No. The system prompt explicitly forbids legal interpretation. The bot describes what your contract says, names the section, and defers to legal for anything that asks 'does this clause apply to my situation'. That boundary is non-negotiable on a terms page, because a chatbot improvising contractual claims is a real liability. The reduction in support load comes from answering the routine 'where does it say I can cancel' questions, not from automating dispute resolution.

SleekAI reads the terms-of-service page from WordPress on every request. Update the page, save, and the next chat reflects the new wording immediately. We strongly recommend versioning the page through WordPress revisions, so you can prove what the bot was saying on any given date if a customer dispute ever requires it. Many legal teams also publish a 'last updated' line and a changelog at the bottom of the terms page itself.

Yes. SaaS products typically have a stack of related agreements: master subscription agreement, data processing addendum, acceptable use policy, SLA, and sometimes per-region addenda. Multibot lets each document be its own bot with its own system prompt and source content. A procurement reviewer reading the DPA gets the DPA bot; a developer reading the AUP gets that one. They cross-link in chat without confusing the contexts.

The bot is configured to route disputes to your legal or contracts inbox. Phrases like 'I want to sue', 'breach of contract', or 'demand a refund under consumer law' trigger a polite handoff and capture the contact details for follow-up. The bot does not improvise positions in a live dispute. This is both legally safer and more reassuring to the customer than an AI summary of an adversarial situation.

Yes. The patterns are even simpler than B2B, because consumer terms are typically shorter and the questions are more predictable: cancellation, refunds, account deletion, dispute resolution. The bot is well suited to that surface, and consumer protection regulators in the EU and UK increasingly expect that consumer-facing terms be understandable, which a conversational layer materially helps with.

No, and we recommend not. Acceptance of terms should be captured by a deliberate checkbox or signature on the signup flow, with a timestamp and the version of the terms shown. A chatbot conversation is not a clean acceptance record. The bot is for understanding, not contracting. Pair it with the existing acceptance flow rather than replacing it.

Every conversation is logged in your WordPress database with timestamp, model used, token usage, and the page where the chat occurred. Combined with WordPress revisions on the terms page itself, you have a full audit trail: what the contract said, what the bot answered, when each occurred. That is more defensible than a typical human support log, which usually does not capture the version of policy the agent was reading.

Yes. The model replies in the language the customer writes in. The clause references and section numbers remain consistent, so a German-speaking customer can ask in German, get an answer in German, and still reference 'Abschnitt 5.3' when they follow up with support in English. We recommend documenting in the terms page itself that the canonical legal version is in a specific language and any translation is informational only.