DeepAdapt is turning AI inference from a repeated GPU expense into a runtime learning system that can answer known work on CPU.
Adaptive + Continual + Intelligence: ACI is a new runtime-intelligence layer in AI infrastructure, that sits above the model.
The problem it targets is that most LLM apps keep calling the model even when the system has already seen the same correction, rule, fact, or workflow before.
ACI sits between the application and the model, receives every request first, and checks tenant-scoped learned state, rules, memory, evidence, and confidence before sending anything to the model.
ACI serves directly on CPU when it knows enough, escalates to the model or execution path only when the request is novel or confidence is insufficient, then writes back corrections, outcomes, or evidence-linked updates.
I am introducing DeepAdapt and our runtime intelligence ACI.
The industry is scaling intelligence by spending more computation. DeepAdapt is scaling intelligence by retaining more experience.
Read our core thesis below.
