Organize product research & AI-assisted decision canvas

Product teams in mid-market and enterprise companies routinely struggle to make timely, evidence-backed product decisions because research and discovery artifacts are fragmented across analytics, notes, prototypes, and ticketing systems. This problem affects an estimated 2.0M product teams/businesses and maps to a $6.0B addressable market (2.0M teams x $3,000 ACV) for tools that consolidate research and decision workflows. The consequence is slow prioritization, duplicated research, and weakening cross-functional trust in decisions. The proposed product is a unifying decision canvas that ingests signals from existing tools, synthesizes unstructured research with LLM-assisted summarization and argumentation, and outputs auditable recommendations, trade-off analyses, and decision threads. Built with extensible integrations, developer APIs and an option for self-hosting, it would target a ~$3,000 ACV seat or team license while instrumenting ROI through shorter decision cycles and clearer evidence trails. The timing is favorable: LLMs now make automated synthesis and counterfactual argumentation feasible, the fragmentation of knowledge across apps heightens demand for a synthesis layer, and a strong open-source preference creates room for hybrid or self-hosted deployments. Competition is medium—there are incumbents in knowledge and product tools—so the product must differentiate through rigorous evidence linking, auditability, enterprise-grade data controls, and workflow templates; the opportunity rates high (Market Score 90/100, Revenue Potential 80/100) but success will hinge on enterprise sales execution, model governance, and convincing teams to change entrenched workflows.