Executive Summary

Many product and engineering leaders in the 2,000,000 software-producing organizations worldwide struggle with building the wrong thing: disparate signals, biased roadmaps, and insufficiently rigorous discovery lead to wasted developer time and escalated cloud and personnel costs. The consequence is delayed outcomes and sunk engineering spend that is increasingly hard to justify to execs demanding measurable ROI. The product would be an AI-guided problem-discovery and decision platform that ingests user research, support tickets, product telemetry, and stakeholder interviews to synthesize insights, generate prioritized, testable hypotheses, and output experiment designs with clear success metrics and integration to A/B and issue-tracking systems. It would provide repeatable workflows, explainable rationales for recommendations, and templates that turn LLM-synthesized findings into executable experiments for PMs and engineers. This is an attractive moment: a $40.0B addressable market (2,000,000 orgs × $20,000 ACV) with high investor and buyer interest (Market Score 92/100, Revenue Potential 84/100) because large language models now make rapid synthesis of qualitative and quantitative signals feasible, engineering unit costs are rising, and organizations are shifting to outcome-based roadmaps that require evidence. Those three trends reduce the cost of discovery and increase the value of tools that can demonstrably reduce wasted build work. To stand out you must deliver rigorous, auditable recommendations that map directly to engineering tasks and measurable experiments, emphasize deep integrations and data governance, and prioritize explainability so product leaders can trust automated suggestions. Strengths are high recurring revenue potential and a clear pain point to target; challenges include medium competition, the technical work of robust data integration, the need to validate statistical soundness of experiments, and enterprise sales complexity—so early focus on demonstrable ROI and conservative claims will be essential.

Executive Summary

Market Opportunity

Stop coding the wrong thing — AI-guided problem discovery & decisions targets a $40.0B = 2,000,000 software-producing orgs x $20,000 ACV (annual product-discovery & decision tooling) total addressable market with medium saturation and a year-over-year growth rate of 18% estimated growth in product-management & developer tooling spend.

Key trends driving demand: AI-assisted product management -- LLMs can rapidly synthesize user research and generate testable hypotheses, lowering the cost of discovery.; Rising engineering unit costs -- higher developer salaries and cloud costs force teams to validate ideas before building.; Shift to outcome-based roadmaps -- execs demand measurable ROI and evidence-based decisions, increasing demand for validation tooling.; Telemetry proliferation -- product analytics and observability give measurable signals that can be used to predict experiment outcomes.; Remote & distributed teams -- misalignment across PM/Design/Eng increases the need for structured decision frameworks and single sources of truth..

Key competitors include productboard, Aha!, Atlassian (Jira & Confluence), Miro, Consulting & UX agencies (e.g., boutique product discovery consultancies).

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Stop coding the wrong thing — AI-guided problem discovery & decisions

Executive Summary

Stop coding the wrong thing — AI-guided problem discovery & decisions

Executive Summary

Market Validation

Market Opportunity

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