Intent recognition

wit.ai turns a user message into a predicted intent with a confidence score. When a message enters your workflow, a Beam agent sends it to wit.ai, reads the recognized intent, and applies your rule tied to the confidence level to decide the next step. It records the intent against the conversation and can route the request to the right handler. Confident matches proceed automatically. Messages that come back with low confidence, or several close intents, are handed to a person. Someone confirms what the user meant before the agent acts on an uncertain interpretation.

Entity extraction

Beyond intent, wit.ai pulls entities such as dates, locations, and amounts from a message. As each message is parsed, a Beam agent reads the extracted entities, applies your rule for which are required, and writes the structured values into the record or downstream system. Complete extractions move forward on their own. Messages missing a required entity, or returning conflicting values, are set aside for a person. Your team supplies the missing detail or corrects the parse before the agent commits the values, so no downstream step runs on an incomplete or contradictory reading.

Training data upkeep

wit.ai improves as its training examples grow. On a schedule, a Beam agent reviews recent low-confidence parses and, following your rule, prepares candidate examples and labels for the model. It records each suggestion for review rather than applying it blindly. Clear, unambiguous additions can be queued automatically where you allow it. Anything that would reshape an intent, merge entities, or touch a sensitive category is routed to a maintainer. A person approves training changes before they reach the model, keeping the understanding aligned with how your users actually phrase requests.