Compare 2-5 brands side-by-side. See gaps, overlaps, and where providers disagree.
Analysis Lens:
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Portfolio: All Your Brands 🤖 AI-Generated | 4 Providers | ℹ️ Methodology
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AI Briefs provide executive-ready summaries of brand perception
Briefs for
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📝 Executive Brief
•
•
Brand Position
Where AI models place your brand on key dimensions
Innovation
Sentiment
Accessibility
Scale: -1.0 (low) to +1.0 (high)
30-Day Movement
How brand perception has shifted recently
Innovation
Sentiment
Accessibility
Center = no change • Left = declining • Right = improving
AI Model Agreement
How much AI providers agree on brand perception
AI providers analyzed.
90-Day Outlook
Projected brand trajectory based on current trends
At RiskNeeds AttentionStableOpportunity
actions •
voids
⚠️ AI-Generated Content:
This executive brief was synthesized by AI from multi-provider perception analysis.
It reflects LLM-derived interpretations and should be validated against primary sources before use in decision-making.
View full methodology
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⚠️ AI-Generated Recommendations:
Steering recommendations are algorithmically derived based on semantic drift patterns.
These are suggestions, not guarantees of outcomes. Actual repositioning requires human judgment
and market validation. View methodology
Enter a brand/concept name and click "Calculate Steering" to analyze drift and generate recommendations.
⚠️ Adversarial Testing Disclosure:
Fragility indices measure response variance across edge-case prompts. High fragility indicates
inconsistent LLM behavior, not factual errors. Results should inform strategy, not replace
human analysis. View methodology
Provider × Category Fragility Heatmap
Click any cell for detailed breakdown. Colors: 🟢 <25% | 🟡 25-40% | 🔴 >40%
PROVIDER
No category data available yet.
Run a new stress test to populate the heatmap with per-category fragility data.
Breaking Points Log
Prompts that caused refusals, confusion, or errors across providers.
on
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Test History
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📈 Fragility Trend Over Time
Data Points
Avg Fragility
Trend
Brands Tested
Fragility
Most Fragile
Most Stable
Not enough data points for trend analysis.
Run at least 2 stress tests to see fragility trends over time.
Enter a brand/concept, select a test suite and click "Run Stress Test" to begin adversarial testing.
Enter a concept name and click "Load Brand" to begin
Target Position
Current Position:
X: Y: Z:
-1.0 +1.0
-1.0 +1.0
-1.0 +1.0
🎯 Repositioning Vector (3D Space)
🖱️ Drag to rotate • Scroll to zoom • Right-click to pan • ↕ Drag bottom edge to resize
ΔX (Ethical)
ΔY (Innovation)
ΔZ (Trust)
Difficulty Analysis
Difficulty Score
Distance to Target
Estimated Time
⚠️ Competitors in Target Zone
📈 Repositioning Plan
→
Current: →Target:
💡 AI Recommendations
ΔX: ΔY: ΔZ:
Keywords:
📁 Saved Scenarios
Target: (, , )
⚠️ Predictive Modeling Disclosure:
Difficulty scores and time estimates are AI-derived projections based on perception distances and historical patterns.
Actual repositioning outcomes depend on market conditions, execution quality, and factors beyond LLM analysis.
View methodology
OpenGIO queries four commercial AI providers simultaneously and synthesizes their responses
into semantic coordinates. Each AI model is shaped by training data, guardrails,
builders' values, and corporate ideology.
Providers Used
Provider
Model
OpenAI
Anthropic
Google
xAI
How Coordinates Are Calculated
Each provider receives identical structured prompts at temperature 0.0 (deterministic)
Responses are parsed into 45 semantic attributes
Attributes are normalized to a -1 to +1 scale
Coordinates are EMA-smoothed against the previous snapshot (α = 0.7) to filter residual model noise while preserving genuine trends
Coordinates represent the centroid (average) of provider responses
Consensus score measures provider agreement (inverse of variance)
The 3D Perception Space
Axis
Low (-1)
High (+1)
X (Red)
Functional (what it does)
Conceptual (what it means)
Y (Green)
Mass Market (accessible)
Elite (premium/exclusive)
Z (Blue)
Negative (distrusted)
Positive (trusted)
Sample Accuracy
All queries use temperature 0.0 for deterministic outputs — the same prompt returns the same coordinates.
Daily coordinates are EMA-smoothed (α = 0.7) against the previous day to filter residual model noise. Movement that persists for 3+ days reflects genuine perception change; single-day spikes are noise.
Limitations
Results reflect AI model perception, not objective reality
Models may contain training biases
Perception can drift as models are updated
Low consensus scores indicate contested or ambiguous concepts
Human Oversight
OpenGIO provides intelligence to support human decision-making.
Final decisions remain your responsibility.
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visualizes how 4 different AI models (OpenAI, Anthropic, Google, xAI) perceive your concept in semantic space.
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