Want semantic search without operating a separate vector stack: no extra database, no standalone embedding service outside the platform.
Platform · Embeddings
Searching thousands of documents takes more than keywords. Numaga turns your documents into embeddings — a numeric representation of meaning — so the knowledge base finds what you mean, even when the words differ. Through the same gateway as all your other AI traffic, so with the same grip.
Want semantic search without operating a separate vector stack: no extra database, no standalone embedding service outside the platform.
Want people to find what is meant — including in large, Dutch-language document collections where keywords fall short.
Don’t want embedding cost as an invisible extra: reported separately, per model, apart from chat usage.
The problem
Semantic search requires every document to be converted into embeddings. In most AI tools that happens out of sight: a separate service, a separate bill, and usage that never lands in your reporting. Large document collections make that painful — tens of thousands of documents means tens of thousands of calls.
In Numaga, embeddings run through the same gateway as the rest of your AI traffic. They fall under the same management, storage sits in the platform’s managed database, and usage is reported separately — not mixed with chat, because the volumes and cost are fundamentally different.
How it works
Automatic with every knowledge-base sync — without separate vector infrastructure.
On every knowledge-base sync and every upload, the platform converts the content into embeddings via an embedding model on the gateway.
The vectors live in your environment’s managed database — single-tenant, in the Netherlands, with no separate vector stack to maintain.
Questions are answered hybrid: semantically (meaning) combined with exact terms — important for names, codes and domain jargon.
Embedding traffic has its own overview: which models, how many calls and what it costs — apart from chat usage, so your reporting stays clean.
We show how embeddings work in your environment — from knowledge-base sync to the cost overview.
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