AI governance and model risk management for financial compliance
Solytics Partners builds a modular platform stack for AI governance, model risk, and financial crime compliance at regulated institutions. The tech foundation—Python, Kotlin, GCP (Kubernetes, Cloud Run, Cloud SQL), TensorFlow, PyTorch, Elasticsearch, RabbitMQ—reflects a data-heavy, ML-first architecture designed for real-time monitoring and regulatory auditability. Active hiring in engineering and product (accelerating velocity) aligns with projects around AI inventory dashboards, model monitoring controls, and risk calculation microservices, signaling expansion beyond compliance tooling into operational assurance infrastructure.
Solytics Partners is a RegTech and AI solutions firm serving financial institutions across risk management, regulatory compliance, and AI governance. The company operates a portfolio of integrated platforms—NIMBUS Uno, MRM Vault, MoDeVa, SAMS, EMoT, ATOMS, and others—designed to unify AI governance, model risk management, financial crime screening, and trade surveillance into end-to-end workflows. Headquartered in New York with 201–500 employees, the firm is addressing core pain points in model lifecycle management, data lineage traceability, and compliance with evolving global AI and financial regulations. Scale and operational complexity drive a focus on automation of governance workflows and audit-ready evidence generation.
Python, Kotlin, GCP (Kubernetes, Cloud Run, Cloud SQL), TensorFlow, PyTorch, Elasticsearch, RabbitMQ, Redis, PostgreSQL, and React. The stack emphasizes ML frameworks, container orchestration, and real-time data streaming for compliance and risk monitoring.
Current projects include AI inventory dashboards, model monitoring controls, market risk and FRTB calculation microservices, risk engines for VaR and expected shortfall, and regulatory reporting solutions. Focus is on automation of governance workflows and data lineage traceability.
Solytics Partners's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.