MiroFish

AI Simulation Chat for Scenario Prediction

Enables users to ask complex “what‑if” questions and receive continuous, AI‑driven forecasts, maintaining a conversational flow similar to ChatGPT.

Text‑First Interaction with Optional Attachments

Starts the prediction process with natural language input, allowing users to add supporting files only when needed, preserving chat speed and simplicity.

Multi‑Agent Orchestration

Coordinates graph building, simulation execution, and report generation behind the scenes, delivering integrated outcomes without leaving the chat interface.

Structured Result Cards

Automatically generates concise result cards after each answer, summarizing key insights and providing direct links to detailed reports and follow‑up actions.

Predictive Analytics Across Domains

Applies the prediction engine to diverse scenarios—price changes, brand shifts, policy debates—offering actionable forecasts on sentiment, narrative spread, and stakeholder reactions.

• Market analysts: Simulate quarterly price changes to forecast customer sentiment and narrative spread.
• Brand managers: Predict public opinion shifts when altering spokespersons or messaging strategies.
• Policy researchers: Identify early supporter and opponent groups for emerging public policy debates.
• Business strategists: Conduct multi-agent scenario simulations without leaving a single chat interface.
• Data scientists: Attach supplemental files to text queries for enriched prediction modeling.

What is the MiroFish Prediction Engine?

MiroFish Prediction Engine is an AI‑driven simulation chat platform that allows users to pose scenario‑based questions in natural language. It processes the query, optionally incorporates attached data files, runs multi‑agent simulations, and returns structured result cards that summarize predictions and suggest next steps.

How does MiroFish handle text‑first interactions?

In a text‑first workflow, users begin with a plain‑language question. MiroFish then decides if additional attachments are needed, preserving conversational speed. This design eliminates the need for separate data upload steps, making scenario prediction feel like a continuous chat with ChatGPT‑style responsiveness.

Can MiroFish predict business outcomes such as price changes?

Yes. By entering a question like “If a product raises its price next quarter, how will customer sentiment and narrative spread evolve?” MiroFish runs a graph‑based simulation that models market reactions, sentiment dynamics, and narrative propagation, delivering a concise prediction report within the chat interface.

What role do multi‑agent simulations play in MiroFish?

MiroFish orchestrates multiple AI agents behind the scenes to handle graph construction, simulation execution, and report generation. This multi‑agent architecture enables complex scenario analysis—such as public opinion shifts after a brand spokesperson change—while keeping the user experience within a single conversational thread.

How are results presented to the user?

After each prediction, MiroFish provides a result card positioned directly below the answer. The card includes a summary of key insights, a link to a detailed report, and suggested follow‑up questions, allowing users to quickly assess outcomes and continue the analytical conversation.

• Identify the prediction question, phrasing it clearly in natural language, because MiroFish operates as a text‑first AI simulation chat for scenario prediction.
• Open the MiroFish interface, locate the chat box, and enter the question; the system automatically initiates the simulation workflow without requiring initial attachments.
• Review the optional “Attach Files” prompt; upload supporting documents only if they add contextual data, ensuring faster processing when no files are needed.
• Observe the multi‑agent engine generate a result card containing a concise summary, a link to the full report, and suggested follow‑up actions.
• Click the report entry point to expand detailed simulation outputs, including sentiment trajectories, narrative spread, and stakeholder analysis relevant to the query.
• Apply the insights to strategic planning, adjusting pricing, messaging, or policy approaches according to the predicted public response demonstrated by MiroFish.