AI agents & RAG systems

Most AI projects fail at the boring parts: chunking, evaluation, cost, and what happens when the model is wrong. My RAG development services cover exactly those parts. I build retrieval-augmented systems grounded in your data, that log every answer and degrade safely when confidence is low.

Who needs a RAG system

If your team spends hours looking things up in contracts, support tickets, product catalogues or internal manuals, the knowledge already exists. It is just hard to reach. A general chatbot cannot help, because it has never seen your documents and will confidently make things up. Retrieval fixes that: the model answers only from the passages it finds in your own material, and it shows you where each answer came from.

What I build with RAG development services

The core is a pipeline that reads your documents, splits them into sensible pieces, and stores them so the right passage can be found by meaning, not just by keyword. On top of that sits the answering layer: a model that receives the question plus the relevant passages, writes a grounded reply, and cites its sources. Where it helps, I go further and turn the assistant into an agent that can look up an order, create a ticket or update a record through your existing API, with every action logged.

I care about what happens when the system is unsure. It should say so, hand over to a person, or ask a clarifying question, rather than guess. I also keep cost in view from the start, because a system that is accurate but too expensive to run will be switched off.

How a project runs

Discovery comes first: we pick one real workflow, collect a sample of documents and write down twenty or thirty questions with known good answers. That list becomes the test set. Then I build the ingestion and retrieval, measure answers against the test set, and adjust until the numbers are good enough to trust. Launch is usually to a small group of internal users before anyone outside sees it. After launch I watch the logs, add documents and tune prompts, and every change is checked against the same test set so nothing gets quietly worse.

Cost and timeline

A typical project takes 4–10 weeks, depending on how many document sources there are and whether the system only answers or also takes actions. I give you a written estimate after discovery, once the scope is clear. Running costs depend on the model provider and volume, and I show you those numbers before we commit.

FAQ

Can my data stay on our own servers?

Yes. If documents cannot leave your infrastructure, the models can run locally, for example with Ollama, and the vector database lives on your own server.

Which model provider do you use?

Whichever fits the job. I have built with Anthropic, OpenAI and Gemini, and I keep the code provider-agnostic so you can switch later.

How do we know the answers are right?

The evaluation harness. Every prompt or model change is run against your test questions, so accuracy is measured, not assumed.