The company handles a large volume of documents, tickets, and bookings daily, where information is often unstructured (PDFs, emails, etc.). Customer service representatives spent a lot of time manually finding the necessary data and entering it into the digital system.
We created an AI agent that identifies critical information from documents (e.g., dates, personal data, payment terms) and structures it automatically into the company’s internal database or booking system. The solution combines text analysis, semantic search, and RAG methodology (Retrieval-Augmented Generation) to provide accurate real-time data extracts.
Customer service representatives no longer have to go through documents manually, freeing up resources for other critical service tasks. The process is faster, and errors decreased because data is no longer entered by hand. The result is a better customer experience, as people receive faster and more accurate service.
You can “re-program” the automation by changing the prompts. Changing rules and response instructions takes place in natural language.
After changing the prompt, you can automatically test the quality of the automation on a larger set of examples.
From usage statistics, you can see which parts the automation struggles with and decide whether to add more information, change prompts, or modify processes.
The automation created for you will be hosted in the company’s cloud server – this way, business secrets and sensitive data are securely protected.
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