About CompeteView
We're building the competitive intelligence platform we wished existed — one that actually works without perfect data.
Our Story
CompeteView was born from a simple frustration: existing competitive intelligence tools assume you have perfect data. They want matching SKUs, GTINs, or other standardized identifiers. But in the real world of B2B e-commerce, that data often doesn't exist.
We set out to solve this problem using modern machine learning techniques. By combining multiple similarity methods — traditional text matching, semantic embeddings, and category-aware scoring — we built a system that can match products across catalogs even when they share no common identifiers.
But accuracy isn't enough. We also built a human-in-the-loop validation system that lets you review, confirm, and correct matches. Because no ML system is perfect, and business decisions deserve verified data.
Today, CompeteView helps e-commerce businesses understand their competitive landscape with confidence — matching products, benchmarking prices, and identifying market opportunities that would otherwise stay hidden.
What We Believe
Technical Excellence
We believe in building the best possible product, using state-of-the-art ML techniques combined with practical engineering.
Actionable Insights
Data is only valuable if it drives decisions. We focus on delivering insights that actually impact your business.
Customer Partnership
We work closely with our customers to understand their unique challenges and tailor solutions to their needs.
Data Integrity
We take data accuracy seriously. Our human-in-the-loop validation ensures you can trust the matches we deliver.
Our Technology
At the core of CompeteView is our ensemble matching engine. We don't rely on a single algorithm — we combine six or more different similarity methods and weight their outputs based on empirical performance.
Our semantic models (SBERT and BGE) understand what products actually are, not just the words used to describe them. Our TF-IDF matching catches exact terminology and model numbers. Our word vector models (Word2Vec, FastText, Doc2Vec) provide additional signal that helps disambiguate edge cases.
The result is matching accuracy that single-method systems can't achieve — and confidence scoring that tells you exactly how reliable each match is.