RAG as a Service for Enterprise Teams

RAG as a service is a managed offering where a provider builds, hosts and maintains a retrieval-augmented generation system over your data. A language model answers questions using passages retrieved from your own content, with citations, while the provider handles ingestion, search, evaluation, hosting and ongoing tuning so your team doesn't have to.

Retrieval-augmented generation is a method in which a system retrieves relevant passages from a knowledge source and gives them to a language model before it answers. The answer is based on that content rather than only on the model's training. The approach was introduced by Lewis and colleagues at Facebook AI Research in 2020.

Enterprise RAG is retrieval-augmented generation built for company-wide use: it connects to many content sources, enforces each user's document permissions, masks personal data, logs every query and answer, and is tested against a set of real questions. Those controls separate it from a basic chatbot over a folder of files.

There's no single best provider. Compare accuracy on your own documents, data handling, permission support, update speed and export options. Run the same set of real test questions with two or three providers, including at least one cloud platform and one development partner, and choose on measured results rather than demos.

Ask how accuracy is measured on your content, where data is stored, how new and changed documents are indexed, whether user permissions are respected at retrieval, and what you can export if you leave. Then request a paid pilot on your own documents with a scored test set before signing a longer contract.

Pricing depends on content volume, permissions, query volume, model choice and quality testing. With SoluLab, a RAG MVP typically costs $15,000 to $30,000, a production system $30,000 to $75,000, and an enterprise RAG platform $80,000 or more. Hosting, model fees and monthly optimization continue after launch.

An architecture review takes 1 to 2 weeks, and a pilot on a defined set of documents usually takes 4 to 8 weeks. A production system with permissions, integrations and monitoring typically takes 8 to 16 weeks, and an enterprise platform across many sources 3 to 6 months, depending on data quality.

A managed service starts quickly and needs fewer in-house skills. An in-house build gives full control and suits unusual data or strict security rules, but needs search, ML and DevOps expertise. Many teams start managed and move parts in-house later, so insist on open formats and exportable indexes from day one.

Use RAG when answers must come from changing documents and show sources. Use fine-tuning to change a model's style or teach it a narrow skill. Many systems use both, and RAG is usually the easier first step because you update the index rather than retraining the model whenever content changes.

Yes. Retrieval can miss the right passage, and the model can misread it. Reduce errors with good chunking, hybrid search and reranking, required citations, an "I don't know" response when sources don't cover a question, a test set of real questions and a human review step for risky answers.

It can be, if the system enforces user permissions at retrieval, keeps data in an approved environment, masks personal data, logs access and treats retrieved text as untrusted to resist prompt injection. Check these points in any provider contract, and confirm where embeddings, indexes and logs are stored.

Yes, if it's built for it. The ingestion pipeline copies each document's access rules into the index, and retrieval filters results by the asking user's identity, so answers only use documents that person could open in the source system. Ask any provider to demonstrate this on your own content.

Yes. SoluLab builds, hosts and maintains RAG systems for enterprises and growing companies, starting with a 1 to 2 week architecture review of your data and use case. Our compliance-governed RAG copilot for One Wallet searches 50K+ documents and cut support ticket load by 72%.