
AI Agents for KYC & AML
What they do: AI agents for KYC & AML compliance
Headquarters: San Francisco, California, USA
Founders / leadership: Alexandre Berkovic (CEO), Chrisjan Wust (CTO)
Recent funding: $7.1M Seed (Feb 2026), led by Cherry Ventures
Team size (approx.): ~10 employees
Regulatory compliance automation (KYC, AML, risk intelligence) for banks and fintechs.
RegTech / Compliance Software
$7.1M
Participants reported to include Y Combinator, Rebel Fund, Deel Ventures and Singularity Capital.
“Backed by Cherry Ventures (lead) with participation from Y Combinator, Rebel Fund, Deel Ventures and Singularity Capital”
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tl;dr: We’re automating compliance for banks and fintechs with AI agents that work like human analysts – on a browser. Demand is exploding, and we’re expanding our engineering team to move faster & meet our 6-month roadmap. If you love building end-to-end systems, shipping fast and tackling hard problems, we should talk.
PS: want to skip the queue? Save Morty from Rick in our AI-proof coding challenge. If things go well, we'll reach out to organize an interview directly with our CTO. And if you win, we pay you $10k, no questions ask.
Background Before you can move money or open an account, someone has to make sure you’re not a criminal. That’s compliance – and it’s broken.
Sphinx builds AI analysts that work inside browsers, APIs, and internal systems just like humans — automating AML, KYC, KYB, and transaction monitoring end-to-end.
Why Join Sphinx
Technical Challenges
Most financial crime data isn't accessible via APIs – it lives in legacy systems, government portals, and third-party platforms. We're building agents that navigate these systems like humans do: clicking, scrolling, interpreting unstructured data, and adapting to interface changes. This requires computer vision, DOM understanding, and robust error handling at scale.
Risk doesn’t stop at borders, and neither can we. Our agents operate across languages and regulations, reconciling entities hidden behind shell structures and mismatched disclosures. Each check enriches a single, evolving graph of people, companies, and signals — a global risk intelligence network that never forgets.
Payments don't wait for compliance. We process risk signals at the same speed money moves – millions of transactions, thousands of entities, sub-second decisions. Doing this right on AWS means distributed inference, caching, queue orchestration, and self-healing data pipelines.
Out-of-the-box deep research agents easily confuse entities that look similar. ChatGPT isn’t going to be able to tell us whether John Smith is a terrorist. We’re building our own deep research pipeline to ensure LLMs don’t mix up facts from different people/companies.
What We Look For
About The Interview
Benefits
Compensation Range: $120K - $300K
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