Focus on people over raw skills.
Venture Crush was created because most startup failures stem from co‑founder conflicts and mismatched teams - around 90 percent of startup failures are due to “people problems,” not just capital or market fit
Deep profile analysis.
The platform analyses more than 150 data points per founder profile (skills, goals, work styles and even personal interests) to build a rich understanding of each person. This goes far beyond the basic CV approach used by job boards.
Natural‑language AI.
Venture Crush’s matching engine uses advanced natural‑language processing to pick up on subtle traits, preferences and contextual cues. It does not simply keyword‑match skills; the AI tries to understand how you work and what kind of collaborator suits you.
User agency.
Once a shortlist is generated, founders still decide whether to proceed - Venture Crush functions like a “dating app for your startup,” giving the final choice to users.
Alignment of values and working styles.
Matches are curated to ensure shared values, compatible working styles and complementary skills. The system deliberately avoids pairing people whose working philosophies clash.
Competitor platforms either prioritise fundraising (AngelList), peer networking (Founders Network), general professional introductions (Lunchclub) or gig‑economy freelancing (Upwork/Fiverr). Venture Crush differentiates itself by leveraging AI to address the human‑compatibility problem that causes most startups to fail, offering a holistic service that helps founders find technical partners whose values and working styles match their own.
Weighted Scores
01
Core Competencies (C)
Ensures that the developer has the necessary technical skills and experience to meet the startup's needs.
02
Values Alignment (V)
A shared value system helps in making consistent decisions and fosters long-term collaboration.
03 & 04
Interests (I) and Passions (P)
Common interests and passions can lead to higher engagement, motivation, and satisfaction in the work environment.
05
Hobbies (H)
While often overlooked, shared hobbies can help in building personal connections that strengthen team dynamics.
06
The Magic Factor (?)
The undefinable spark our algorithm is always testing and refining through real feedback from founders and techies. Don’t believe us? Test it for yourself.
The Science Behind the Match
1. The Foundation: Beyond Surface-Level Matching
Our proprietary AI engine analyses a vast range of attributes - from technical skills and past work to passions and hobbies.
It processes this data through semantic normalisation and ontology mapping to understand deeper human compatibility.
The goal: to identify not just who can do the job, but who you’ll actually enjoy building with.
Each participant is represented through rich, high-dimensional profiles capturing both skill and personality.
2. The Scoring System: The 'Crush' Equation
Each match is powered by six weighted scores:
C = Core Competencies | V = Values | I = Interests | P = Passions | H = Hobbies | ? = The Magic Factor.
These variables reflect how well two people align across professional, personal, and emotional dimensions.
Together, they create a single Compatibility Score - a measurable signal of chemistry.
3. The Intelligence Layer: How the AI Thinks
The system uses weighted scoring models to assess experience depth and alignment.
Graph-based similarity measures reveal hidden relationships between profiles.
Anomaly detection removes bias, exaggeration, or noise from inputs.
Cross-validation against past outcomes keeps the algorithm learning and improving.
4. The Engine: Scalable, Transparent, and Evolving
A non-linear aggregator fuses all features to form the final Crush Score, used for real-time matching.
It’s optimised for availability, workload balance, and geographic preference.
Built on a distributed microservice backbone, it adapts dynamically as users evolve.
Explainability layers make every match transparent — showing why two people were paired.
Behind the Match
Precision Matching
Because our algorithm weights core competencies, values, interests and more, each match is highly targeted.
Cultural Fit at Scale
Shared values, passions, and even hobbies are considered to ensure teams vibe — not just work together.
Adaptive Intelligence
The weights in our scoring formula can be tuned per founder or project to optimize match outcomes.
Tested & Evolving
Our “magic” factor is refined continuously through real feedback - giving us an edge over static systems.
The Founders
Venture Crush was created by founders who’ve lived the chaos of early-stage startups and wanted to make it smarter, faster, and more human.
Between them, Harry and Sumit have launched ventures across London, Dubai, and Delhi - leading teams that blend product, design, and AI into real-world impact. They share a belief that great startups aren’t built through luck or LinkedIn connections, but through genuine chemistry between people who think, build, and dream alike.
Venture Crush is their answer to that - a platform born from experience, built for the next generation of founders and techies who want to build with purpose.
Harry Geisler
Co-Founder
Through years of building companies in high risk, low resource environments, Harry saw firsthand that good teams survive challenges while mismatched ones collapse. After watching many talented developers impacted by tech layoffs, he founded Venture Crush to help founders and developers form stronger, more compatible partnerships built on values, working style, and real fit.
Sumit Dubey
Co-Founder
Sumit Dubey is a problem-solver at heart - equal parts strategist and builder. With a background spanning AI, operations, and venture development across India and the Middle East, he’s the steady engine behind Venture Crush’s global rollout. Sumit brings structure to chaos, turning early-stage ideas into scalable systems and making sure every big vision actually lands.
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