How Kepler built verifiable AI for financial services with Claude
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Re: How Kepler built verifiable AI for financial services with Claude
#2Anthropic published a profile on what we're building at Kepler. Sharing because the architectural argument (LLM for intent, deterministic code for retrieval and computation, every number traceable to source) is the part I'd actually want HN to push on. Happy to answer questions in the thread.
Re: How Kepler built verifiable AI for financial services with Claude
#3[flagged]
Re: How Kepler built verifiable AI for financial services with Claude
#4> The duo’s answer was to build deterministic infrastructure that serves as a trust and verification layer for AI.
On the one hand, very encouraging to see plain old deterministic infra w/o using slop machines.
On the other hand, this is a recognition that LLMs are just additional friction in the system that we would better off without in the first place!
Re: How Kepler built verifiable AI for financial services with Claude
#5Anthropic published a profile on what we're building at Kepler. Sharing because the architectural argument (LLM for intent, deterministic code for retrieval and computation, every number traceable to source) is the part I'd actually want HN to push on. Happy to answer questions in the thread.
Very interesting. What size team does it take to build this, incl. analysts, project managers, product managers etc.? How long did you spend in analysis before building and the how long to first customer using it?
Re: How Kepler built verifiable AI for financial services with Claude
#6> The duo’s answer was to build deterministic infrastructure that serves as a trust and verification layer for AI. On the one hand, very encouraging to see plain old deterministic infra w/o using slop machines. On the other hand, this is a recognition that LLMs are just additional friction in the system that we would better off without in the first place!
Just friction? What do you mean? What would you do instead?
Re: How Kepler built verifiable AI for financial services with Claude
#7From a systems engineering standpoint, the purpose of LLMs is to construct, verify, and "push down" abstractions and deterministic layers. Deterministic layers are able to cope reliably with the law of medium numbers.
Re: How Kepler built verifiable AI for financial services with Claude
#8Anthropic published a profile on what we're building at Kepler. Sharing because the architectural argument (LLM for intent, deterministic code for retrieval and computation, every number traceable to source) is the part I'd actually want HN to push on. Happy to answer questions in the thread.
could I get a link to the Kepler finance site? googling for "Kepler financial" yields 5-6 other finserv companies
Re: How Kepler built verifiable AI for financial services with Claude
#9> The duo’s answer was to build deterministic infrastructure that serves as a trust and verification layer for AI. On the one hand, very encouraging to see plain old deterministic infra w/o using slop machines. On the other hand, this is a recognition that LLMs are just additional friction in the system that we would better off without in the first place!
You're misunderstanding something about the problem space they're describing. The deterministic infra is for an underlying "execution layer"; the LLMs are providing utility by figuring out how to express English language queries in terms of the primitives of that verifiable layer. That way, you can describe your results deterministically even though the process of arriving at them was not necessarily deterministic.
Re: How Kepler built verifiable AI for financial services with Claude
#10[deleted]