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Most teams using Claude and other frontier LLMs today are getting real value from them. A question gets asked, the model pulls together an answer, and something that used to take an analyst an afternoon takes a few minutes instead. The problem is what happens after that answer is delivered. Nothing. This is one of the gaps we set out in our whitepaper, How to scale AI in private markets. The whitepaper covers the full picture of what it takes to move from ad hoc prompting to a system that compounds over time. This piece goes deeper on one part of that: why AI needs to keep working after the prompt ends. Claude and other frontier LLMs are inherently reactive. They wait for a prompt, respond to it, and then the interaction ends. For a lot of work, that is fine. But dealmaking and portfolio work are not one-off questions. Tracking companies, monitoring pipelines, and spotting changes in a market all require ongoing attention, not a single well-timed query. The reactive trap In practice, this shows up as a quiet kind of drift. A company gets researched, assessed, and filed away as "not right now." Six months later, something changes: new funding, a leadership change, a shift in strategy, and it becomes exactly the opportunity the firm was looking for. But nobody thought to ask about it again, so nobody knew. The same pattern plays out with monitoring. Claude can answer a specific question about a specific company well. What it will not do on its own is notice that something has changed and flag it before anyone asks. Every interaction is a fresh start. There is no built-in mechanism for persistence, no memory of what happened last time, and no awareness of what might be worth checking on today. This is not a flaw in the model. It is simply how prompt-based tools work. The gap only becomes visible once teams try to run this way at scale. What continuous actually requires A useful way to think about it is the difference between a system that waits and a system that watches. A reactive setup depends on someone remembering to ask the right question at the right time. A continuous one surfaces relevant changes without waiting to be asked. A reactive setup treats every interaction as isolated. A continuous one builds on what came before, so outputs accumulate rather than disappear. As Martin Pomeroy, co-founder and CTO of Deal Engine, puts it: a key concept in private markets is "not yet." Many opportunities are not immediately actionable, but they become relevant over time. Supporting that requires a system that tracks companies continuously, rather than analyzing them once and moving on. That shift, from point-in-time answers to ongoing awareness, is what separates AI that helps with individual tasks from AI that actually compounds across a firm. Why this matters more as adoption grows The more a firm relies on Claude and other frontier LLMs, the more this gap costs. Without something tracking companies and pipelines in the background, the same names get rediscovered months apart, the same signals get missed until someone happens to look, and institutional memory stays locked inside individual chat histories rather than building into something the whole team can draw on. Models are exceptional at the interface layer: taking a request and producing a good answer. But a good answer at a single point in time is not the same as an ongoing view of what is happening across a portfolio or a market. That requires something running continuously behind the interface, connecting to the firm's data, and carrying context forward from one interaction to the next. Giving AI a heartbeat, in other words, means building the layer that keeps working after the prompt ends. For the full framework, including where most firms stall and what a scalable setup actually requires, read the whitepaper: How to scale AI in private markets. If you want to see what that looks like in practice, book a demo or get in touch with the team.