2025-12-22 00:00:33

Back in 2023, Kalyani Ramadurgam teamed up with Ashi Agrawal, a former software engineer from Affirm, to launch Kobalt Labs. Their mission? Bringing compliance infrastructure into the machine-learning age.

The move reflects a broader shift in how fintech and Web3 projects are tackling regulatory challenges. As automation and AI reshape financial systems, the need for compliance tools that keep pace has become critical. Rather than relying on traditional, manual processes, Kobalt Labs is positioning itself at the intersection of compliance and advanced technology—helping platforms scale without sacrificing regulatory standards.

It's an interesting play on a problem that many blockchain and crypto platforms have grappled with: how to maintain institutional-grade compliance while operating in a rapidly evolving ecosystem.

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AirdropHarvester

· 2025-12-24 13:36

Compliance automation has been overdue for someone to handle. Regulation and innovation are not mutually exclusive.

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FlashLoanLarry

· 2025-12-22 00:30

ngl, the compliance-as-infrastructure angle is lowkey the real value extraction play here. most builders still treat it like a tax, not realizing the basis points saved on regulatory friction compound hard over capital cycles. curious if their ml models actually catch protocol vulnerabilities before auditors or if it's just... performative scalability theater 🤔

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GigaBrainAnon

· 2025-12-22 00:27

Ngl, this is the way to solve the problem; compliance automation should have come earlier.

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just_here_for_vibes

· 2025-12-22 00:21

Compliance automation is a direction that should have been pursued long ago; the traditional auditing process is really frustrating.

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LiquidityWitch

· 2025-12-22 00:20

nah but this is lowkey the compliance spell we've been waiting for... ML-powered regs? that's just transmuting bureaucratic chaos into actual alpha protection fr fr

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DeFiDoctor

· 2025-12-22 00:08

The consultation records show that these types of compliance infrastructure projects are often "preventive treatments," but very few can actually be implemented. The involvement of engineers from Affirm is certainly a plus, but what I care more about is—what are their models' precision and recall in actual risk identification? Having an AI framework is far from enough.

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