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When users ask AI analytics business questions, Simba leverages a company’s live data to provide answers and insights. To maintain accuracy, these answers are said to be generative while being deterministic, since the output is based solely on the data provided. The output also includes a trail, allowing users to see how the answer was determined and what information it was based on. When it comes to security, we apply enterprise controls at query time and automatically apply your own security and governance rules.
“We address the problem at the front end rather than the tail end at the data source layer by applying governance, business semantics, and a full audit trail at query time to ensure answers are consistent, traceable, and aligned with business rules,” Shawin Mosadeghzad, head of Simba Intelligence at the company, said in an email.
Building AI on real corporate data also helps foster flexibility and adaptability, as it can be deployed in cloud environments, on-premises, or a mix of the two. It is also built on a “zero data movement architecture” that does not require copying or moving data, which means there is little need for data management. Mosaddeghzad said this was a key point in the product design.
“this [architecture] By leveraging AI, you can access living data across numerous data platforms, such as data lakes, data warehouses, and transactional data, without having to replicate or transfer data built on fragile pipelines. Deploy next to your database and query data with zero data movement through aggressive caching, semantic layers, and business rules. “This is critical for global organizations whose data resides on-premises, in the cloud, and/or in hybrid environments and who require a solution that meets their data sovereignty and residency needs.”
He added that Simba Intelligence is not a large-scale language model, but a technology that can be powerfully combined with language models from leading AI model providers such as OpenAI and Anthropic. It can also be integrated into your own products and platforms without requiring additional portals or switching between solutions. To this end, Simba Intelligence also features an MCP that allows interaction with other AIs. This is all part of a larger design philosophy that focuses on flexibility and versatility to avoid vendor lock-in.
Mosaddeghzad pointed out that no matter how powerful an AI system is, if people are not confident that they can trust its output, they will not gain anything from it. This is why we built a system with both definitive output and a high degree of user control.
“At its heart, Simba Intelligence is built on trust. Can you stand by your answers? Can you explain what you did at each step? Can you validate what is accessible and what is not? Can you apply business rules and context? And finally, can you do it not just for the one data platform you are connecting to, but for many data platforms at the same time? “This trust is critical in an enterprise environment where bad insights can not only cause confusion but also bad decisions. We empower teams to make decisions they can live with. Developers need to work together to help customers embed intelligence they can trust.”
