Why does a ChatGPT summary stop being useful after the first read?
Because it doesn't connect to anything. Paste a CIM into ChatGPT and you'll get a competent summary. But that summary doesn't know about the last three deals your firm did in the same sector, doesn't remember how your team adjusts for stock-based comp, and has no idea what your IC tends to flag in businesses like this one. Every new chat starts from zero, which is the same problem whether the file came from Box, SharePoint or your inbox. As the deeper argument on system of record lays out, the issue isn't that AI can't read your files, it's that reading isn't the same as knowing which version is right, and a chatbot has no way to make that call on its own.
Does switching to a "better" model fix this?
No, and that's worth being precise about. Frontier models converge in capability every few months and settle back to rough parity within weeks of the next release, so whichever one currently tops a benchmark usually won't hold that spot for long. The full case for why the model isn't the constraint goes through this in detail, but the short version is: the jump in value comes from whether your data is structured and connected, not from which logo sits on the chat window. A firm stuck pasting documents into ChatGPT will be exactly as under-served by next year's frontier model as it is by today's, because the model was never what was missing.
When plain ChatGPT is genuinely enough
It's worth saying this plainly rather than pretending every use case needs a platform. Drafting a first pass at an email, summarising one document you already have open, brainstorming how to frame a memo, checking your grammar, these are real jobs and ChatGPT handles them well. If the task starts and ends inside a single conversation with no need to reference anything your firm knew last month, a generic model is the right tool and buying anything more is a waste of money.
What does purpose-built AI actually add?
A structured, connected layer of your firm's own knowledge that any model can reason over properly, whether you're screening deals for a private equity fund, tracking relationships at an investment bank, or running diligence for a VC portfolio. DealSage's ontology holds Deal, Contact, Organisation and custom objects, each field traced back to its source, and reaches you through email, a live-linked Excel plugin, or your own LLM over MCP. That last part matters: this isn't a pitch to abandon ChatGPT. It's a way to give it, or Claude, or whichever model you prefer, something real to work with instead of a blank page. See how the platform is built for the full picture.