A handful of companies sell a "private RAG" product you configure yourself. Fewer actually take on the engineering project of designing retrieval-augmented generation around your specific documents, access rules and hardware, then delivering it as software you own and run. If your requirements involve privileged, regulated or otherwise sensitive documents, that second category — a development company rather than a subscription — is usually what you are actually looking for.

Retrieval-augmented generation, or RAG, is the technique behind on-premise document search: it retrieves the relevant passages from your documents before asking a language model to answer, which keeps the answer grounded in what you actually have rather than what the model memorized during training.

What an on-premise RAG engagement actually involves

Unlike deploying a cloud SaaS tool, building RAG on-premise means someone has to:

  • Classify which documents and data can move where, and what compliance or client-confidentiality rules apply
  • Choose and benchmark an open-weight language model and embedding model against your actual documents, not a generic leaderboard
  • Size and provision the infrastructure — GPU servers on-site or in a private cloud tenancy
  • Build the ingestion pipeline, the retrieval logic, access controls that match your existing permissions, and the interface staff will use
  • Document and hand the system over in a way your team can maintain

That is systems engineering with an AI component, not a plug-in.

What to evaluate in a development company

  • Evidence of prior on-premise, not just cloud-API, work. Building a chatbot that calls a hosted API is a very different skillset from deploying and serving models on your own infrastructure. Ask specifically about on-premise projects.
  • A benchmarking methodology, not a recommended model list. A company worth hiring will test candidate models on samples of your documents and show you the accuracy difference, rather than defaulting to whichever model they used last time.
  • Security and access-control experience. Ask how they handle permission mirroring, encryption at rest, and audit logging — not whether the AI "is secure," which is not a meaningful claim on its own.
  • Infrastructure sizing skill. They should explain, in plain terms, why a given GPU configuration fits your document volume and expected user count, and what a pilot on smaller hardware would look like.
  • A support and handover plan. On-premise systems need model updates, monitoring and occasional re-benchmarking. Ask what happens after launch, not just during the build.
  • A pricing model that matches the project's shape. Fixed-scope builds and ongoing retainers both exist; a company that cannot describe its pricing model clearly before scoping your project is a caution sign.

Questions worth asking any shortlist

Ask for a walkthrough of a past on-premise deployment's architecture, not just a results summary. Ask what happens when a new document type appears that the system was not built for. Ask what hardware failure or model deprecation looks like operationally. The answers tell you more about a company than a client list does.

In-house build vs. hiring a development company

Some organizations have the internal skill to build this themselves — a team that already combines infrastructure, security and MLOps experience. Most do not, which is exactly why a specialist development company exists as a category. Hiring one is not an admission that the work is too hard to ever bring in-house; it is usually the faster and less risky way to get a working system while internal staff build up the operational skills to run it day to day afterward. A reasonable middle path many organizations choose is hiring a development company for the initial build and benchmarking, with a defined handover point where internal staff take over routine operation, keeping the outside partner on a lighter support retainer for updates and re-benchmarking.

How we approach it

We classify your data and compliance requirements first, benchmark candidate models against your own documents, size infrastructure from the result, and build and hand over the system with documentation your team can run without us. See the on-premise AI overview for the full process, or read about retrieval-augmented generation for business and how it compares to fine-tuning.

Frequently asked questions

What is included in an on-premise RAG development project?

Data classification, model benchmarking on your documents, infrastructure sizing and setup, the ingestion and retrieval pipeline, access controls matched to your existing permissions, and handover documentation. It is a full build, not a configuration task.

How is pricing usually structured for these projects?

Most development companies price either as a fixed-scope build with defined deliverables, or as an ongoing retainer covering maintenance and model updates after launch. Ask any company you are evaluating to explain which model applies and why before you scope the project.

How long does an on-premise RAG build take?

It depends heavily on document volume, how much cleanup the archive needs, and infrastructure procurement time if hardware has to be purchased. A benchmarked pilot on a subset of documents is usually the fastest way to get a realistic timeline for the full build.

Should we hire an agency or build this with an in-house team?

It depends on whether you already have staff with both MLOps and infrastructure experience. Many businesses hire a development company for the initial build and benchmarking, then keep day-to-day operation in-house once the system is handed over with documentation.

What should we ask a company's references?

Whether the system still performs well months after launch, how the company handled a change in document types or volume, and how responsive support was when something broke. Launch-day results tell you less than how the relationship held up afterward.