ShareIn today’s competitive procurement landscape, identifying the right opportunities fast can make or break a business’s growth strategy. For small to mid-sized companies across Europe, monitoring hundreds of procurement portals manually is both time-consuming and inefficient.
Bidbot, a startup focused on EU tender qualification, approached Peruzzi Solutions with a clear mission to create a scalable, AI-powered platform that automatically discovers and ranks public tenders based on their relevance to each user’s business profile. This is an AI development case study.
Our team delivered a fully functional Proof of Concept (PoC) in just one month, laying the foundations for an intelligent, self-improving system that’s helping companies save time and win more public contracts.
The problem
Public tenders in the EU are published daily on multiple platforms, including the official Tenders Electronic Daily (TED). Each listing includes long technical descriptions, inconsistent formats, and complex metadata.
For Bidbot’s target users - busy business development teams - the process of manually sorting through these tenders was overwhelming, hence, creating a problem.
The key challenge:
- Automate tender discovery and filtering, so users receive only the most relevant opportunities.
- Reduce noise, ensuring that results align with company size, sector, and region.
- Deliver insights fast, through a modern, user-friendly interface.
The solution
At Peruzzi Solutions, we always start with a focused Proof of Concept to provide a quick solution. For Bidbot, we followed a structured approach:
1. Data Collection & Structuring
We built a robust data pipeline to scrape, clean, and structure data from TED (Tenders Electronic Daily). This included automated normalization of tender metadata and multilingual processing across EU member states.
2. AI-Powered Relevance Ranking
Using LangChain and FastAPI, we developed a modular relevance ranking engine. The model combines AI prompts and heuristics to evaluate tenders based on criteria such as sector, region, and buyer profile.
This ensured that each user receives a personalized feed of high-relevance tenders instead of a generic list.
3. End-to-End Platform Build
We delivered a complete web-based platform, built with Vue.js, ASP.NET, and Microsoft SQL Server, featuring:
- Secure user onboarding and authentication
- Subscription and notification flows
- Custom dashboards and daily digests
- Feedback collection for continuous AI refinement
Within four weeks, Bidbot had an operational product ready for pilot testing, not just a prototype.
The system’s architecture allows new tenders to be automatically processed, scored, and ranked in real time. Users receive curated tender suggestions daily, while the AI model continues to learn from user feedback to refine its matching accuracy.
This dynamic feedback loop means the platform gets smarter over time - a core design principle in all Peruzzi AI builds.
The impact
Efficiency: The impact was noticeable: Bidbot’s users now spend 70% less time filtering through irrelevant tenders, focusing instead on opportunities that truly matter to them.
Scalability: The system was designed with scalability in mind. Its modular architecture allows for future integration of:
- Sector-specific AI models
- Advanced analytics and reporting tools
- Custom recommendation engines for private sector tenders
User Experience: The interface is built for non-technical users — fast, clean, and intuitive — enabling legal, procurement, and sales teams to benefit without additional training.