As early adopters, we integrated CamelAI into anastasia.ai and immediately saw traction shift. Its iframe-based integration enabled a full multitenant RLS architecture in production in under a week, freeing us from custom BI engineering and accelerating time‑to‑value.
What truly sets CamelAI apart is its knowledge base and reference-query system. The platform allows us to define our own domain terminology, metric definitions, table relationships, and formatting preferences in a knowledge base, ensuring consistent interpretations across queries. Meanwhile, reference queries give the model explicit SQL patterns and example metrics, anchoring responses in precise, repeatable logic.
This combination not only standardizes tone and personality, but dramatically improves data output correctness—a critical factor in building trust with our customers. After tuning these features, the accuracy of CamelAI’s insights improved noticeably, reducing manual review cycles.
Once live, our users got context-aware, accurate answers directly in the product. Engagement and conversion rose quickly, without a corresponding rise in support overhead or infrastructure work.
In summary, CamelAI delivers secure, scalable embedded AI analytics with enterprise-grade accuracy. The customizable knowledge base and reference-query features are not optional bells and whistles—they’re essential for any SaaS team that cares about data fidelity. For anyone building tenant-aware, trustworthy analytics, CamelAI is a standout choice.
CamelAI
Yes, we support large volumes of data and actively have customers with billions of rows of data.