What is Semantic Search?
Traditional startup databases like Crunchbase and PitchBook rely on simple keyword matching, which can miss relevant companies that use different terminology. In contrast, semantic search engines understand the actual meaning of your query through advanced techniques like word embeddings. For instance, if you search for “companies developing autonomous drone swarm technology for agriculture,” the engine understands the underlying concept and surfaces relevant startups, whether they describe themselves as working on coordinated UAV systems, precision farming robotics, or multi-drone crop monitoring. This intelligent approach ensures you discover relevant companies based on what they actually do, rather than just the specific words they use to describe themselves.
Traditional Keyword Search
StartupSeeker Semantic Search
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Relative Traction Score
Putting a definitive “success score” on startups is inherently challenging, as success means different things to different people, and investor preferences vary widely. However, the Relative Traction Score offers a data-driven way to understand how popular a startup is compared to others in the market. This scoring system is uniquely designed to be age-independent, meaning young startups can be fairly evaluated against more established ones. The score works by analyzing where a company stands among others of similar age across various digital presence metrics, using mathematical balancing to create meaningful comparisons. When funding data is available, it's also factored in by comparing investment levels with those of similarly-aged companies. While not a crystal ball for predicting success, the Relative Traction Score provides a practical way to measure a startup's current market presence and popularity relative to its peers.