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BarrioVibe
Software & AI

RAG Systems Development

A general model has read the internet and none of your contracts, SOPs or product manuals. Retrieval-augmented generation closes that gap by finding the relevant passages in your own material first and making the model answer from them. We build the pipeline end to end: ingestion and chunking, embeddings and a vector store, hybrid search with a re-ranking pass, and answers that carry a citation back to the passage they came from. The evaluation set comes with it, because otherwise nobody can tell whether retrieval is working.

Start a projectWhat’s included

Scope

What'sincluded

Everything below is in the standard engagement. Anything outside it is agreed in writing before the work starts, never after.

  • Ingestion of your own sources: documents, PDFs, wikis, tickets, transcripts and database records
  • Chunking and metadata design tuned to how your material is actually structured
  • Embeddings and a vector store, hosted or self-hosted depending on your data residency rules
  • Hybrid retrieval combining semantic and keyword search, with a cross-encoder re-ranking pass
  • Grounded answers with citations back to the source passage, and a refusal path when nothing relevant is found
  • An evaluation set measuring retrieval and answer quality, plus a refresh pipeline so the index does not go stale

Who this is for

Built for three situations

  • Teams with large internal document sets
  • Support functions answering the same questions repeatedly
  • Regulated businesses that need a citable answer

Process

How thisactually runs

  1. Corpus review

    We look at what you actually have, in what formats and in what state, and tell you honestly whether it is good enough to retrieve from before anything is built.

  2. Pipeline build

    Ingestion, chunking, embedding and the vector store stood up, with hybrid search and re-ranking wired in and tuned against your material.

  3. Evaluation

    A question set built from real queries your team asks, scored for whether the right passage was retrieved and whether the answer was faithful to it.

  4. Deploy and refresh

    Shipped behind your own authentication, with a re-indexing job so new and changed documents reach the index without anyone rebuilding it by hand.

Deliverables

What you end up holding

  • The retrieval pipeline and its vector store, deployed in your own infrastructure
  • A query interface, or an API your existing product can call
  • The evaluation set and its scores, so quality is a number rather than an impression
  • A re-indexing job that keeps the corpus current
  • Source code and documentation, yours at handover

What we need from you

Documents required

  • A representative sample of the documents to be indexed
  • Access to the systems the content lives in, or an export from them
  • Any confidentiality or data residency requirements that constrain where it can be hosted
  • A list of real questions the system is expected to answer

Missing something? Tell us anyway. We can usually work around a gap, and it is better to know before we start.

Next step

Tell us your situation and we will scope it.

Talk to us about it

Scope, fee and dates confirmed in writing before anything starts.

Questions

AboutRAG Systems Development

  • Why not just paste the documents into the chat window?

    That works until the material outgrows the context window, which happens quickly, and it puts the whole corpus in front of the model on every question whether it is relevant or not. Retrieval finds the few passages that matter for that specific question, which is both cheaper and measurably more accurate than flooding the prompt.

  • How do I know it is not making things up?

    Every answer carries the passage it came from, so you can check it in one click. We also build an evaluation set from your team’s own questions and score faithfulness on it, meaning whether the answer is actually supported by the text that was retrieved. That is a number we report to you rather than a claim we make. The system is also built to say it does not know when retrieval comes back empty, which is the behaviour most demos quietly leave out.

  • Does my data get sent to a model provider?

    Retrieved passages are sent to whichever model generates the answer, under enterprise terms that exclude your content from training. Where that is not acceptable, the same architecture runs against a self-hosted open model with nothing leaving your infrastructure. We settle that at the corpus review stage, before anything is indexed.

  • What happens when the documents change?

    The refresh pipeline re-indexes changed and new documents on a schedule or on an event from the source system, so the index tracks the corpus. A stale index is the most common reason a retrieval system that worked at launch stops being trusted six months later, which is why it is in scope rather than left as an exercise.

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