AI policy assistant that cites the right clause
SleekAI adds an AI chatbot for AI Policy Assistant to your WordPress site. It answers from the content you choose, such as posts, pages, custom post types, post meta, taxonomies and user data, with your own OpenAI, Anthropic, Google, or OpenRouter key.
Policies don’t get read, they get asked about
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.
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, so the public terms bot and the internal staff handbook bot are two separate scopes running on the same install.
Features
Cites every clause
Instruct the bot to name the policy and section on every answer. The visitor sees a referenced paragraph instead of a paraphrase, and the citation lets them open the source document directly if they want the full context.
Role-scoped
Display conditions restrict the bot by role. The public terms bot sees only the public terms; the internal handbook bot sees only employee docs. The same install runs both without leakage between scopes.
Audit trail
The log is the audit trail the policy team has always wanted, and the editorial backlog that points to the gaps in the handbook.
Where SleekAI sits between staff and the policy
Employee handbooks
Staff query PTO, expense, parental leave, and conduct policies without paging HR. The bot cites the section, so the answer is checkable. HR gets a log of which policies need to be clearer.
Customer terms and privacy
Visitors ask plain-language questions about terms, privacy, and data handling. The bot grounds in the published terms post and cites the section, instead of summarising the legal page in a way that could be wrong.
Vendor and partner policies
Account managers and procurement query SLA terms, partner agreements, and onboarding policies. The bot reads the partner documents and cites the clause, so the answer is consistent across reps.
How SleekAI runs a grounded policy bot
Map the policy sources
Group them by audience: employee handbook, customer terms, vendor agreements. Each group becomes the scope for its own bot.
Map your data
Choose the posts, fields, taxonomies or users the bot should know and reference them in the instruction.
Restrict by role
Use display conditions and Multibot to scope each bot by audience. Public terms bot loads on public pages; the employee handbook bot loads only for logged-in staff with the right role. Anonymous visitors never see internal scopes.
Read the question log
Failed grounding signals are the policy gaps. The log is the audit trail and the editorial backlog combined.
Why a policy assistant turns documents into operations
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.
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.
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.
Frequently asked questions
Is the bot’s answer legally binding?
No, and the system prompt should say so. The bot is a navigator over the policy, not a substitute for it. Tell the bot to recommend that visitors consult HR, legal, or compliance for binding decisions, and to cite the source clause so the human can verify before acting. That posture is what keeps the assistant useful and defensible.
How do I keep the bot’s answers current with policy updates?
When the policy team updates the document, the bot’s next answer reflects the new clause. Avoid duplicating policy text into the system prompt, because the prompt becomes a parallel source of truth that drifts the moment someone forgets to update both places.
Can the bot quote policies verbatim?
Yes, when instructed to. For legally sensitive questions, having the bot quote the clause verbatim plus a citation is the safest pattern. For navigational questions, a paraphrase plus a citation is friendlier. Mix the two by telling the bot in the prompt when to quote and when to summarise.
Can different teams see different policy scopes?
Yes. Multibot lets you run separate bots per audience, each with its own document scope and prompt. The customer terms bot doesn’t see the employee handbook, and the employee handbook bot doesn’t see vendor agreements. Role-based display conditions enforce the boundaries automatically.
Which providers can I use?
OpenAI, Anthropic, Google Gemini, OpenRouter, and any OpenAI-compatible endpoint. For policy work, larger and more recent models tend to follow citation instructions more reliably.
Will it hallucinate a policy that doesn’t exist?
Only if you let it. Tell the bot in the system prompt to decline when grounding is missing and to never invent clauses. With a well-grounded archive and a strict refusal rule, hallucinated policies become extremely rare. The conversation log makes any that do slip through visible quickly, so the prompt or grounding can be tightened.
Which AI providers does it support?
OpenAI, Anthropic, Google and OpenRouter, plus custom OpenAI-compatible endpoints. You bring your own API key and the provider bills you directly.
Where does the chatbot get its answers?
From the WordPress data you map into its instruction: post titles and content, post meta, ACF and Meta Box fields, taxonomies, terms, users and user meta, or a WP_Query. Values resolve on every request.
Pricing
Explore our flexible licensing options tailored to your needs. Upgrade your license anytime to access more features, or opt for a lifetime license for ongoing value, including lifetime updates and lifetime support. Our hassle-free upgrade process ensures that our platform can grow with you, starting from whichever plan you choose.
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