Compliance depth. Engineering rigor.
A small focused team with backgrounds in bank examination, fintech compliance, and payments infrastructure engineering. We built the tool we needed and couldn't find.
The people building Riftbeacon
Priya Sundaram
Six years at Veritas Compliance Partners advising early-stage fintechs on BSA program design. Certified Anti-Money Laundering Specialist (CAMS). Deep experience with FinCEN examination preparation and SAR narrative quality review. Holds an MBA from NYU Stern and a BA in Economics from University of Michigan.
Daniel Park
Previously staff engineer at Finix, building payment processing infrastructure. Seven years in fintech engineering with a focus on high-throughput transaction systems and event-stream processing. Architected Riftbeacon's rule evaluation engine and real-time OFAC screening pipeline. MS Computer Science, Columbia University.
Adrienne Watts
Former bank examiner at the OCC, specializing in BSA/AML program reviews at community and mid-size banks. Nine years examining AML programs before transitioning to fintech compliance advisory. Deep expertise in SAR quality standards, examination expectations, and FinCEN regulatory interpretation. CAMS certified.
Marcus Ellery
Product leadership at Unit (banking-as-a-service) and previously at Stripe. Focuses on the interface between developer experience and compliance requirements — translating regulatory obligations into buildable API workflows. Instrumental in designing Riftbeacon's case management UX and onboarding sequence. BA Economics, Brown University.
Compliance credibility is the product
Examiner-informed design
Adrienne's OCC background means every alert rationale, SAR narrative template, and audit trail entry is designed around what examiners actually check — not what vendors assume examiners check.
Engineering for transaction scale
Daniel's background in high-throughput payments infrastructure means the rule engine was designed for 50M+ transaction/month volumes from day one — not retrofitted as clients scale.
No black-box ML
Every ML scoring model has an adjacent explainability layer. We won't ship a model that produces a score without a rationale — because our compliance team won't accept it and neither will the examiners our clients face.
We're hiring compliance engineers and ML engineers
Small team, meaningful problem, early equity. Reach out if you're at the intersection of financial regulation and production engineering.