Multi-brand customer support
Multi-brand customer support is the capability to serve customers across multiple brands, sub-brands, or product lines from a single platform — with each brand maintaining its own identity, policies, tone of voice, and knowledge base. This is a common requirement for holding companies, multi-product businesses, and companies operating across regions with different brand identities.
The complexity of multi-brand support is often underestimated. Each brand may have:
Different products, pricing, and policies
Different tone of voice and communication style guidelines
Different regulatory requirements (especially across regions)
Different knowledge bases and escalation paths
Different SLAs and operating hours
Shared backend systems but separate customer-facing identities
For traditional contact centers, multi-brand support typically means separate agent teams (or at minimum, separate training) for each brand. This creates operational overhead: duplicate staffing, duplicate QA processes, and difficulty sharing best practices across brands.
AI changes this equation. A well-architected AI agent platform can serve multiple brands from a single deployment — switching between brand personalities, policy sets, and knowledge bases based on which brand the customer is interacting with. This delivers the operational efficiency of a single platform with the customer experience of dedicated brand support.
The key architectural requirement is true separation of brand contexts within the AI system, not just cosmetic customization. The AI needs distinct knowledge bases, distinct guardrails, and distinct personality configurations per brand — not a single model with brand-name tokens swapped in.
This capability is particularly valuable for insurance companies (multiple product brands), financial services groups (multiple financial products), and consumer companies operating across regions.
Related terms: AI concierge, omnichannel customer support, AI guardrails
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.