Mohit Aron has built two enterprise infrastructure companies from scratch—Nutanix, which he co-founded, and Cohesity, which he founded—and each time he watched the same failure repeat at scale: leadership teams spending hours in pipeline reviews and forecasting meetings just reconstructing what the business had already done, not deciding what to do next. SciFin Tech Inc. is his attempt to kill that problem. The company launched Sept. 2 with $44 million in seed funding.
Altimeter Capital and Madrona Venture Group co-led the round. Foundation Capital also participated. A $44 million seed is large by any standard—most institutional seed rounds land between $3 million and $15 million—and it reflects both Aron's track record and investor conviction that enterprise data fragmentation is a durable, high-value problem.
Aron described the core issue as a "context gap": the distance between what is actually happening inside a business and what its software systems show at any given moment. "There is way too much art and not enough science," he said. At his prior companies, deal reviews became exercises in reconstructing state rather than driving action. "We were extracting the news and not changing the news," he said.
SciFin's answer is what Aron calls "context convergence." The platform pulls information scattered across finance systems, account records, deal pipelines, forecasts, sales representative data, territory assignments, customer conversations and operational tools into a single continuously reconciled layer. Specialist AI agents run in the background, updating that layer as meetings happen and operations shift, so the system's picture of the business does not drift from reality.
The primary user interface is an AI assistant named Pixie—named after Aron's dog. Pixie runs inside a web interface resembling a ChatGPT-style chat window, where revenue team members can ask questions about business operations and receive answers, reports and recommended actions drawn from the unified context layer. Aron said Pixie is also accessible through voice, email, Slack and WhatsApp, which lowers friction for sales representatives who live in those channels rather than in a browser.
The initial go-to-market focus is revenue and go-to-market teams. That is a deliberate wedge. Revenue operations is where data fragmentation pain is most acute and where budget exists to pay for a fix. CROs and CFOs own the pipeline and forecast, and both hate the manual reconciliation that currently sits between raw data and a number they can defend to the board.
Emmanuel Dounias, a managing director at Morgan Stanley, spoke to the platform's potential in a financial services context. "Advisors have access to a lot of information, but it isn't always connected or available when they need it," Dounias said. "SciFin's aim to bring that context together would reduce manual work, help advisors spend more time with clients on more relevant conversations, and better provide the services their clients need."
The competitive landscape Aron is entering is crowded. Salesforce owns the CRM layer and has spent heavily on its Einstein AI suite. Clari and Gong both address revenue intelligence and forecasting from different angles. Microsoft's Copilot for Sales sits on top of Dynamics and Teams. What SciFin is betting on is that none of these point solutions actually solve the cross-system context problem—they each optimize within their own data silo while reconciliation work still falls on the analyst or the rev-ops manager.
Aron's investor base reflects confidence that his enterprise credibility opens doors that would stay closed for a first-time founder. Nutanix went public in 2016 and now carries a multi-billion-dollar market cap as a hyper-converged infrastructure vendor. Cohesity, the data management company he founded after leaving Nutanix, raised over $900 million in venture capital and merged with Veritas in 2024. Those outcomes give Altimeter and Madrona real reason to write a $44 million seed check before a single enterprise contract has been publicly disclosed.
The seed size also signals how the company intends to compete: build the context layer fast, sign design partners at the enterprise level before a larger Series A, and use capital runway to demonstrate that AI agents can maintain an accurate, current picture of a business without requiring an army of analysts to babysit it. If Aron can show that the system self-corrects as operations change—rather than requiring manual data hygiene—the product has a defensible moat. Enterprise software that gets more accurate as it ingests more operational history is hard to rip out.
SciFin has not disclosed customer names, contract values or headcount. Aron is the founder and chief executive.
