A private lending company in Poland, operating much like a digital-first bank, relied heavily on brokers to submit mortgage applications via email. Each submission came in a different format — unstructured text, attachments, and scattered data. We built an AI-driven system that reads these emails, extracts key information, runs credit scoring, and creates ready-to-process leads in the CRM — eliminating manual work and accelerating decision-making.
A private lending company based in Poland operates as a “mini bank,” specializing in mortgage financing for real estate transactions.
The company works through a network of brokers who submit applications via email, including financial details, property information, and supporting documents such as PDFs or scanned files.
There is no standard format — each broker structures submissions differently. As volumes increased, this flexibility started creating operational strain.
Every application required manual review — opening emails, checking attachments, identifying relevant data, and entering it into internal systems.
Since brokers followed different formats, processing effort varied significantly, often requiring clarification and follow-ups.
As application volumes increased, this created a clear bottleneck — slowing down response times and making scaling difficult without increasing team size.
An intelligent automation layer was introduced to process incoming emails, extract structured data using AI, and seamlessly connect with scoring and CRM systems.
The focus was not just automation, but reliability — building a system that could handle different email formats, inconsistent data placement, and mixed document types without requiring brokers to change how they work.
The solution acts as a bridge between incoming communication and internal systems, converting unstructured inputs into consistent, usable data in real time.