Conversation summarization

One AI condenses long conversations into short summaries your team can scan quickly. A Beam agent picks up a finished chat or call transcript, requests a summary, and applies your rule to attach it to the matching ticket or account record. When the summary comes back with low confidence or the source text is too thin to be useful, the agent sets the item aside and notifies a person. Someone confirms the summary on sensitive threads and decides whether a flagged conversation needs a closer read before it reaches the customer file for good.

Article categorization

One AI sorts articles and documents into the topics your team defines. A Beam agent reads each new item, requests a category, and files the record into the matching queue based on your rule. When a document falls between topics or fits none with confidence, the agent holds it and asks a person for a decision instead of guessing at a label. A reviewer confirms the category on borderline items and adjusts the topic set as fresh material arrives, and the agent then follows the updated definitions on everything that lands after that point.

Sentiment and entity tagging

One AI reads free text and returns sentiment scores plus the names, places, and terms inside it. A Beam agent feeds incoming messages through the model, reads the output, and applies your threshold rule to tag each record and route negative cases to the right team. Text that scores near the neutral line or mixes praise with complaint goes to a person for a closer look. Someone confirms the tags on ambiguous language and decides how new entity types should be handled as the volume grows across the months ahead.