Reduce token waste in AI browser automation by compressing DOM noise

Many teams building LLM-driven agents that navigate web UIs are wasting tokens because raw DOM snapshots include abundant boilerplate, repeated attributes, and invisible structure that inflate prompt sizes and inference costs. This is most acute for AI/automation engineers and platform teams running hundreds to thousands of workflows daily, translating into measurable spend on inference and slower feedback loops. A practical product is a Playwright middleware that semantically compresses DOMs at the edge using lightweight encoder models and heuristics, returning concise, fidelity-tunable representations or diffs to LLMs and exposing audit hooks so developers can validate what was dropped. Integration would be a one-line plugin for Playwright with SDKs for agent frameworks, delivered as an open-source reference plus a hosted SaaS that charges on token-savings or per developer seat. The market is attractive now: roughly 10M developers spend about $1,200/year on tooling (a $12.0B addressable market), Playwright adoption creates a clear integration surface, serverless/edge inference makes inline compression viable, and teams are increasingly running LLM agents in production—hence a Market Score of 88/100 and Revenue Potential of 86/100. You can stand out by optimizing for developer ergonomics, providing measurable fidelity guarantees and transparency, offering an open-core implementation to build trust, and embedding deeply with Playwright and popular agent runtimes. Key challenges are reliably preserving task-relevant semantics across diverse sites, keeping added latency within strict SLOs, and competing in a medium-competition landscape where open-source and cloud vendors can replicate core features. If you can demonstrate consistent token reductions on real workflows, low-friction integrations, and a clear hosted value-add, this idea is worth pursuing; otherwise the technical and go-to-market hurdles could limit adoption.