Scenario Lab

Most high-stakes decisions get made blind. You launch the product, send the campaign, or submit the proposal — then find out how people actually react. The traditional ways to preview that reaction — focus groups, research panels, consultancy reviews — are slow, expensive, and only ever capture a handful of voices. The challenge was to give a founder or team a way to pressure-test a decision against a realistic spread of stakeholders in minutes, with enough depth and structure that the output could genuinely change what they do next — not just a chatbot's opinion.

Scenario Lab is built as a mission control deck for decisions. You pick one of six purpose-built modules — Market Reaction, Campaign Risk, Customer Personas, Grant Proposal Risk, Strategy What-If, or Public Reaction Forecaster — and an AI assistant walks you through setup in a short conversation. From there the engine assembles a panel of distinct stakeholder agents, each with their own age, role, goals, concerns, biases, and decision style, grounded in the real regional market. The agents react to your project across several rounds; every response is scored and sentiment-tagged, then synthesized into a single structured report — an overall risk score, key findings, objections raised, opportunities identified, the strongest objection, and a set of strategic recommendations and next steps. When a finding needs probing, you open a live chat with any individual agent and ask follow-ups in their own voice.

The product turns a process that normally takes weeks into one that runs in minutes. A single run spins up a nine-person stakeholder panel, plays out three rounds of reactions, and returns a 0–100 risk score with a full sentiment breakdown, prioritized objections, and concrete recommendations. The sample Market Reaction run scored 78/100 (high risk) at 7.3/10 agent confidence — surfacing, before any money was spent, that the real barrier wasn't interest but proving depth and cost-effectiveness to budget-conscious buyers.