Two months after leaving Uber, Yifu Diao and Ming Fang had a $3 million check. The seed round, co-led by Kindred Ventures and Uncork Capital in December 2021, was a vote of confidence in their ability to productize the internal fraud-fighting system they had built for the ride-hailing giant [TechCrunch, December 2021]. Their startup, Sperta, is now selling that system as a service.
It is a bet on a specific kind of automation. Financial services and technology companies face a common operational tangle: fraud detection, credit underwriting, insurance processing, and compliance checks. Each requires a complex web of rules, data sources, and manual reviews. Sperta's pitch is a no-code decision engine that lets analysts, not just engineers, build and modify those workflows [CB Insights]. The founders call it a rules engine as a service, translated from their experience on Uber's internal Mastermind project [Kindred Ventures].
The Wedge: From Mastermind to Market
The product's credibility is tied directly to its origins. At Uber, Diao and Fang worked on the risk team during a period of hypergrowth, where fraud challenges scaled alongside the business. The system they helped build, Mastermind, was the internal platform used to create and manage the rules that flagged suspicious activity. Sperta is essentially their attempt to turn that bespoke, in-house infrastructure into a configurable product for other companies [TechCrunch, December 2021].
This gives them a wedge into a crowded market. They are not selling a generic low-code tool. Their stated target user is an analyst or data scientist who already knows SQL, suggesting a focus on sophistication over simplicity [Sperta Blog]. The company's blog posts argue that traditional no-code interfaces are too limited for the complex, nested logic required in financial risk decisions. Sperta aims to sit in the middle, offering more power than a drag-and-drop builder but less friction than a full engineering deployment.
The Founding Team and Early Traction
The team is small, reportedly around three employees, and deeply technical [LeadIQ, 2026]. The co-founders' backgrounds are a classic fintech-founder blend of big-tech engineering and specific domain experience.
| Role | Name | Prior Experience |
|---|---|---|
| Co-Founder & CEO | Yifu Diao | Software Engineer, Uber; Cardless; LinkedIn; Zynga [Welcome to the Jungle] |
| Co-Founder & CTO | Ming Fang | Software Engineer, Google; Uber (Risk Team) [Welcome to the Jungle] |
Their shared history at Uber's risk team is the core of the company's narrative. The $3 million seed round, raised so quickly after founding, was explicitly for hiring, according to the initial announcement [TechCrunch, December 2021]. The company has also achieved SOC 2 Type 1 compliance, a necessary baseline for handling sensitive financial data [LinkedIn, 2026]. Public traction metrics or named customer logos, however, have not been disclosed since the fundraise.
The Competitive Landscape and Market Fit
Sperta operates in a space with established incumbents and newer entrants. One named competitor is Shieldrule Technologies. The broader competitive set includes legacy business rules management systems and a growing category of no-code automation platforms aimed at operations teams.
Sperta's differentiation rests on a few key claims:
- Domain-specific design. Built by engineers who scaled a real-world, high-stakes fraud system, not as a general-purpose tool.
- SQL-first approach. Acknowledges that the target user is technically proficient, aiming for depth over broad accessibility.
- Holistic framework. The platform aims to integrate compliance, credit, and fraud workflows into a single AI-powered decisioning layer, rather than treating them as separate silos [CIO Economic Times, 2026].
The market tailwind is clear. As fintechs and embedded finance offerings proliferate, the need to automate risk and compliance decisions grows more urgent. Manual reviews and brittle, code-dependent rules engines become bottlenecks. Sperta is betting that companies will pay for a system that offers both control and agility.
The Quiet Period and the Path Forward
The most notable fact about Sperta's last few years is the lack of public news. Since the December 2021 seed announcement, there has been no follow-on funding round disclosed and no public launch of major customer partnerships. This could indicate a deliberate stealth mode, a challenging go-to-market journey, or simply a focus on deep product development before a commercial push.
The company's next twelve months will likely answer that question. The key milestones to watch are a Series A fundraise, the announcement of flagship design partners in fintech or insurance, and concrete data on deployment scale. The seed capital from Kindred Ventures and Uncork Capital bought runway; the next round will need to be bought with commercial traction.
For now, Sperta's bet is a $3 million wager that the rules engine they built for one hypergrowth company can become the standard for many others. The question for Diao and Fang is whether the market sees Mastermind's successor as a necessity or a nice-to-have. Can they convert their Uber pedigree into paying customers before the runway ends?
Sources
- [TechCrunch, December 2021] Ex-Uber software engineers raise $3M for Sperta | https://techcrunch.com/2021/12/16/ex-uber-software-engineers-raise-3m-for-sperta/
- [Kindred Ventures, December 2021] Our Investment In Sperta | https://medium.com/kindred-ventures/our-investment-in-sperta-598200eaee6
- [CB Insights] Sperta Company Profile | https://www.cbinsights.com/company/sperta
- [Welcome to the Jungle] Sperta Company Profile | https://app.welcometothejungle.com/companies/Sperta
- [LeadIQ, 2026] Sperta Employee Data
- [LinkedIn, 2026] Sperta Company Page
- [Sperta Blog] On No-Code and Complex Logic
- [CIO Economic Times, 2026] Article referencing Sperta's AI framework
- [Preqin] Sperta Inc. Funding Profile | https://www.preqin.com/data/profile/asset/sperta-inc-/459234