How AI Tender Matching Works: The Technology Behind Smart Procurement
March 19, 2026 · 7 min read
When people hear that an AI system matches tenders to companies, they often imagine simple keyword matching. The reality is significantly more sophisticated.
The problem with keyword matching
Keyword matching fails because tender language and company descriptions rarely overlap precisely. "Civil engineering services for highway rehabilitation" will not match "road infrastructure maintenance." The words differ. The meaning is the same.
Vector embeddings: turning text into numbers
The foundation of modern AI matching is the vector embedding — a mathematical representation that captures meaning, not just words. Two texts with similar meanings have similar embeddings even if they share no words.
The matching process
When Trinta processes a new tender, it generates an embedding from the title, description, category, and issuer. When a company completes their profile, an embedding is generated from that too. Finding matches is a mathematical similarity search across millions of tenders in milliseconds.
The five-factor scoring system
Semantic similarity alone is not enough. Trinta combines it with four additional factors:
Industry overlap (25%): Do the company's industries match the tender's categories?
Geographic fit (15%): Is the company active in the tender's country?
Company size fit (12%): Does the contract value match company capacity?
Historical signals (8%): Has the company won similar contracts before?
The feedback loop
Matching improves through user feedback. Thumbs up and thumbs down signals improve future matches for similar companies.
Share this article
Related articles