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Users ask the AI analytics questions about their business, and Simba draws on live enterprise data to provide answers and insights. These answers are said to be deterministic versus generative in order to maintain accuracy, as outputs are based only on the data provided. Outputs also come with a trail enabling users to see how they determined the answer and on what information it was based. For security, the system applies enterprise controls at query time and enforces the user’s own security and governance rules automatically.
“We’re addressing the problem at the front end of the data source layer, instead of the tail end, by applying governance, business semantics and a full audit trail at query time,” said Shawhin Mosadeghzad, the company’s lead for Simba Intelligence, in an email. “This is to ensure answers are consistent, traceable and aligned with business rules when connecting to live enterprise data.”
Basing the AI on live enterprise data serves to encourage flexibility and adaptability, as it can be deployed in a cloud environment, on premises or a mixture of the two. It also means there is little to no need for data management, as it is built on a “zero-data movement architecture” that does not require copying or moving one’s data. Mosadeghzad said this is a key point in the product’s design.
“This [architecture] enables harnessing AI to access live data where it lives, without duplication or transferring data that’s built on brittle pipelines, across a multitude of data platforms, including data lakes, data warehouses and transactional data. We deploy next to the database and query the data through aggressive caching capabilities, the semantic layer and business rules, with zero-data movement. This is critical for global organizations that have data living on-premises, in the cloud and/or hybrid environments, and need a solution that meets their data sovereignty and residency needs,” he said.
He added that Simba Intelligence is not a large language model but, rather, a technology that can pair powerfully with one, such as those from major AI model providers like OpenAI or Anthropic. It can also be integrated into one’s own products and platforms without needing additional portals or switching between solutions. To this end, Simba Intelligence also boasts an MCP that allows other AIs to interact with it. This is all part of a larger design philosophy that emphasizes flexibility and versatility to avoid vendor lock-in.
Mosadeghzad noted that as powerful as any AI system may be, people won’t get anything out of it unless they’re confident they can trust its outputs. This was why the company built it with both deterministic outputs and a high degree of user control.
“At its core, Simba Intelligence is built on trust: Can you stand behind an answer? Can you explain what it did at every step? Can you verify what it has access to and what it doesn’t? Can it apply business rules and context? And finally, can it do that not just against one data platform you’re connecting to, but many more at the same time? That trust matters enormously in enterprise environments where a bad insight doesn’t just cause confusion; it can drive a wrong decision,” he said. “We enable teams to make decisions they can stand behind and builders to embed intelligence their customers can trust.”
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