KobiGPT vs Gemini

The most important clarification on this page: KobiGPT's default chat model is from the Gemini family. So the question is not "which model is better" but "will you use the model directly, or with an enterprise layer around it".

Using the model directly means finding the relevant document yourself for every query, pasting it in, and watching the context limit. The product layer automates that: documents are chunked and embedded, the most relevant passages are retrieved at query time, and the answer shows which file it came from.

Concretely, four things: an indexing pipeline, scope separation, authorisation, and metering. The indexing pipeline covers upload, chunking, embedding, and queue management. Scope separation ensures each assistant sees only its own files. Authorisation manages role-based access.

On metering, the Kobi Kredi mechanism applies: a document upload is billed at 0.1 Kobi, an AI message at a minimum of 0.1 Kobi, with the monthly quota defined per plan tier. Using the model API directly means building that metering and quota management yourself.

Another layer that matters in enterprise use is data isolation. KobiGPT indexes each company's documents in its own vector collection; one company's content never appears as a source in another company's query. That separation is provided at the infrastructure level.

A team using the model directly has to build that isolation into its own architecture. If you serve a single company, that may not be extra work; if you are building something serving multiple customers, isolation becomes one of the costliest items.