RAG development services

An engagement covers the data audit, the retrieval architecture (chunking, embeddings, vector store and reranking), the pipeline build, integration with your LLM or agent layer, evaluation on your own queries, deployment with monitoring and index refresh, and handover to your team. You can also start with a review of a RAG system you already run.

Both, and most engagements start with the consulting part. A RAG architecture review looks at what you already have (retrieval quality, chunking, embedding choice, evaluation coverage) and returns a ranked list of what is costing you accuracy, with a verdict on whether a rebuild is warranted. When the answer is to build, the same engineers continue into delivery, so nothing is handed over between a strategy team and an implementation team.

Yes. Source code, retrieval configuration, evaluation suites and runbooks transfer to you at handover, with training. The stack is model-agnostic and built on open-source foundations, so no part of it can only be operated by Vstorm. Another team could pick the system up, and that is the test of ownership that matters.

Both depend on the number and state of your sources, your access-control requirements, the systems RAG connects to and the accuracy the use case needs. We start with a Proof of Value on your own documents and queries. It shows retrieval quality and running costs before the production build, and we estimate the rest from there.

We build a query set from your real questions and measure recall and grounding on it before launch: whether the right passage comes back, and whether the answer stays within it. The same suite runs as a regression test after every index or model change and is part of the handoff.

RAG fits when answers depend on your own documents or data that change over time, and when users need to see where an answer came from. It is the wrong tool when the task involves no knowledge lookup, or when the knowledge is small and stable enough to sit in the prompt. Fine-tuning is the better choice when the model has to learn a domain style or vocabulary rather than facts, and we sometimes combine the two.

Internal databases, document repositories, CRM and helpdesk systems, public APIs and proprietary knowledge bases. Retrieval follows your existing permissions, so users only get documents their role allows them to see.

Yes. Retrieval and inference can run in your VPC, in an approved cloud tenancy or fully on your own servers, with no data leaving your environment. Because the stack is model-agnostic, the language model can be self-hosted as well.

An agent without a retrieval layer only knows what the model learned in training, which goes stale in any domain with live data. With RAG underneath, the agent can query internal knowledge bases, fetch current product or policy information and read proprietary documents before it acts. That is the architecture behind our real estate due diligence agent and our healthcare appointment agent.

LangChain structures most retrieval pipelines: document loaders, text splitters, embedding integrations and vector store connectors. LlamaIndex covers more complex retrieval architectures, and LangGraph is used when RAG runs inside a stateful agent loop. Vector stores such as Milvus, Qdrant and pgvector are chosen for the scale and deployment model. Open-source tooling keeps the system readable by your team and free from proprietary pricing changes after deployment.

Healthcare retrieval has problems generic RAG rarely handles: dense clinical documentation, domain terminology, role-tiered access and a high cost of a wrong answer. We start with an audit of the knowledge base (what it contains, how it is structured and where retrieval is likely to fail) before building the pipeline. Our multi-channel agent for a US healthcare provider with over 100,000 members shows how a RAG-grounded agent can personalize communication and stay auditable. Every healthcare delivery attaches sources, so clinical staff can check what an answer is based on.

Yes, and it is one of the most valuable uses of RAG. The agent reads a document, works out what additional context it needs, retrieves that context from connected sources and produces a structured output without a person at each step. Our real estate due diligence agent cross-references property documents with regulatory and market data. Our Senetic engagement drafts email replies from live product data and routes them for dispatch without manual steps.

Documents are split into chunks and turned into vector embeddings. When a question comes in, semantic search finds the chunks closest in meaning, and a large language model writes the answer from them, with a reference to the source. The model answers from your content instead of relying on what it learned in training.

A search engine returns a ranked list of documents and leaves the reading to you. RAG retrieves the relevant passages and writes an answer from them, while still showing which sources it used.