BP Software Solutions is a company being built around one focus: real-time, AI-generated recommendations that scale infrastructure up and down as demand actually shifts — so businesses stop guessing at capacity and start running on what's actually needed, right now.
Recommendation systems are our core capability — for products, content, and the experiences businesses build around them. Our current focus is applying that same approach to infrastructure itself: an AI that recommends, in real time, exactly how much infrastructure a business needs.
Personalized, real-time recommendation engines using collaborative filtering, content-based filtering, and deep learning — designed to help businesses increase engagement, conversion, and customer retention.
Infrastructure optimization built on the same core capability: an AI system that generates real-time recommendations for exactly how much compute is needed right now, and acts on them — scaling up or down automatically, rather than running on fixed rules or manual guesswork.
Most companies treat infrastructure scaling as background plumbing — a set of fixed rules bolted on to keep things running. We treat it as its own recommendation problem: an AI system whose job is to recommend, in real time, exactly how much infrastructure is needed right now — and to act on that recommendation automatically.
Compute and storage costs don't scale linearly with demand — they spike unpredictably as traffic and data grow, and most teams only discover the ceiling after they've already hit it. Get it wrong and it shows up as slow systems, ballooning cloud bills, or capacity that works in a demo but buckles under real load. Getting it right means infrastructure that scales with what's actually happening, not with what was guessed months ago.
Most infrastructure scaling today runs on static rules — fixed thresholds, manual capacity planning, autoscaling triggered by lagging metrics like CPU or memory after the spike has already started. That's reactive by design: it responds to what already happened, not what's about to happen. Teams end up over-provisioning "just in case," or under-provisioning until something breaks in production. Either way, the business pays for it — in cost, in performance, or both.
We don't provision for peak load and hope it's enough. Our system continuously recommends — and acts on — exactly how much infrastructure is needed right now, scaling compute up or down in real time as demand actually shifts, instead of running on fixed capacity rules or manual guesswork.
Efficient infrastructure lowers a client's cost of running these systems — but as AI workloads consume more compute and more tokens on more servers every year, that same efficiency has a real effect on the energy those servers draw. Building lean isn't only good business. It's the more responsible way to build.
Focus on U.S. industries where personalized experiences matter most, and build our first client relationships.
Ongoing R&D to keep our recommendation models and infrastructure at the technological forefront.
Adapt our solutions for global markets as our U.S. presence solidifies.
Our founder has spent over 15 years living the exact problem this company exists to solve — architecting data infrastructure and machine learning systems across healthcare, banking, and e-commerce, and leading multiple agile teams through real growth on real platforms. That's where the pattern became impossible to ignore: the scaling walls that show up without warning, the budget conversations that follow an over-provisioned cluster, and the outages that follow an under-provisioned one — repeating across industries, platforms, and teams. That leadership has been recognized with industry awards and professional distinctions along the way.
Seeing that same pattern play out again and again, across settings, is what's driving the interest to build something that removes the guesswork entirely: a system that recommends the right infrastructure, at the right moment, instead of leaving that call to instinct or fixed rules.
We're not open for engagements yet, but we're glad to hear from potential future partners, collaborators, or anyone who'd like to stay in the loop as things develop.