Motivation
Public tenders are often difficult to find, understand, and analyze efficiently. Government databases are frequently outdated, provide very limited filtering options, and rarely offer modern interfaces (APIs) that facilitate automated discovery and evaluation. As a result, companies must spend a considerable amount of time manually reviewing notices, downloading documents, and determining whether a tender is actually relevant to their business.
This problem is especially significant for small and medium-sized companies, which usually do not have dedicated teams focused on public procurement. Valuable opportunities are often missed because the available search tools rely heavily on exact keywords, administrative classifications, or fragmented metadata that do not accurately represent the real technical requirements of a tender.
With this project, we aim to build a solution that addresses these limitations by combining structured tender data with AI-powered enrichment. Our objective is not only to make tenders easier to search, but also easier to understand. By extracting relevant technical information, generating meaningful summaries, classifying opportunities by domain, and enabling more natural search queries, we hope to significantly reduce the effort required to identify suitable opportunities and help companies focus on the tenders that truly match their capabilities.