Using AI to Process Customer Complaints: The Epotos Case Study
Epotos has shown how it moved complaint handling off manual email and Excel and onto a local AI pipeline. The system reads emails and attachments with Tesseract, classifies each request with the Qwen3-30B-A3B model running in Ollama, and immediately creates records and tasks in Bitrix24. The result: less manual forwarding, a single standardized checklist, faster root-cause analysis of defects, and real-time tracking of complaint status.
AI-processed from Habr AI; edited by Hamidun News
Epotos has deployed a neural network to process customer complaints: a local OCR and LLM stack now parses incoming emails, and employees receive a ready-made task with a checklist in Bitrix24.
How does AI-based complaint processing work?
Take Epotos as an example: OCR and an LLM parse incoming emails and attachments, and the results land in Bitrix24 with a ready-made checklist for employees.
Why does a business need AI for handling complaints?
It frees administrative staff from manually forwarding and logging requests: emails are parsed automatically, and employees receive a task that is already filled out.
Can AI for complaint processing run locally?
Yes. Epotos uses a local AI pipeline with no cloud services. OCR and an LLM process incoming emails, and the system automatically logs each request in Bitrix24.
Key Questions Answered
How does AI-based complaint processing work?
Take Epotos as an example: OCR and an LLM parse incoming emails and attachments, and the results land in Bitrix24 with a ready-made checklist for employees.
Why does a business need AI for handling complaints?
It frees administrative staff from manually forwarding and logging requests: emails are parsed automatically, and employees receive a task that is already filled out.
Can AI for complaint processing run locally?
Yes. Epotos uses a local AI pipeline with no cloud services. OCR and an LLM process incoming emails, and the system automatically logs each request in Bitrix24.
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