MiroFish
VisitAI simulation chat for scenario prediction
MiroFish introduction
Turn text, PDF, MD, and TXT into graph building, simulation, reporting, and follow-up chat through one AI prediction workflow.
- Website:
- mirofish.homes
MiroFish overview
MiroFish is an AI simulation chat tool where users ask a question directly and the system handles seed → simulation → report as one continuous prediction workflow. It starts with a plain-language question, optionally adds PDF, Markdown, or text files as reality seeds, then runs multi-agent graph building, simulation, and reporting behind the scenes while keeping the user inside a single conversation. Results are delivered as structured cards with summaries, report entry points, and follow-up paths.
MiroFish features
- Text-first question entry
- Optional PDF, Markdown, and TXT attachments for grounding
- Multi-agent graph building, simulation, and reporting workflow
- Single conversational interface from seed to report
- Structured result cards with summary, report entry point, and follow-up path
- Knowledge graph extraction of actors, relationships, pressures, and factual anchors
- Agent simulation across short-form and threaded social surfaces over multiple rounds
- Prediction report with turning points, risks, confidence signals, and follow-up questions
- Deep interaction: continue asking questions against the generated scenario
- One continuous workflow instead of isolated answers
Questions about MiroFish
MiroFish pros
- Can start with text alone; file uploads are optional but supported for grounding
- Supports PDF, Markdown, and TXT as source material
- Combines graph building, simulation, reporting, and follow-up chat in one workflow
- Result cards provide a summary, report entry point, and follow-up path under each answer
- Reports include likely trajectory, key actors, risk signals, evidence lines, and next questions
- Allows continued questioning against the generated scenario instead of a static answer
- Useful for reaction-driven planning scenarios where static forecasts may miss feedback loops
- Positioned as exploratory decision support, not a guaranteed forecast
MiroFish limitations
- Outputs are not guaranteed forecasts and require judgment, analytics, and real-world validation
- Simulations are only as grounded as the provided seed material; files work best with concrete actors, incentives, constraints, or prior context
MiroFish use cases
- Campaign test: pressure-test a launch narrative before it goes public
- Pricing reaction: model customer sentiment and objection paths before a price change
- Policy stress test: find groups, incentives, and loopholes in a policy rollout
- Market narrative: stress-test market stories with feedback loops between analysts and public discourse
- Crisis response and creative continuation (scenarios with human reaction loops)
- Planning moments where simulated audiences, incentives, and narrative paths reveal what a normal forecast misses
Who MiroFish is for
- Teams and individuals planning launches, pricing changes, policy rollouts, or market narratives
- Analysts who need exploratory decision support for reaction-based scenarios
- Practitioners testing campaigns, pricing, policies, or market stories before committing