ECONOMIC TWIN™ creates structured digital models of your business, your market, and your competitive environment — and uses those models to simulate strategic scenarios, predict AI visibility outcomes, and identify opportunities before they become visible to everyone else.
Leadership teams know their own revenue, their own clients, their own services. They have general awareness of their market. They have anecdotal knowledge of their competitors. But they rarely have a structured, quantified, machine-readable model of how their business, their market, and their competitors interact — and how changes in any one of these systems will propagate to the others.
In the AI era, this problem has a new dimension. AI visibility is now a strategic variable. A business's AI Readiness Score affects how often it is recommended, which affects buyer discovery rates, which affects revenue. A competitor's investment in TRUST LAYER™ or PROOF LAYER™ can shift AI recommendation patterns within weeks — before any traditional competitive intelligence system would detect it.
ECONOMIC TWIN™ models all of these variables in a structured, machine-readable format — and uses the model to simulate the strategic impact of decisions before they are made.
Three interconnected models. Each updated automatically from AI SONAR™ monitoring data and AI AUDIT™ results.
A complete digital model of the business — updated automatically every time AI SONAR™ detects a change in AI visibility status, every time a signal is deployed or updated, and every time an AI AUDIT™ is completed.
Service lines, revenue by service, client concentration, geographic distribution.
Every service, its target client, pricing model, delivery format, and current AI visibility status.
AI Readiness Score, signal layer status, authority layer status, proof layer status, monitoring coverage.
Relative AI visibility score vs top three competitors, per service category, per geography.
A structured model of the business's market — updated weekly from AI SONAR™ monitoring data and structured market intelligence feeds. Shows where the market is moving before it is obvious.
AI query volume for the sector, trend direction, seasonal patterns, emerging categories.
Which buyer queries are driving AI recommendation requests, with volume and trend direction.
AI query concentration by geography with growth rate — where demand is accelerating.
New AI systems entering the market, existing systems updating knowledge graph algorithms.
Structured models of the top three competitors — updated continuously by AI SONAR™ competitor monitoring. Every signal deployment, every authority gain, every recommendation position change detected and modelled.
Estimated from publicly accessible signal data — updated weekly from SONAR™ monitoring.
Which of the 167 signals they have deployed, which are missing, which are inconsistent.
Citation count, directory presence, proof layer status, and trust signal density.
Specific signal or authority gaps in competitor profiles that represent strategic opportunities.
Three distinct capabilities built on the three-twin model. Each answers a different strategic question.
Model the predicted impact of a specific action on AI Visibility Score, recommendation frequency, and buyer discovery rate — before the action is taken.
Simulate multiple strategic paths simultaneously and compare their projected AI visibility and authority outcomes across different time horizons.
Apply scenario templates — common strategic situations in B2B markets — to the business's specific context for structured, comparable outputs.
Every scenario in the library can be configured for the specific business context and run against the three-twin model in minutes.
Models the AI visibility impact of adding a new service category — signal requirements, expected timeline to Findable™ status, buyer intent alignment gaps, and initial competitive position.
Models the signal requirements and expected visibility timeline for entering a new geographic market — language coverage, local directory presence, geographic intent filter deployment.
Models the recommended response to a competitor strengthening their AI authority layer — which counter-actions have the highest impact per unit of investment.
Models the impact of a new EU AI Act requirement on signal and governance structure — what changes, at what cost, with what timeline, and what the competitive advantage is for early compliance.
Models the complete impact of deploying the full platform stack — from current state through each product to Recommendable™ — with projected score at each stage and total timeline.
Models the AI visibility impact of acquiring or merging with another business — entity graph consolidation requirements, conflicting signals, and the path to a unified authority profile.
Versioned digital model of the business in machine-readable format. Updated automatically from AI SONAR™ and AI AUDIT™ data. Includes all revenue, service, client, and AI infrastructure dimensions.
Structured model of the market with AI query demand data, competitor landscape, buyer intent patterns, and regulatory environment. Updated weekly from SONAR™ data.
Structured models of top three competitors with AI visibility scores, signal layer analysis, authority profiles, gap opportunities, and trend direction. Continuously updated by SONAR™.
Structured reports comparing multiple strategic paths with projected AI visibility outcomes, implementation timelines, investment requirements, and risk assessments.
Structured intelligence on competitor AI visibility movements, signal changes, and strategic positioning shifts — updated continuously from SONAR™ monitoring.
Quarterly prioritised list of strategic actions ranked by projected AI visibility impact — generated from all Twin data and updated every quarter.
Where AI recommendation positioning is becoming a decisive factor in buyer discovery. Typically businesses with revenues above €1M operating where multiple competitors are already investing in AI visibility.
That need structured, data-driven inputs for strategic planning — not opinions or anecdotal competitive intelligence. Board-ready outputs with projected ROI and scenario comparisons.
That need to justify AI visibility investment to leadership with projected ROI, competitive positioning data, and scenario-based planning tools that can be reported in quarterly reviews.
Consulting, legal, accounting, and technology firms where the buying decision is heavily influenced by which firm AI recommends first — and where authority signals compound over years.
ECONOMIC TWIN™ is the tenth and final product in the ADI™ platform. Together, all ten products form the complete infrastructure for AI visibility, AI authority, and AI-era strategic intelligence. Every product works alone. Every product works better together.