Overview
Scenario Simulator lets users pose "what if" questions and receive evidence-grounded scenario analyses drawn from their actual document corpus. It bridges the gap between backward-looking business intelligence and forward-looking strategic planning.
Rather than relying on gut feel or generic market research, teams get impact analyses built from their own financial data, contract terms, historical precedent, and strategic memos — all with cited sources and confidence intervals.
The Problem
- Traditional BI tells you what happened but never why or what next
- Only 27% of analytics projects produce actionable insights
- 70–75% of dashboards are ignored after their first view
- Executives need forward-looking analysis grounded in organizational context, not generic market data
- Scenario planning today is manual, expensive, and rarely refreshed
How It Works
- User poses a hypothetical question — "What if we lose our top 3 clients?" or "What if raw material costs increase 20%?"
- Decomposition — The system breaks the question into sub-questions: financial impact, operational impact, strategic implications, historical precedent
- Multi-source retrieval — For each sub-question: document retrieval (relevant docs) + text-to-SQL (structured data) + web search (external context)
- Multi-agent synthesis — One agent per dimension; an orchestrator assembles findings into a coherent scenario analysis
- Output as a structured report with cited evidence, confidence scores per dimension, key assumptions, and recommended actions
User Story
A CFO asks: "What if our largest supplier exits the European market?" The Scenario Simulator retrieves relevant supply chain contracts, vendor assessments, financial impact models from uploaded spreadsheets, and past disruption post-mortems. It produces a multi-dimensional analysis: estimated revenue impact ($2.1M–$3.4M), timeline to alternative sourcing (4–6 months based on similar past transitions), regulatory complications (2 contracts reference EU-specific compliance), and strategic options ranked by feasibility. Each claim links back to the source document.
Complexity & Timeline
| Aspect | Detail |
|---|---|
| Complexity | Complex |
| Estimated Build | 5–6 weeks |
| Platform Dependencies | Text-to-SQL, Retrieval, Web search, Experiences (report rendering), Surfaces |
| New Infrastructure | Multi-agent orchestration framework, confidence scoring system |
Target Clients
- Personas: CFOs, Strategy Directors, Board Members, M&A teams, Investor Relations
- Verticals: Private Equity, Corporate Development, Consulting, Manufacturing, Financial Services
- Pitch: "Ask 'what if' and get answers grounded in your actual data — not guesswork."
Revenue Potential
High-value feature targeting executive buyers with significant budget authority. Natural fit for strategy and planning use cases where the alternative is expensive consulting engagements ($100K+ per scenario analysis). Supports premium per-query pricing model for compute-intensive analyses. Competitive landscape: no tool currently combines structured and unstructured data for scenario analysis.
Feature Synergies
- Simulation Sandbox — Simulator reasons over existing data; Sandbox lets you change the data landscape and observe second-order effects
- Predictive Narrative — "Here's the trajectory → now what if X happens?" — narrative trends feed into scenario assumptions
- Decision Journal — Log scenario-informed decisions and track outcomes to calibrate future analyses
Risks & Open Questions
- Multi-agent orchestration is computationally expensive — need clear pricing model for heavy usage
- Confidence scoring for qualitative projections is inherently subjective
- Users may over-trust quantitative-looking outputs from fundamentally qualitative analysis
- Requires sufficient document density to generate meaningful analyses — thin corpora produce weak results