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. ...
Most people now use ChatGPT, Claude, or Gemini every week without a clear mental model of what’s happening underneath. That’s fine right up until the model is confidently wrong and you have no way to tell. AI Model Basics is a new 20-minute LinkedIn Learning course aimed at exactly that gap. What the Course Covers How large language models actually work — enough mechanism to reason about them, without the math What prompting does and why it changes the output as much as it does Why models produce wrong answers, and what kinds of wrong to expect Personalization — what these tools can and can’t retain about you How to evaluate and improve what you get back instead of accepting the first response Who This Is For Anyone using AI tools at work who wants to understand them rather than just operate them. It’s beginner-level and assumes no technical background — no coding, no prior AI experience. ...
Most AI projects don’t fail at the model. They fail somewhere between the demo that impressed everyone and the system that has to survive contact with a real customer’s data, workflows, and definitions. I published a new LinkedIn Learning course on the role that closes that gap: Forward Deployed Engineering in the Age of AI. Why This Course Exists AI deployment differs fundamentally from a traditional SaaS rollout. A SaaS product is largely the same on day one for every customer. An AI system is not — its behavior depends on the customer’s data quality, their vocabulary, their edge cases, and their tolerance for being wrong. That means the work of making it succeed happens in the field, not at headquarters. ...
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. ...