Text parsing and tagging

TXT Werk breaks raw text into structured pieces such as tokens, entities, or labels. On a trigger like a new document arriving, a Beam agent passes the text through the framework, reads the tagged output, and applies the rule your team approved, for example filing the item by its detected topic. Confident, well-formed results are acted on directly. Text that returns low-confidence tags, comes back in a language the rule does not cover, or produces an empty result is set aside, and the agent asks a person to classify it before filing.

Entity extraction reads

TXT Werk pulls named items out of text, such as people, places, or amounts. A Beam agent reads the extracted entities, checks them against the fields your team maps, and writes them into the connected record where permissions allow, so a form or profile fills without manual typing. Clean extractions with a single clear match are saved automatically. When an entity is ambiguous, conflicts with an existing value, or the extraction misses a required field, the agent holds the record and routes it to a person to confirm the values.

Content classification rules

TXT Werk assigns text to categories your team defines. When content arrives, a Beam agent runs the classification, reads the assigned label and its confidence, and acts on the approved routing, for example sending a support message to the right queue. Items above the confidence level you set are routed on their own. A message the framework labels weakly, one that spans two categories, or one your policy marks for review is not routed automatically. The agent flags it for a person, who assigns the correct category before the item continues downstream.