and document repositories. We build a retrieval layer that pulls across those sources so your AI can answer from data spread across departments, formats, and languages. Integration into your existing interfaces and workflows is part of the build, not an add-on.

Yes, you can outsource RAG development to an embedded AI team like Bitontree that builds the system and keeps running it in production. We work as an extension of your team, adopting your stack, data sources, and release cadence, so you get the pipeline without hiring a full in-house AI group. The difference from a typical vendor is that we stay on after launch, monitoring retrieval quality instead of handing off and disappearing.

Yes, RAG is built to handle large volumes of unstructured content like PDFs, web archives, and presentation decks, alongside structured data and images. Our multimodal RAG systems retrieve and reason over the formats your information actually lives in, not just plain text. The refresh pipeline keeps that index current as archives grow.