Presented at AI Engineer. Finance used to be bottlenecked on producing content. It is now bottlenecked on verifying it. An analyst who once spent the day building a model now spends it checking whether a machine’s output can be defended to a client, a committee, or a regulator. ...
Presented at AI Engineer. Most companies hire forward deployed engineers as an extension of go-to-market: a technical body to keep a large account happy. That’s the wrong frame. FDE is a product strategy. The job is to find the real problem, ship the smallest thing that solves it, and turn what you learned into leverage for the product. ...
Originally published on Anthropic’s blog Inside a platform that indexes 26M+ SEC filings, earnings call transcripts, IR presentations, consensus estimates, and private data across 14,000+ companies and 27 global markets, and how the team behind it built AI that validates every number to the exact filing, page, and line item. The quick pitch NameKepler Founded2025 FoundersVinoo Ganesh (CEO) and John McRaven (CTO) StackAWS, Rust, Python, containers for orchestration GrowthIndexed 26M+ SEC filings, 50M+ public documents, 1M+ private documents, and 14,000+ companies across 27 global markets in less than three months. Financial firms operate in a heavily regulated environment where reporting has to be auditable and accountable. Every figure in a regulatory filing, deal pitch, or research report needs to be verifiable against source documents. ...
Originally published on Kepler Everyone’s talking about context engineering right now, but most of the conversation is focused on the wrong thing. Read the blog posts, the guides, the thought leadership. They’re all asking the same questions: What should I include in the context window? How do I manage tokens efficiently? How do I curate what the model sees? These are valid questions. They’re also the easy part. The hard part isn’t deciding what context to include. It’s building systems that deliver that context reliably, with provenance, at scale, every single time. That’s not a context problem. That’s an engineering problem. And engineering means something specific. ...
Originally published on Kepler AI produces numbers that are incorrect, untraceable, and unverifiable. Asking the same question twice yields different answers, and there’s no way to distinguish right from wrong outputs. This unreliability poses serious risks across industries like healthcare, legal, insurance, government, and finance where numerical accuracy directly impacts outcomes. Through conversations with 137 financial firms - including private equity, hedge funds, and investment banks - a consistent pattern emerged: everyone wants to use AI, but nobody trusts it. As one managing director stated, “I can’t put a number in front of a client if I can’t show where it came from.” ...