Chata.ai Raises US $10M For Deterministic AI In Finance

Feb 5, 2026 | NCFA Fintech Market Activity | AI and Finance Innovation

Calgary Fintech Advances Auditable AI for Financial Data

On January 21 2026, Calgary-based Chata Technologies Chata.ai closed a US$10 million Series A to expand its deterministic AI analytics platform for financial services, backed by 7RIDGE (UK) and Izou Partners (US).

Chata.ai focuses on a model that produces repeatable, auditable answers to natural language questions over financial data. In plain terms, deterministic AI means the system gives the same answer every time to the same question, with a clear record of how it arrived there. Instead of relying on probabilistic GPU driven models, its system runs inference on CPUs and is designed for environments where consistency, traceability, and explainability matter. Financial teams can query data directly without dashboards or engineering support while maintaining a clear audit trail.

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The company says funding will support growth across traditional finance, digital asset platforms, and wealth technology use cases where AI tools must operate inside compliance and risk frameworks. Investors pointed to the platform’s ability to combine scale, cost efficiency, and verifiable outputs as a practical fit for regulated finance workflows.

Kelly Cherniwchan, Chief Executive Officer, Chata Technologies:

“This investment allows us to bring trusted data analytics to financial institutions where precision, reliability, and regulatory clarity are critical.”

Deterministic AI changes the conversation for finance teams. The value is in being able to ask a question twice and receive the same answer, with a record of how that answer was produced.

Talking Point

When financial institutions adopt AI tools, will platforms that provide auditability and consistent outputs become the standard over systems that produce variable results?

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Financial firms want AI that fits inside existing control structures rather than sitting outside them. Deterministic models may become a foundation layer for analytics, reporting, risk review, and automated decision support across regulated finance.

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