Architecture

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. ...

Part of Lenny Rachitsky’s “The AI-Native Product Manager” free workshop series on Maven. Most products fail not because of bad engineering, but because teams build the wrong thing. They either overbuild features nobody asked for or underbuild solutions that miss the real problem. Forward Deployed Engineering (FDE) is the antidote. What is FDE? Forward Deployed Engineering is a methodology where technical teams embed directly with customers to witness how software meets reality. Instead of building from assumptions, FDEs develop customer instinct by being in the field — watching real users interact with real products under real conditions. ...

Originally published on Next Play If you work in tech, you should learn about Forward Deployed Engineering (FDE). It’s quickly become one of the most popular roles that nearly all fast-growing AI companies are looking for…at the same time, it’s the type of role very few people and companies seem to actually understand. I designed Project Frontline at Palantir, a program that sent over 250 engineers into live customer deployments. Those engineers are now at OpenAI, xAI, Anduril, and dozens of leading companies. ...

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.” ...