Natural language processing (NLP)
Natural language processing (NLP) is the field of AI focused on enabling machines to understand, interpret, and generate human language. It encompasses the technologies that allow AI systems to read text, understand speech, extract meaning, and produce natural-sounding responses.
NLP has evolved through several generations:
Rule-based (1960s-2000s): Manually coded grammar rules and pattern matching. Brittle and expensive to maintain.
Statistical (2000s-2010s): Machine learning models trained on labeled data. Better at handling variation but limited by training data.
Neural/transformer-based (2017-present): Deep learning models (BERT, GPT, etc.) that learn language patterns from massive datasets. Dramatic improvement in understanding context, nuance, and ambiguity.
In customer service, NLP is the foundation for:
Understanding customer messages: Parsing intent, extracting entities (dates, account numbers, product names), and interpreting sentiment
Generating responses: Producing natural, contextually appropriate replies
Processing unstructured data: Analyzing free-text feedback, survey responses, and social media mentions
Multilingual support: Handling customer interactions across languages
For CX teams, the practical distinction that matters is between NLP as a component and NLP as a solution. Having strong NLP capabilities doesn't automatically translate to effective customer service AI — the NLP needs to be combined with system integrations, business logic, guardrails, and operational workflows to deliver value. The best NLP model in the world is useless if it can't access the customer's account or execute a refund.
Related terms: intent detection, sentiment analysis, large language model, conversational AI
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.