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§ Private Profile · New York City, NY, USA
AI software company automating risk operations, compliance, and partner diligence for banks and fintechs, specializing in AI risk management.
Kobalt Labs has raised $11.0M across 1 funding round.
Key people at Kobalt Labs.
Kobalt Labs was founded in 2023 by Ashi Agrawal (Founder) and Kalyani Ramadurgam (Founder).
Kobalt Labs has raised $11.0M in total across 1 funding round.
Kobalt Labs, based in New York City, New York, USA, develops an AI-powered copilot that automates risk operations, compliance, and partner diligence for financial institutions. The platform specifically aids community, regional, and sponsor banks in scaling their partnerships and vendor volumes, aiming to increase revenue without requiring additional headcount. Fintechs utilize Kobalt Labs to complete partner diligence in approximately one-tenth of the traditional time, streamlining their operational processes. Operating with a team of 6 employees, the company was part of the Y Combinator Summer 2023 batch, with Aaron Epstein serving as a primary partner. Kobalt Labs further expanded its market presence by joining the NayaOne Tech Marketplace on August 12, 2024, indicating growing industry adoption. Founded in 2023 by Ashi Agrawal and Kalyani Ramadurgam, the firm is currently focused on enterprise scaling and a customer-driven roadmap.
Kobalt Labs has raised $11.0M across 1 funding round. Most recently, it raised $11.0M Series A in December 2025.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Dec 5, 2025 | $11M Series A | — | Alloy Labs, Y Combinator | Announced |
Key people at Kobalt Labs.
Kobalt Labs was founded in 2023 by Ashi Agrawal (Founder) and Kalyani Ramadurgam (Founder).
Kobalt Labs has raised $11.0M in total across 1 funding round.
Kobalt Labs's investors include Alloy Labs, Y Combinator.
Kobalt Labs is an AI-powered copilot designed to automate and streamline risk and compliance operations specifically for fintech companies and banks. Its platform ingests third-party contracts and documentation, internal policies, past assessments, and external regulations to instantly identify and track risks, significantly reducing manual effort. This enables community, regional, and sponsor banks, as well as fintechs, to scale their partnership and vendor diligence processes efficiently without increasing headcount, often completing partner diligence in one-tenth the usual time[1][2][3].
For an investment firm, Kobalt Labs represents a cutting-edge fintech startup focused on AI-driven regulatory technology (RegTech) with a mission to modernize and automate compliance workflows in financial services. Its investment appeal lies in its ability to address a critical pain point—compliance costs and operational inefficiencies—within a highly regulated and growing sector. The company’s impact on the startup ecosystem includes accelerating fintech-bank collaborations and enabling faster, safer scaling of embedded banking and BaaS (Banking as a Service) programs[2][3].
For a portfolio company, Kobalt Labs builds an AI copilot product that serves banks and fintechs by automating third-party risk and compliance diligence. It solves the problem of time-consuming, manual risk operations by providing real-time regulatory coverage, automated gap detection in contracts, and continuous monitoring. The company has demonstrated strong growth momentum through partnerships with embedded banking platforms like Treasury Prime, boosting internal diligence capacity by over four times and expanding its reach within the financial ecosystem[2][3][4].
Kobalt Labs was founded by Kalyani Ramadurgam (CEO) and Ashi Agrawal (CTO), both Stanford computer science graduates with deep AI and fintech backgrounds. Kalyani previously conducted AI research at Stanford, built financial security products at Apple, and developed health data tools used by governments globally. Ashi has experience at Affirm, Nuna, and Meta, specializing in infrastructure and scalable systems[1].
The idea emerged from recognizing the inefficiencies and risks in manual compliance and risk operations within fintech and banking partnerships. Early traction came from demonstrating that their AI platform could drastically reduce diligence time and increase regulatory coverage, leading to key partnerships such as with Treasury Prime, a leading embedded banking software provider. This partnership marked a pivotal moment, enabling Kobalt Labs to scale its technology to a broader network of banks and fintechs[2][3].
Kobalt Labs rides the wave of AI-driven RegTech innovation amid increasing regulatory complexity and rising compliance costs in financial services. The timing is critical as embedded banking and fintech partnerships grow rapidly, demanding scalable, automated solutions to manage third-party risk and compliance efficiently.
Market forces favor Kobalt Labs due to:
By modernizing third-party diligence, Kobalt Labs influences the ecosystem by enabling faster, safer scaling of fintech partnerships, reducing compliance bottlenecks, and setting new standards for continuous, AI-powered regulatory adherence[2][3][4].
Looking ahead, Kobalt Labs is poised to expand its footprint across more banks and fintechs, deepening integrations with embedded banking platforms and broadening its regulatory coverage. Trends shaping its journey include the continued growth of embedded finance, increasing regulatory complexity, and the rising demand for AI-driven operational efficiency in financial services.
Its influence is likely to evolve from a niche compliance tool to a foundational platform for risk operations in fintech and banking, potentially expanding into adjacent areas like fraud detection and operational resilience. As regulatory environments become more dynamic, Kobalt’s real-time, AI-powered approach will be increasingly indispensable for financial institutions aiming to scale responsibly and cost-effectively.
This trajectory ties back to Kobalt Labs’ core mission: to modernize and automate risk operations, enabling fintechs and banks to innovate and grow without being hampered by manual compliance burdens[1][2][3][4].
