WarmConnect analyzes 56+ touchpoint types across 8 dimensions to find the warmest path to anyone in the world.
We're continuously expanding our analysis to deliver even greater connection accuracy.
Deep
Connection Analysis
56+
Touchpoint Types
& Growing Daily
87%
Success Rate
Warm
Connections Only
Building the World's Most Sophisticated Connection Graph
While others show you if people are connected, WarmConnect understands the strength, context, and warmth of every relationship. Our AI-driven platform doesn't just find connectionsβit predicts which introductions will actually succeed.
By analyzing professional histories, educational backgrounds, board positions, investment relationships, cultural affinities, and dozens of other factors, we've built the world's most comprehensive understanding of how people are truly connected.
π―
Beyond Binary Connections
We score relationships from 0-100 based on multiple dimensions, not just "connected" or "not connected"
β°
Temporal Intelligence
Relationships decay over time. We track recency and duration to ensure introductions happen at the right moment
π
Hidden Networks
We uncover non-obvious connections through sports teams, military service, shared adversity, and cultural bonds
Every relationship is unique. Our proprietary algorithms analyze connections across eight major dimensions, with each dimension containing multiple touchpoint types that contribute to the overall connection strength.
π Always Improving: We're actively expanding our touchpoint analysis beyond the current 56+ types. Our research team continuously identifies new relationship indicators and patterns to provide you with even more accurate and valuable connection insights.
Professional Touchpoints (11 types)
Direct Working Relationships
β’Manager/Direct Report: Tracks reporting relationships with temporal decay
β’Peer Executives: C-suite and VP-level lateral relationships
β’Cross-functional Projects: Collaborated on specific initiatives
β’Same Department: Worked in same functional area
Extended Professional Networks
β’Same Company: Overlapping employment with location analysis
β’Media Production Teams: Film, TV, digital content collaborations
β’Collaborative Team Projects: Product launches, research teams
Temporal Analysis: Each professional relationship includes "overlap_type" conditions that adjust scores based on whether people worked together concurrently or years apart
Educational Touchpoints (7 types)
Academic Connections
β’Same University: Alumni networks with temporal overlap
β’Overlapping Years: Actual campus overlap vs. different eras
β’Same Degree Program: MBA, engineering, medical school bonds
β’Elite MBA Programs: Harvard, Stanford, Wharton special scoring
Campus Life Bonds
β’Greek Life: Fraternity/sorority connections across chapters
β’University Leadership: Student government, club officers
β‘Anomaly Detection: Identifies unusual but strong connections
β‘Quality Scoring: Validates and weights data sources
β‘Privacy Protection: Anonymizes and aggregates sensitive data
Real-Time Processing Pipeline
Data IngestionβEntity ExtractionβRelationship MappingβTemporal AnalysisβScore CalculationβGraph Update
Temporal Intelligence: Relationships Change Over Time
One of our key innovations is understanding that relationships are dynamic. Two people who worked closely together five years ago have a different connection strength than current colleagues. Our temporal analysis ensures you're leveraging relationships at their peak effectiveness.
How We Track Relationship Decay
Current Relationships (Score: 100%)
Active colleagues, current board members, ongoing projects
Recent Past (1-2 years) (Score: 75%)
Former direct colleagues, recent shared experiences
Moderate Past (3-5 years) (Score: 50%)
Past colleagues, older shared experiences
Distant Past (5+ years) (Score: 25%)
Historical connections requiring reactivation
Overlap Analysis Examples
Board Service Together
β’ Concurrent service: 4.0 score
β’ 1 year gap: 3.0 score
β’ 2-3 years gap: 2.0 score
β’ 5+ years gap: 1.0 score
University Attendance
β’ Same years on campus: 3.0 score
β’ 1 year overlap: 2.5 score
β’ 2 years apart: 2.0 score
β’ 10+ years apart: 0.5 score
Manager-Report Relationship
β’ Currently managing: 5.5 score
β’ Past year: 4.5 score
β’ 2-3 years ago: 3.5 score
β’ 5+ years ago: 2.5 score
Finding the Warmest Path, Not Just the Shortest
Our path-finding algorithms optimize for introduction success, not just degrees of separation. We analyze extensive networks of potential paths to find the ones most likely to result in meaningful connections.
Path Scoring Example: Reaching a Fortune 500 CEO
π Warmest Path (Score: 92/100)
High Success Rate
You β Former Colleague (current board member) β Fellow Board Member β Target CEO
β Step 1: Strong professional bond + current relationship
β Step 2: Active board collaboration (quarterly meetings)
β Step 3: Peer CEOs in complementary industries
β‘ Shortest Path (Score: 41/100)
23% Success Rate
You β LinkedIn Connection β Target CEO
β οΈ Weak first-degree connection (met once at conference)
β οΈ Large seniority gap and different industries
β Likely to be ignored or delegated to assistant
Path Quality Factors
β’ Strength of each connection
β’ Recency of interactions
β’ Mutual benefit potential
β’ Seniority alignment
β’ Geographic feasibility
β’ Industry relevance
Introduction Context
β’ Shared interests identified
β’ Mutual connections highlighted
β’ Timing recommendations
β’ Conversation starters
β’ Follow-up suggestions
Success Predictors
β’ Introducer's track record
β’ Recipient's openness
β’ Request specificity
β’ Value proposition clarity
β’ Cultural alignment
Continuous Learning & Improvement
Our algorithms get smarter with every introduction. By tracking outcomes and gathering feedback, we continuously refine our scoring models to improve success rates.