Finance teams have the data to answer high-stakes business questions, but traditional analytics workflows make insight slow to produce and hard to scale. Sapien removes that friction with natural-language, context-aware analysis, accelerating how teams get to answers and act on them.

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&pizza replaced manual SQL queries and Excel exports with Sapien's AI platform, giving every store leader instant access to performance insights without waiting on IT or analytical support and saving dozens of hours per week across teams.

Traditional business intelligence and most financial AI tools are effective at calculation but remain static in their understanding of a business. Sapien’s Company Engine takes a different approach by continuously learning a company’s structure, data semantics, and business logic over time rather than treating each analysis as an isolated request. This enables analysis that explains not only what changed, but why it changed, using the definitions and priorities the business actually operates on.

Not all AI demos are created equal, especially for finance and ops teams. Most default to generic dashboards and surface-level summaries that skip over the complexity these teams actually deal with. We wrote up what leaders should look for, what to demand, and where most demos miss the mark.

Finance teams have the data to answer high-stakes business questions, but traditional analytics workflows make insight slow to produce and hard to scale. Sapien removes that friction with natural-language, context-aware analysis, accelerating how teams get to answers and act on them.

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&pizza replaced manual SQL queries and Excel exports with Sapien's AI platform, giving every store leader instant access to performance insights without waiting on IT or analytical support and saving dozens of hours per week across teams.

Traditional business intelligence and most financial AI tools are effective at calculation but remain static in their understanding of a business. Sapien’s Company Engine takes a different approach by continuously learning a company’s structure, data semantics, and business logic over time rather than treating each analysis as an isolated request. This enables analysis that explains not only what changed, but why it changed, using the definitions and priorities the business actually operates on.

Finance teams reasonably worry about AI being wrong. Traditional software earns trust through determinism but AI works differently. It can't guarantee identical results every time, but it has complete verifiability. For financial operations, the ability to see every data source, every calculation, and every assumption behind a number matters more than deterministic accuracy. AI that shows its work builds trust that deterministic systems can't match at scale.

Finance teams don’t struggle with AI because their data isn’t “AI-ready.” They struggle because communication with AI is unfamiliar and imprecise. When AI interprets questions differently than intended, trust breaks down. Sapien addresses this by making assumptions explicit, allowing users to correct misunderstandings in plain language, and retaining company-specific context over time.

Most CFOs aren’t avoiding AI because it lacks capability. What stops adoption is expectations. Finance teams are judging AI using frameworks built for traditional enterprise software. But AI is not meant to quietly automate. Its real value is collapsing weeks of analysis into hours, making questions that used to be too expensive in time suddenly worth asking.

This funding from top investors in AI and finance will support larger-scale research efforts to build the AI that helps humans run companies.

What we've learned on working with customers and researching to push the limits of agent reliability and observability

Discover how Carlex partnered with Sapien to eliminate manual finance reporting, supercharge analytics across business units, and uncover profit-driving insights once accessible only to large finance teams.

A technical dive into how Sapien can understand companies inside and build intuitions across massive data siloes.