Quality assurance for AI customer service
Coach reviews every conversation across your support operation, human or AI, scores it against your standards and turns findings into fixes.
100% coverage. Not manual spot checks.
Coach QA
Every ticket scored Good, Warning or Critical
Coach’s Ticket Quality Score reviews 100% of conversations against your quality standards, AI and human alike, replacing manual spot checks with full coverage.
Defense in depth
Four layers of quality control
Agent quality, pre-deployment simulations, runtime guardrails and post-conversation QA work together, so issues are caught before, during and after every conversation.
Quality Guarantee
If quality fails, you do not pay
When Coach gives a conversation a bad score, we refund the AI portion of that interaction. Simply put, if our product fails your quality standards, you should not pay for it.
From insight to fix in one loop
Coach does not stop at scoring. It spots issues, finds opportunities and proposes workflow improvements you can validate with simulations and ship, all inside Lorikeet.
Frequently asked questions
How does Lorikeet QA AI customer service conversations?
Coach reviews 100% of tickets, scoring each conversation Good, Warning or Critical against your quality standards. Scores combine AI review with human calibration, covering both AI and human agent conversations.
What is a Ticket Quality Score?
The Ticket Quality Score, or TQS, is Lorikeet’s per-conversation quality rating. Every ticket gets one, giving you full coverage of your support operation instead of the small manual samples traditional QA relies on.
What happens when quality slips?
Runtime guardrails catch sensitive or off-policy moments during the conversation and escalate to your team, Coach flags Warning and Critical conversations for review, and our Quality Guarantee refunds the AI portion of any badly scored interaction.
Does Coach QA human agents too?
Yes. Coach applies the same quality standards across every conversation, human or AI, so you get one consistent view of quality across your whole support operation.
How is this different from traditional QA tools?
Traditional QA samples a few percent of tickets and stops at a scorecard. Coach reviews every ticket and closes the loop: findings become workflow improvements you can test with simulations and ship in the same platform.
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.