From thousands of public tenders to a handful of high-value leads
Country
Poland
Industry
Financial Services
POLFUND Fundusz Poręczeń Kredytowych Private joint stock company, established in 2001, is one of Poland's largest macro-regional funds supporting SME finance in the west of the country, providing credit and bid-bond guarantees in partnership with major banks and EU institutions.
To automate lead generation and cut counterparty-verification risk, Polfund partnered with us: We deployed AIConsole as the AI orchestration platform at the core of the Polfund solution and built SmartLead AI on top of it from scratch. Both are now live in production.
Services
Technologies
Project in numbers
Tenders screened daily
Lead research automated
to verify a counterparty
Continuous monitoring
The Challenge
Project challenge: automating lead generation and counterparty verification in public procurement
The public procurement market generates thousands of sales signals every day, therefore financial institutions offering bid bonds and guarantees don't struggle with a lack of data - they struggle with overload of it, scattered across sources, and the risk of choosing the wrong clients. This article shows how POLFUND Fundusz Poręczeń Kredytowych Private joint stock company moved from manually browsing tenders, to an automated lead generation and verification process.
The business model is simple: companies participating in public tenders require bid bonds, and the institution's role is to identify these opportunities, assess risk and issue guarantees quickly.
The operational challenge lies in scale. Relevant information is dispersed across announcements, PDFs and protocols, counterparties require verification across multiple registries, and short response time makes manual processing inefficient. Consequently, valuable opportunities are missed and relationship managers spend more time gathering information, rather than building client relationships.
Before implementing the solution, Polfund sought to streamline several operational challenges inherent to client acquisition in the public procurement market.
- Time-consuming research: manually searching thousands of listings on e-zamowienia.gov.pl and TED, for tenders requiring bid bonds or performance guarantees.
- Difficult data extraction: key information about bidders was buried in PDF attachments or on contracting authorities' websites, making it hard to identify companies and reach decision-makers.
- Missing contact details: public notices rarely include phone numbers or emails of contact people responsible for finance, blocking sales outreach.
- Risk of faulty verification: manually checking companies across registries slowed the process and increased the risk of partnering with indebted entities.
- Delayed response: manual analysis of bid-opening protocols lengthened the time to reach prospects, reducing the chances of selling follow-on products (e.g. performance guarantees).
Project challenge: automating lead generation and counterparty verification in public procurement
The public procurement market generates thousands of sales signals every day, therefore financial institutions offering bid bonds and guarantees don't struggle with a lack of data - they struggle with overload of it, scattered across sources, and the risk of choosing the wrong clients. This article shows how POLFUND Fundusz Poręczeń Kredytowych Private joint stock company moved from manually browsing tenders, to an automated lead generation and verification process.
The business model is simple: companies participating in public tenders require bid bonds, and the institution's role is to identify these opportunities, assess risk and issue guarantees quickly.
The operational challenge lies in scale. Relevant information is dispersed across announcements, PDFs and protocols, counterparties require verification across multiple registries, and short response time makes manual processing inefficient. Consequently, valuable opportunities are missed and relationship managers spend more time gathering information, rather than building client relationships.
Before implementing the solution, Polfund sought to streamline several operational challenges inherent to client acquisition in the public procurement market.
- Time-consuming research: manually searching thousands of listings on e-zamowienia.gov.pl and TED, for tenders requiring bid bonds or performance guarantees.
- Difficult data extraction: key information about bidders was buried in PDF attachments or on contracting authorities' websites, making it hard to identify companies and reach decision-makers.
- Missing contact details: public notices rarely include phone numbers or emails of contact people responsible for finance, blocking sales outreach.
- Risk of faulty verification: manually checking companies across registries slowed the process and increased the risk of partnering with indebted entities.
- Delayed response: manual analysis of bid-opening protocols lengthened the time to reach prospects, reducing the chances of selling follow-on products (e.g. performance guarantees).
Time-consuming research
Manually scanning thousands of tender listings
Difficult data extraction
Key info buried in scattered PDFs
Missing contact details
Finance contacts often unavailable or outdated
Risk of faulty verification
Manual registry checks, slower and riskier
Delayed response
Manual protocol analysis delays outreach further
Manual Process Challenges
Automated source monitoring
Scans up to 5,000 new tenders daily from e-zamowienia.gov.pl and 500 from ted.europa.eu.
Dedicated site maps
AI agents trained on specific portal structures achieve 95% data-extraction accuracy on key platforms.
Business filtering
Auto-selects tenders by bid-bond guarantee requirement and contract value (up to PLN 100M for guarantees), cutting the noise.
Tender fit assessment
The model supports the assessment of a company's potential fit for a specific procurement proceeding based on historical data.
Key AI Capabilities
The Solution: SmartLead AI and AIConsole
Rather than building another tender alert tool, 10Clouds Financial Institutions, together with Polfund, designed a system that determines which opportunities deserve attention. Two products work together: SmartLead AI handles monitoring and selection, and AIConsole supports data analysis and verification, while business decisions remain the responsibility of POLFUND employees.
The system continuously monitors procurement proceedings and identifies those that may be relevant to POLFUND's offering, extracts the meaningful details from tender documentation, and supplements it with data from available public registers before forwarding the results to an employee for further analysis.
The sales team receives a structured list of potential business opportunities for further verification and taking autonomous business decisions.
How The Lead Engine Works
The pipeline turns raw public data into qualified leads in five high-level steps:
- Monitor public procurement sources for thousands of new tenders each day
- Filter for the ones that potentially might need a bid bond or guarantee
- Extract entities, bid values and registry data from documents and portals
- Enrich prospect data with relevant information from public registers and business sources
- Deliver a short list of high-value, sales-ready opportunities for further autonomous business decisions
Everything runs in a secure European cloud environment, designed for the requirements of the financial sector (AI Act and GDPR compliant).
Public tender sources
Thousands of new proceedings scanned across public procurement portals every day.
SmartLead AI monitoring
Filters for bid-bond and guarantee relevance, removing the information noise.
Data extraction
Entities, bid values and registry data pulled from PDF attachments and buyer portals.
AIConsole risk verification
Legal status, debt signals and financial health checked in seconds, not hours.
Qualified leads to sales
A short list of verified, high-value opportunities, pending consent confirmation before outreach.
Runs continuously in a secure European cloud, purpose-built for a financial sector
Business Impact
- A significant part of the data search and analysis process has been automated.
- The time required to analyse and prepare data for further processing has been reduced.
- Enhanced database quality through the automated application of defined business and registry criteria.
- Improved prioritization of companies that may potentially be interested in POLFUND's products.
- Data enrichment – recovering hard-to-find contacts from PDFs and external sources.
A process supported by automation and artificial intelligence solutions. Lead research that once consumed hours of manual work is now handled end-to-end by the system. The system significantly reduces the time required to collect and perform an initial analysis of counterparty data, while low-quality prospects, dormant, indebted or these on too early stage of business development are filtered out automatically. The sales team spends its time on conversations, not on selection.
Most importantly, the fund now reaches the right contractors earlier and with more confidence than manual work ever allowed.
