MiroFish

MiroFish is an AI simulation chat tool built for scenario prediction. It turns text, PDF, Markdown, and TXT material into a continuous prediction workflow that covers graph building, simulation, reporting, and follow-up chat. Instead of offering a single isolated answer, MiroFish keeps structure, personas, social dynamics, and report synthesis in one sequence, so users can ask questions against a generated world rather than stopping at a static response. The product is positioned as a text-first experience: users start with a plain-language question and then decide whether supporting files are necessary, without losing the speed of chat. The system handles seed material, simulation, and report generation as one orchestrated process. A knowledge graph extracts actors, relationships, pressures, and factual anchors so that agents reason from structure. Agent simulation then lets personas interact across short-form and threaded social surfaces over multiple rounds. Finally, a prediction report condenses emergent behavior into turning points, risks, confidence signals, and follow-up paths. Result cards appear below each answer with a summary, a report entry point, and a follow-up path. The workflow is organized into five steps: seed material, knowledge graph, agent simulation, prediction report, and deep interaction. MiroFish highlights several use cases where reaction matters more than a static answer. Campaign testing lets teams pressure-test a launch narrative before it goes public, simulating how audience groups might amplify, resist, or reinterpret a message before spend is committed. Pricing reaction explores the friction behind a price increase by modeling customer sentiment, value perception, and likely objection paths across segments. Policy stress testing finds groups, incentives, and loopholes in a rollout, using simulation as a tabletop exercise for controversy, coalition formation, and second-order reactions. Market narrative analysis watches narrative, incentives, and sentiment interact, stress-testing stories where spreadsheets miss the feedback loop between analysts, retail attention, and public discourse. The site also offers practical playbooks. It advises writing a sharper prediction prompt by naming the decision, the audience, the likely trigger, and the time horizon, because a narrow question gives the simulated world less room to drift. It suggests using files as reality seeds, noting that PDF, Markdown, and text files work best when they contain concrete actors, incentives, constraints, or prior context, such as a strategy memo, product FAQ, policy brief, market note, or customer research summary. It also encourages reading the report like a rehearsal and treating output as decision support, looking for resistance signals, narrative bridges, and assumptions worth checking with real data. A report preview shows the expected structure: an executive summary, risk signals, narrative paths, and follow-up questions. MiroFish is clear that it is not a guaranteed forecast. It is exploratory decision support, a way to rehearse plausible reactions before using judgment, analytics, and real-world validation. The product is available on the web and can be started directly from the site.