Investors asked it, customers asked it, and we asked ourselves. Here is the direct answer.
Information is not created by inference
One simple principle answers this question. Information that is not in the input is not in the output. Your settlement rules, your points policy, the history behind your legacy are not on the internet. They are not in the training data. So even when GPT-7 arrives, that model will not know your systems. A better model just answers more fluently about what it does not know.
The new-hire analogy
Say you hired the smartest person in the world. On their first day, with no handover, you say "fix our settlement system." Could they? No. It is not a problem of intelligence but of knowledge. Better models are like a smarter new hire arriving every time. The handover problem has never been solved. The handover is exactly what Platty sells. For people and for AI alike.
Even with all the information in, signal-to-noise makes it fail
"Context windows keep growing, so why not just feed in the entire codebase?" That is the next version of the objection. It does not work. No matter how smart the model is, a low signal-to-noise ratio (SNR) in the input blurs the output. The more irrelevant code, stale documents, and contradictory information you mix in, the more the model assembles plausible wrong answers out of the residue. Hallucination is a flaw in the input before it is a flaw in the model.
Put a person in the same position and it is obvious. Hand an IQ-200 specialist a project and throw a few dozen mutually contradictory requirements at them at the same time. The output will be strange. Not because they lack intelligence, but because the input was polluted. This structure does not change as models get smarter.
So context should not be given in bulk. It should be verified, and given only as far as it is needed. That is why Platty grades documents, puts only the correct ones into the SSOT, and hands over just the relevant portion for each question. Context management is not something models outgrow as they improve. It is something that has to get more precise.
Bottlenecks move, they do not disappear
When models were weak, the bottleneck was reasoning. Reasoning is now good enough, and that exposed the real bottleneck: the material the AI works with, that is, accurate context about the company. The better models get, the wider this gap grows. Between two companies using the same model, what separates results is not the model but what you give the model.
So Platty does not compete with the models
Platty is the layer that makes whatever model arrives next understand your company. Better models are not a threat to us, they are a tailwind. The better the reasoning, the more value accurate context creates.