Information overload for professionals — role-aware relevance engine

Many knowledge workers—estimated 200 million globally across finance, legal, consulting, and product roles—spend a material portion of their day filtering noise from signals, which reduces effective decision time and leads to missed opportunities. The problem is role-specific: a VP of product, an M&A analyst, and a regulatory compliance officer each need different slices of the same incoming filings, news, and internal comms, and current tools deliver volume rather than relevance. A practical product is a role-aware relevance engine that combines API-accessible structured feeds (filings, funding, policy) with AI-generated, role-focused digests and provenance metadata, and pushes prioritized signals directly into workflows like Slack and CRMs. With a $30.0B addressable market (200M professionals × $150 ARPU/year), a Market Score of 92/100 and Revenue Potential of 88/100, timing is favorable: recent advances in summarization, proliferating APIs, and demand for embedded workflows lower technical and go-to-market barriers. This can stand out by emphasizing explainable relevance—signal provenance, configurable role profiles, and closed-loop feedback that tunes precision for each persona—rather than raw summarization volume. Strengths include clear unit economics if you reach $150 ARPU and defensibility from proprietary signal layers and customer-labeled relevancy data; challenges are real and include preventing model hallucination, securing data licensing and integrations, and executing enterprise pilots to demonstrate measurable time-to-insight improvements.