Overview

AlphaGraph is an AI-powered scenario simulation platform built for strategy, crisis, and policy teams. Describe any scenario in natural language, and AlphaGraph builds a living simulation with autonomous stakeholder agents, a contextual knowledge graph, and a structured intelligence report.

Instead of guessing how stakeholders will react, you simulate it. AlphaGraph generates realistic multi-agent interactions across social channels, captures emerging narratives, and distils everything into actionable insights your team can use before making real-world decisions.

How it works

Every simulation follows a four-stage pipeline. Each stage builds on the previous, transforming your scenario description into a comprehensive intelligence report.

Use cases

• Crisis preparedness — Simulate how a product recall, data breach, or leadership change would play out in public discourse before it happens.
• Strategic decision-making — Test how a merger announcement, market entry, or pricing change would be received by stakeholders.
• Policy impact analysis — Model how new regulations, government announcements, or policy shifts would ripple through affected communities.
• Communications planning — Evaluate messaging strategies by simulating public and media reactions before launch.

AI Copilot

The AlphaGraph copilot is your starting point. Describe your scenario in plain language and the copilot guides you through the setup process — enriching your input with contextual research, evaluating readiness, and preparing everything for launch.

Contextual research

When you describe a scenario, the copilot automatically searches for relevant context — recent news, market data, regulatory filings, and public discourse. This enriched context feeds into the knowledge graph and helps agents behave realistically.

Readiness assessment

Before launching, the copilot evaluates your scenario across several dimensions: scenario clarity, stakeholder mapping, constraint definition, objective specificity, seed material quality, and output specification. When readiness reaches the threshold, you can launch the simulation.

Knowledge Graph

AlphaGraph automatically builds a knowledge graph from your scenario context. This graph captures the entities, relationships, and dynamics that matter — and serves as the foundation that grounds agent behaviour in real-world context rather than generic patterns.

Entity types

The graph identifies and classifies entities into categories such as organizations, products, people, policies, markets, media outlets, events, and locations. Relationships between entities capture dependencies, affiliations, competitive dynamics, and regulatory connections.

Interactive visualization

During and after the simulation, the knowledge graph is displayed as an interactive visualization in the simulation panel. You can explore entity clusters, examine relationships, and understand how the scenario's ecosystem is structured.

Stakeholder Agents

AlphaGraph generates a diverse cast of AI agents representing the stakeholders who would realistically engage with your scenario. Each agent has a unique persona with a defined role, stance, communication style, and relationship to the scenario.

Role diversity

Agents span the full stakeholder landscape: executives, regulators, media and journalists, industry analysts, investors, activists, and the general public. The distribution ensures a realistic cross-section of perspectives — including contrarian and provocative voices.

Stance spectrum

Each agent is assigned a stance that reflects their genuine interests and incentives — not a predetermined outcome. Stances range from supportive to alarmed, ensuring that the simulation captures the full spectrum of realistic reactions.

Privacy by design

AlphaGraph is designed for GDPR compliance. When a scenario is created, an AI-powered named entity recognition system automatically detects real person names and replaces them with institutional role titles (e.g. "the Vooruit chairman" instead of a politician's name). This sanitization happens at write time and is stored permanently — every downstream consumer (reports, PDF exports, follow-up simulations) reads sanitized data by default. Private citizens receive fictional identities. An audit trail logs every name replacement for compliance verification.

Simulation Engine

The simulation engine runs autonomous agent interactions across parallel social channels. Agents post, comment, reply, and debate — generating emergent narrative dynamics that reveal how public discourse would evolve around your scenario.

Multi-channel simulation

Simulations run across multiple social platforms simultaneously. Each channel has its own dynamics — short-form discourse on one platform, long-form discussion threads on another. Platform-specific progress is tracked independently.

Configurable depth

You control the simulation depth by setting the round count. More rounds produce richer narrative dynamics and more data points for analysis. The simulation panel displays real-time progress with per-channel round counters and a live action feed.

Emergent dynamics

Agent interactions are not scripted. Each agent independently decides what to post, who to respond to, and how to react — based on their persona, the evolving conversation, and the knowledge graph context. Narrative coalitions, viral moments, and sentiment shifts emerge organically.

Intelligence Report

After the simulation completes, a multi-stage AI analysis pipeline transforms raw interaction data into a structured intelligence report. The report follows the Pyramid Principle — leading with the key insight, then building the argument through evidence-backed strategic pillars.

Analysis pipeline

The report is generated through a seven-stage process: data extraction, strategic synthesis, coalition analysis, visual design, narrative writing, fact-checking, and editorial review. Each stage uses specialised AI models optimised for its task. The pipeline runs in parallel where possible — coalition analysis and visual design execute simultaneously.

Coalition graph

Before the report is written, AlphaGraph identifies stakeholder coalitions — clusters of agents who share stances and coordinate on themes. Every key finding in the report is linked to specific agent actions via evidence IDs, making claims fully traceable back to raw simulation data.

References appendix

Every claim in the report is backed by numbered citations that link to specific agent actions. The references appendix at the end of the report lists all cited actions, grouped by coalition, with the agent name, round number, platform, and a content excerpt. This makes every finding fully auditable.

Confidence scores

Each key finding carries a computed confidence score (HIGH, MEDIUM, or LOW) based on how many agents contributed supporting actions and how many simulation rounds the evidence spans. These scores are rendered as colored badges in both the web view and the PDF export — green for HIGH, amber for MEDIUM, red for LOW.

Theme classification

Actions are classified into narrative themes by the data extraction stage. Each action is assigned a primary theme; actions with ambiguous or mixed themes are explicitly grouped under a separate category. A methodology footnote in the report documents this approach for transparency.

Influence-weighted metrics

Agent impact is measured by influence score rather than raw action count. The formula accounts for role credibility (regulators and journalists carry more weight than anonymous participants) and coalition alignment. The methodology is transparent and published in every report.

Interactive visualizations

Reports include interactive charts — activity timelines showing discourse evolution, stakeholder analysis breakdowns, platform comparisons, and narrative cluster maps. Every visualization is tied to a specific strategic argument.

Recommendations

Each report concludes with prioritised, actionable recommendations. Recommendations are grounded in simulation evidence, tagged with severity levels and suggested timelines (immediate, 24h, 1 week, ongoing).

Outcome tracking

Each key finding includes a feedback link. After events unfold in the real world, you can report whether findings materialised, partially materialised, or did not occur. This ground-truth data helps measure simulation accuracy over time.

Deep Analysis

After the report is generated, you can continue the conversation. Ask questions about specific findings, explore narrative dynamics in depth, or interview individual agents to understand their decision patterns.

Ask anything

The post-simulation copilot has full access to the simulation data, agent profiles, and report content. Ask about specific stakeholder reactions, request comparisons between agent groups, or dive deeper into any section of the report.

Agent interviews

Tag any agent with @ in the chat to interview them directly. The agent responds in character, explaining their motivations, reactions, and decision-making process. You can tag multiple agents in the same message for a group interview — each agent responds with their own perspective.

Follow-up Simulations

AlphaGraph supports simulation chaining — run a follow-up scenario that builds on the findings of a previous run. The knowledge graph and agent personas carry forward, preserving continuity while exploring new variables.

Graph continuity

The knowledge graph from the parent simulation is copied into the follow-up. This means entities, relationships, and context are preserved — the follow-up doesn't start from scratch. The original graph remains intact for reference or additional branches.

Agent consistency

Stakeholder agent personas are maintained across follow-up runs. The same institutional roles, stances, and behavioural patterns carry forward, ensuring that reactions in the follow-up are consistent with the original simulation's dynamics.

Branching

You can run multiple follow-up scenarios from the same parent, exploring different what-if branches. Each branch creates an independent copy of the graph and agents, so experiments don't interfere with each other.

Frequently Asked Questions