Insurance distribution is high-consideration and rules-heavy: coverage terms, exclusions, eligibility criteria, and regulatory constraints all shape which product a prospect can actually buy. Generic chatbot tools handle FAQs but cannot reason over product rules and customer context at the same time. Kleio is purpose-built for advised, high-consideration sales journeys, with a Knowledge Engine that unifies product and coverage data, eligibility rules, customer history, and policy documents so AI agents qualify protection needs accurately and hand advisors quote-ready leads. Kleio runs at enterprise scale in automotive retail, real estate, and travel, verticals that share insurance's advisor-led dynamics.

AI agents engage every inbound enquiry within seconds, then ask structured questions to capture life stage, assets, existing cover, risk exposure, budget, and renewal timing. They surface eligibility constraints early so unqualified prospects don't consume advisor time, run multi-channel follow-up sequences across email, SMS, and WhatsApp, and enrich the CRM automatically. Advisors receive a complete prospect profile with the protection need already scoped, instead of a name and a phone number.

Yes, but only when the data architecture supports it. Generic tools reason over flat documents and invent coverage terms, which is unacceptable in insurance. Kleio's five-store Knowledge Engine separates product and coverage data, pricing and availability, customer profiles, relationship graphs, and document retrieval, so agents cite actual policy wording, respect eligibility rules, and explain exclusions accurately rather than approximating. When a question falls outside what the indexed data supports, the agent escalates to a licensed advisor instead of guessing.

A chatbot answers FAQs and routes the prospect to a form or a callback queue. An AI insurance agent qualifies the protection need, explains coverage options in plain language, checks eligibility, prepares the quote request, books the advisor callback, runs multi-channel follow-ups, and updates the CRM with full context. The difference shows up in conversion: prospects get substantive answers while intent is still high, rather than waiting for a callback that often never comes.

Most insurance journeys still force customers through long forms before showing anything useful, which drives heavy drop-off. AI agents invert that sequence: they answer coverage questions, compare options, check eligibility, and gather what a quote requires conversationally, 24/7. For existing policyholders, agents handle renewal questions, cover adjustments, and document requests. Human advisors step in for the cases where regulated advice and judgement genuinely add value.

Insurance distribution is regulated, through the IDD across the EU, FCA rules in the UK, and state-level regulation in the US, so scope matters. Kleio agents are configured for pre-sales work: qualification, information, coverage explanation, and quote preparation, with regulated advice and the recommendation itself reserved for licensed advisors. Every interaction is logged and auditable, agents cite source policy documents rather than paraphrasing them, and hallucination controls prevent inaccurate claims about cover or price. Kleio is SOC 2 and GDPR compliant, and the precise boundary between information and advice is set with your compliance team during deployment.

Yes. Kleio connects to policy administration platforms, quote and rating engines, broker management systems, and CRMs including Salesforce, Microsoft Dynamics, and HubSpot through standard connectors and APIs. The Knowledge Engine ingests product and coverage data, rating inputs, eligibility rules, and customer history in real time, so agents always work from current information. Every AI-driven quote, follow-up, and booking syncs back, so advisors see the full interaction history in the systems they already use.

Comparable deployments in advisor-led verticals run 8 to 12 weeks. For insurance, most of that time goes into modelling your products, coverage terms, and eligibility rules, indexing policy documents so agents can cite them accurately, connecting quote engines and CRM, configuring the compliance boundary between information and regulated advice, and connecting messaging channels for automated follow-up. Customer-facing and advisor-copilot use cases deploy in parallel.

Kleio's enterprise deployments today are concentrated in automotive retail, real estate, and travel, including Emil Frey (Autosphere), Orpi, and TTC Tour Brands. These verticals share insurance's defining characteristics: advised sales journeys, complex product and pricing rules, and distribution through large agent networks. Insurance deployments are in development, and the underlying Knowledge Engine architecture transfers directly. Reference conversations with adjacent-vertical clients are available on request.

Yes. Kleio automates the repetitive, high-volume work that fills an insurance agent's day: collecting the information a quote requires, preparing quote requests and indicative comparisons, running multi-channel follow-up sequences across email, SMS, and WhatsApp, scheduling advisor calls around real calendar availability, reactivating lapsed prospects ahead of renewal dates, and updating the CRM after every interaction. Agents keep ownership of the advice, the relationship, and the close, while the AI removes the admin around it.

Licensed agent capacity is the binding constraint in insurance distribution, and it cannot be scaled quickly, since hiring and licensing take months. Kleio gives each agent a copilot that automates qualification, quote preparation, follow-up, and scheduling, so each one handles significantly more pipeline while keeping control of advice and closing. AI also extends coverage to evenings and weekends, engaging enquiries instantly so agents start each day with qualified pipeline rather than a backlog of missed enquiries.