Stop generic bot traces — train a lightweight GRU on your mouse data

Executive Summary

Many developer and automation teams struggle with generic bot traces that make browser-driven workflows easy to detect and block, a problem that affects an estimated 200,000 teams running enterprise automation, testing, and RPA and leads to flaky tests, failed scrapes, and higher operational costs. False positives and evolving server-side fingerprinting techniques force teams to choose brittle workarounds or expensive third-party services. You could build a privacy-first SDK that trains a compact GRU on a team's mouse movement, timing, and optional engagement signals, then runs the quantized model locally in the browser or a lightweight agent to synthesize human-like traces. Aim for a GRU in the ~10k–50k parameter range, quantized to under ~200KB with inference latency in the 5–20 ms range on common devices, and package connectors for Playwright, Selenium, and major RPA platforms along with server-side evaluation and UX tools for opt-in collection and debugging. This keeps user data on-device, reduces network dependencies, and makes adoption simpler for dev teams. The timing is favorable: tiny-ML runtimes and broader automation adoption mean enterprises are more likely to pay for privacy-preserving, on-device personalization, and the TAM is roughly $12.0B (200,000 teams × $60K ACV). Differentiation comes from per-team personalization, low-latency on-device inference, and tight integration with existing toolchains, but realistic challenges remain—collecting representative behavioral data, handling model drift and adversarial detection, and persuading security teams to adopt a new agent—so plan for strong evaluation hooks and a conservative compliance posture.