Every team with a serious policy archive has the same workflow. Someone has a question, they ask a colleague, the colleague guesses or checks the PDF, and the answer lands in Slack without ever being written down. The policy document exists for legal cover, not for use. The actual operational source of truth is whichever five people happen to remember the relevant clause this quarter, and that group changes every time someone leaves.

SleekAI grounds replies in your real policies: handbooks in WordPress, terms pages in posts, compliance PDFs in the vector store, ACF clause fields, taxonomy-tagged exceptions. Every answer cites the document and the section so staff and customers see where the answer came from. Display conditions restrict the bot by role and capability, so the public terms bot and the internal staff handbook bot are two separate scopes running on the same install.

Conversation logs become the audit trail the policy team has always wanted. Filter by clause to see which policies generate the most questions. Filter by failed grounding to find the gaps in the handbook. Filter by user role to see how policies land differently across teams. That data is what turns a static policy document into a living artifact, instead of an annual rewrite nobody reads.

Policy documents have always lived two lives. The first life is the legal artifact, written carefully, reviewed by counsel, and stored in a folder for compliance posture. The second life is the operational reality, where actual staff and customers ask questions about what the policy means, and the answer comes from whichever colleague happens to remember the relevant clause this quarter.

The gap between the two lives is the source of most of the inefficiency around policies. The legal team writes a 40-page handbook; the operational team uses three sentences from it; nobody ever updates the document until the next annual review. SleekAI closes that gap.

The bot reads the canonical policy, cites the section, and gives staff and customers a navigable interface over text that has historically been unreadable in practice. The change is subtle but profound: the policy document becomes operationally useful, not just legally complete. Editors get a feedback loop because the questions that generate weak grounding are the policies that need work.

HR and legal get an audit trail because every conversation logs the question, the cited section, and the reply. Customers get answers that match the actual published terms, instead of paraphrases that may or may not be accurate. None of this requires throwing away the existing policy.

The handbook stays where it is. The bot just makes it answer questions, which is the only reason anyone wrote it in the first place.