Problem Definition

At a medical-device company, a single customer thread could run to dozens — sometimes more than 200 — emails. Finding the relevant information buried across that thread was slow and sometimes effectively impossible. The cost wasn't just time: critical details got lost in the noise, cases were misclassified, and responses to the customer were delayed — a real risk when the information in those threads can be clinically or regulatorily relevant.

Client stack

• Ticketing: Freshdesk

Goal

Cut the time and difficulty of handling long, critical cases — without sacrificing service quality.

Solution

1. An AI bot reads the entire customer thread and extracts the information provided across every message.
2. It structures that information according to the fields the support team actually needs.
3. It populates each piece into the appropriate Freshdesk field.

The agent now checks a single structured field instead of reading the full thread — saving time and removing the mistakes that came from manually parsing hundreds of emails.

Enhancement #1 — Cost-efficient incremental reads

Instead of re-reading the whole thread each time, the bot reads only the latest incoming message and updates the fields only when new data appears — cutting token cost on long-running cases.

Enhancement #2 — Missing-information flagging