Via standard email or webhook. Help Scout, Freshdesk, Zendesk, HelpScout, Awesome Support, Fluent Support, and any helpdesk that accepts inbound email tickets work without a custom connector. The bot sends the full transcript and any captured fields (license key, order number, account email) on escalation, so the agent picks up a thread with the context already framed instead of asking the customer to repeat themselves.

Yes. For corpora that exceed the practical system-prompt budget, SleekAI can use OpenAI's Files vector store (up to 1GB per file) so retrieval stays fast on hundreds of articles. The standard WordPress data-source wizard covers smaller libraries directly via WP_Query. Either way, the bot cites the grounded source so customers can confirm and the team can audit.

If the prompt is written that way, yes. Tell the bot to refuse rather than guess on policy, billing, and account questions, and to escalate to a human when grounding doesn't cover the ask. The cost of a guessed refund window is much higher than the cost of an escalation, and the logs make it easy to tighten scope before it costs trust.

Display conditions support logged-in state and user role, so the bot can ground against the current user's account data (plan, license, order history) via the existing WordPress user object and any account CPT or postmeta. For logged-out visitors the bot scopes to public docs and policies and prompts to log in or open a ticket for account-specific questions.

Most teams either replace the generic widget or run SleekAI on docs and account pages while keeping the existing live-chat tool on the home page for sales. Multibot supports this split natively. The data the bot collects on escalation can also POST to the existing live-chat tool's inbound webhook if you want a single inbox view across both.

Two numbers from the logs: the share of conversations that ended without an escalation request, and the share of escalations whose first reply from the team didn't need a clarifying follow-up. The first measures volume removed from the inbox; the second measures the quality of the context that arrived with the escalations that remained. Both move together as the prompt and the docs tighten.

It can quote the policy accurately and refuse to extend it. The bot doesn't authorise overrides; those still go to a human. What it does prevent is the inconsistent answer pattern where two different agents give slightly different windows, which is the case customers learn to exploit. A grounded bot answers the same way every time from the same canonical policy page.

Most support conversations are 4 to 10 turns at 500 to 1500 tokens per turn, which lands at well under a cent per resolved chat on a mid-tier model. Deflection rates of 30 to 60 percent of inbound volume are realistic on a well-documented product, and the comparison against agent-minutes saved is usually two orders of magnitude favourable. Logs record token usage so the budget is visible.