APRABot was founded by supply chain and AI practitioners who spent years watching good companies make bad decisions because their demand signal was broken. We set out to fix that — permanently.
Supply chains break at the edges — when demand shifts faster than the plan, when inventory is in the wrong place at the wrong time, when finance and operations are arguing about a number instead of a decision.
APRABot exists to eliminate that gap. We give every team — from the warehouse floor to the boardroom — a single, trusted, continuously updated picture of what demand will do next. So plans are built on signal, not gut feel.
Our founders spent years building and operating demand forecasting systems at scale.
APRABot is founded to bring that same forecasting rigor to companies everywhere.
A forecast that isn't accurate isn't a forecast — it's noise with a confidence interval. We obsess over error metrics because every percentage point is real money for our customers.
A black-box model doesn't get adopted. Every APRABot forecast comes with driver attribution — planners need to know why, not just what, before they'll stake a plan on it.
Demand doesn't wait for the monthly S&OP cycle. Our platform reforecasts continuously so the signal is always current, and our team moves fast to ship what customers need.
Your demand patterns are your competitive advantage. We are contractually and architecturally committed to ensuring your data is never used for any purpose beyond your own forecasts.
The best algorithm in the world fails if planners can't use it. We invest as much in UX as we do in model architecture — because adoption drives outcomes.
Our commercial model is tied to customer outcomes. If your inventory doesn't come down and your accuracy doesn't go up, we haven't done our job.
Our team combines deep supply chain domain expertise with production ML engineering — the two things you need to actually solve this problem.
Deep experience in supply chain and demand forecasting at scale before founding APRABot.
PhD-level researchers and engineers specialising in gradient boosting, time-series forecasting, and multi-agent AI systems for structured supply chain data.
Practitioners with hands-on experience in S&OP, inventory management, and demand planning across Fortune 500 and high-growth companies.
We're always looking for people who care about accuracy, explainability, and making supply chains smarter.