Automated Enquiry Categorization

We begin by collecting a sample of real enquiries from every channel: website forms, email, WhatsApp, social messages and call notes. Two or three hundred examples are enough to see the patterns. We read them with your team and agree on categories. Good categories are few, clear and tied to action: for example sales enquiry, quote request, support issue, billing question, complaint, job application, partnership, vendor pitch and spam. Each category gets a short definition, examples and an owner, and we decide what extra tags matter, such as service, city, language and urgency. We then label the sample by hand to form a reference set, which is also used to test accuracy later. Next we design the method. For clear patterns, simple rules work well, such as form dropdowns, keywords and sender addresses. For free text where wording varies, we add an AI classifier that reads the message and assigns a category and tags, with a confidence score. We choose a tool that fits your data policy and budget, and we write careful instructions listing the categories, their definitions, examples of hard cases and the exact output format. We test the classifier on the reference set and measure how often it matches human labels, where it confuses categories and how it handles short, misspelled or mixed-language messages. We refine the instructions and rules until results are good enough for your use; see prompt and knowledge base development. We set confidence thresholds: high-confidence results are applied automatically, medium ones are flagged for quick review and low ones go to a general queue. We then connect the classifier to your systems. New messages arrive through an integration, are classified, tagged and saved in the CRM or help desk, and trigger the next step such as an alert, an auto-reply or assignment; see website to CRM integration and form to email and CRM integration. We add handling for sensitive items such as complaints, legal threats or messages that mention safety, which always go to a named person promptly. We build a correction loop: when staff change a category, the change is recorded and used to improve the rules. We prepare a dashboard showing volume by category, response time by category and the share corrected by humans. We train the team in reviewing flagged items and updating the category list; see standard operating procedure documentation. We review accuracy monthly at first. We cannot make an AI tool always right, and new types of enquiry will appear, but a monitored system reliably cuts manual sorting and speeds up first responses.