A life insurance prospect rarely knows what they want when they hit the site. They know they need 'some kind of policy', they've seen advertisements for both 'term is cheap' and 'whole life builds value', and they're trying to figure out which of those messages applies to them. The agent on the other end of the call is patient about it, but the agent's time is finite and the same conversation happens fifty times a week. SleekAI runs the educational layer of that conversation on the site, in plain language, without any of the breathless framing that makes life insurance marketing feel like a sales trap.

The bot is grounded in the brokerage's product list, carrier appetite notes, and target underwriting bands. It explains what term, whole, IUL, and final expense actually are, what they typically cost in rough bands by age and health, and what kind of buyer each one tends to fit. It captures intended use (mortgage protection, income replacement, business continuation, estate planning, burial expenses) so the agent walks into the call already knowing whether to lead with term or with permanent. None of that requires a license to talk through, and yet it's the part of the conversation that determines whether the agent gets to the right product on the first try.

The boundary is hard-coded. The bot never quotes a binding premium, never gives a personalised illustration, never asks for medical specifics beyond rough health-class signals, and never describes itself as licensed. Personalised recommendation and binding quotes route to a licensed agent in the prospect's state. Compliance language uses 'estimate', 'illustrative', and 'subject to underwriting' consistently. That's what makes the bot safe to deploy on a life brokerage site without inviting state DOI scrutiny.

Life insurance is a product where the worst possible chatbot failure is also the most tempting one, a confident, specific, personalised recommendation that the bot has no business making. The model will happily generate it if the system prompt doesn't lock the boundary, and the result is a state DOI complaint at best and an E and O claim at worst. SleekAI's value on a life brokerage site comes from the discipline of the system prompt and the structure of the routing logic.

The bot handles every educational and process question (product type definitions, intended-use framing, rough age-banded premium ranges, accelerated underwriting eligibility, what to expect during a paramed exam, what 'preferred non-smoker' actually means) and defers every personalised recommendation to a licensed agent in the prospect's state. It uses 'estimate' and 'subject to underwriting' language consistently, it never binds, and it never collects medical detail that the public site has no business storing. The compliance posture matters because life insurance is heavily regulated and a chatbot that crosses the binding line on a public site is not a customer-experience issue, it's a licensing issue.

Transcripts live in WordPress with model and token data so the agency can export to E and O archive on a recurring schedule. Carrier and appetite data live as pages or ACF fields so the agency owns the source of truth. The lead-quality benefit, prospects who arrive at the agent call already understanding what kind of product they're considering, is real, but it's downstream of the compliance discipline.

Get the boundary right first, and the lead quality follows; get the boundary wrong, and no amount of intake polish makes the bot a safe addition to the site.