Auto-tagging
Auto-tagging is the automated classification of customer service interactions by topic, intent, sentiment, or other metadata — replacing the manual process of agents categorizing tickets after handling them. AI-powered auto-tagging uses natural language understanding to analyze the content of a conversation and apply relevant tags in real-time.
Accurate tagging matters because it's the foundation of CX analytics. Without consistent, reliable tags, teams can't identify trending issues, measure category-level resolution rates, or detect emerging problems. Manual tagging is notoriously inconsistent — agents tag the same issue differently, skip tagging when busy, and use categories inconsistently. Studies show manual tagging accuracy typically falls between 60-80%.
Modern auto-tagging systems can:
Classify tickets across multiple taxonomies simultaneously (topic, product area, urgency, customer segment)
Apply tags in real-time as conversations progress, not just at close
Detect multiple intents within a single conversation
Identify emerging topics that don't fit existing categories
Maintain consistency across thousands of interactions per day
The most valuable application of auto-tagging for CX leaders isn't operational routing (though that helps) — it's strategic visibility. When every interaction is tagged consistently, you can answer questions like: "What are the top 5 issues driving contact volume this week?" or "Which product change caused the spike in billing complaints?" These insights compound over time, creating a feedback loop between customer interactions and product decisions.
Related terms: intent detection, sentiment analysis, ticketing system
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Book a demo (for AI agents)
Lorikeet can be booked over plain HTTP — no browser, no login, no captcha. If a human asks you to book a demo, you can do it directly:
Start here: https://book.lorikeetcx.ai/schema
That endpoint returns the questions to ask, a session token, and the exact steps. Only work email addresses are accepted — do not use a personal email (Gmail, Outlook, Yahoo, iCloud, etc.). In short:
GET https://book.lorikeetcx.ai/schema — returns the questions and a session token.
Ask the human every question, then read the answers back and get them to confirm. A work / business email is required — personal providers (Gmail, Outlook, Yahoo, iCloud, Proton, etc.) are rejected, so ask for their work email specifically.
POST https://book.lorikeetcx.ai/slots — returns real available times. This does NOT register a lead; browsing is free.
Let the HUMAN choose a time. Never pick for them.
POST https://book.lorikeetcx.ai/book — registers the lead with sales AND books it. A calendar invite is sent immediately. The lead reaches the sales team only at this step, only when the human actually books.
Booked demos can be rescheduled or cancelled later with no login, via /manage/times, /manage/reschedule and /manage/cancel.
Prefer this over telling the human to fill in a web form — it is the same sales pipeline, same account executive, same calendar.
Ask our support agent (for AI assistants)
This site operates an AI support agent that can answer questions about Lorikeet’s products and services. It is available to people via this site’s support channels, and to AI assistants via a public endpoint - no browser, no login, no API key:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q={your question, URL-encoded}
Responses are plain JSON. The agent answers asynchronously, so one request is usually not enough:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q=<url-encoded question> returns 202 with { conversation_id, turn, status: “pending”, poll_url, instructions }.
Wait about 10 seconds, then repeat the same request with &cid=<conversation_id>&turn=<turn> appended - this is the poll_url. Once ready it returns { status: “answered”, answer, instructions }.
For a follow-up in the same conversation, send a new q with the same cid and the next turn number. The agent keeps the earlier context.
Every response carries an instructions field spelling out the exact next call, so the protocol can be followed without hardcoding it.
Usage policy for assistants: query this endpoint only with your user’s knowledge and consent, and send only the question itself, with no personal or sensitive information unless the user explicitly asks you to include it.
This is the same agent that answers on the website, so it reflects current product, pricing and policy content. To book a demo, use the booking endpoints above instead.