Stop OS/app bloat stealing RAM — lightweight AI agent to reclaim & auto‑offload

Many knowledge workers and IT teams on roughly 200M target devices today suffer real productivity and cost pain as OS and app bloat steadily consume RAM, causing sluggish multitasking, higher battery use, and unnecessary hardware upgrades—this is especially acute on 8–16GB laptops used for hybrid work and on managed fleets where support costs scale. The consequence is wasted capacity and avoidable refresh cycles that compound across organizations and individual users. You could build a lightweight AI agent (single-digit MB footprint, on-device inference) that classifies per-process memory importance with compressed edge models, reclaims idle working sets, and safely auto-offloads cold memory to compressed swap or optional secure cloud, exposing granular user controls, admin policies, telemetry, and one-click recovery. Privacy-first on-device inference plus an enterprise console and a small plugin SDK for app-specific handlers would make the product usable for both consumers and managed deployments. The market looks attractive now: an addressable market of about $6.0B (200M devices × $30 ARPU/year) with a high market score (90) and revenue potential (88) is enabled by a growing backlash against software bloat, mature tiny ML and model-compression techniques, and hybrid work economics that favor squeezing more life out of existing hardware. Competition is medium, so go-to-market can leverage OEM or MSP partnerships and a clear compliance story to accelerate adoption. To stand out you’ll need rigorous per-process predictive policies, explainable decisions and strong privacy guarantees, but expect hard engineering work—obtaining safe low-level OS hooks across Windows/Mac/Linux, avoiding false positives, keeping compatibility with security tooling and OS updates, and maintaining cross-platform drivers will be the principal technical and operational challenges.