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GeniosAI exited stealth on November 9, 2026 with $19.1 million in seed and Series A funding, led by FactSet, to sell traceable AI analysis to banks and finance teams.
Key Takeaways
- GeniosAI raised $19.1M across seed and Series A rounds; FactSet led the Series A.
- Zoom and General Catalyst also invested. Valuation and the seed/Series A split are undisclosed.
- The San Jose company says Fortune 500 banks and technology firms already use its platform.
Lead
GeniosAI (stylized geniosAI), a San Jose, California startup building AI tools for financial institutions, came out of stealth on November 9, 2026 with $19.1 million raised across seed and Series A rounds. FactSet, the financial data provider, led the Series A. Zoom and General Catalyst also took part. The company did not disclose its valuation or say how the $19.1 million divides between the two rounds.
What Does GeniosAI Actually Build?
GeniosAI sells a financial intelligence platform for banks, investment teams, and the office of the CFO. The company calls its architecture an AI domain stack with three layers: a financial ontology, purpose-built agents, and what it terms traceable orchestration.
The pitch rests on traceability. Every figure in a research document or financial model, the company says, can be followed back to its source data, with the reasoning chain visible. Assumptions, calculations, and review decisions are preserved so users can verify results, challenge conclusions, and build on earlier work.
That addresses a known weakness of general-purpose language models in finance, where an unsourced number is a liability. GeniosAI targets buy-side and sell-side investment professionals as well as corporate finance teams. The company says its software is already in use at Fortune 500 banks and technology companies, for tasks such as earnings preparation, comparing results against analyst expectations, and anticipating investor questions. It named no customers.
Who Is Behind It?
Co-founder and CEO Prabhdeep (PD) Singh leads the company. Other co-founders were not named in the announcement. "A financial answer is only useful if you can show how you got there," Singh said, framing the product around retained institutional knowledge rather than one-off analysis.
Why Did FactSet Lead the Round?
FactSet led because the investment doubles as a distribution and data arrangement. Under the partnership, FactSet's market data feeds into the GeniosAI platform so that analysis can be traced to a licensed source. FactSet CEO Sanoke Viswanathan said, "The value of data is defined by how decisively you can act on it."
A data vendor backing an AI analysis layer is a hedge as much as a bet. Agents that read filings and produce models could sit between FactSet and its users, so owning a stake in one of them keeps the data licensed through the front door. The terms of FactSet's commercial arrangement, including any exclusivity, were not disclosed.
Zoom's participation points to a second integration. The platform can surface insights inside Zoom meetings, including earnings calls, which places the product where finance teams already spend time listening to management.
What Does the Round Say About Investor Appetite?
The round suggests that capital for vertical AI in finance is available, but that the buyers of the story are strategic. Two of the three named investors are operating companies with their own products to protect or extend. General Catalyst is the only traditional venture name on the list.
The $19.1 million total is moderate by current AI standards, and combining seed and Series A into one announcement means the market has no clean read on step-up pricing. Without a valuation, the evidence for how investors price a traceability-first approach is thin. The Fortune 500 usage claim is also unverified, since no customer or revenue figure accompanied it.
What Are the Risks and Competition?
GeniosAI enters a market that incumbents already contest. Data vendors, terminal providers, and large banks' internal AI teams all build or buy research automation. Newer startups pitch similar agents for diligence and modeling. A traceability layer is a defensible feature, but it can also be copied by larger rivals with deeper data access.
Institutional buyers also move slowly. Procurement, model-risk review, and compliance sign-off at large banks often stretch deployments across many months, which tempers how fast early pilots turn into recurring revenue. The company says the new capital will go toward scaling operations and developing its AI domain stack.
Outlook
GeniosAI has $19.1 million, a strategic data partner, and a distribution channel through Zoom. The product claim, analysis that can be audited line by line, fits the demands of regulated finance. What remains unproven is revenue, customer concentration, and valuation, none of which were disclosed. The next signals will be named enterprise customers and whether the FactSet tie-up broadens beyond data supply.
