Mathematical methods and products that find and eliminate bottlenecks across AI pipelines and quantitative finance. Drop-in where possible, deep integration where it matters — no retraining required.
Every result below is a validated method in the P(t) portfolio. Governor is the first packaged into something you adopt directly — a control layer for multi-agent systems. More will follow the same path: proven in the papers, then productized.
Agent swarms chain calls — one agent’s output feeds the next. When an agent goes wrong early, the rest confidently build on its garbage and you pay full price for all of it. Governor watches each agent’s health, catches the cascade as it starts to spread, and throttles the doomed work before it finishes — so you spend less, or spend the same budget on a better answer.
P(t) = E(t) × W(t) is a stochastic health signal that works across every layer of the ML lifecycle because the underlying math is fundamental, not tuned:
Is this process still worth continuing?
Methods operate at every layer of the stack — some sit outside the model as drop-in API layers, others operate inside the training and inference process for deeper optimization. The same math applies across ML pipelines and quantitative finance.
Patent pending · 21 published papers · All code public
We build mathematical methods and products purpose-built for specific bottlenecks — training runs, scaling decisions, inference pipelines, retrieval systems, chain selection, backtesting, live risk allocation. Most optimization work treats these as separate problems requiring separate tools.
We disagree. The patterns repeat. The math transfers.
If the numbers don't hold on your data, we don't have a deal.