No. SleekAI is configured to refuse individualized financial advice, specific investment recommendations, and any blanket statement about expected returns. The bot can explain what the product does, who it is for, and how it compares to documented alternatives, but it cannot tell a specific user where to put their money. That boundary is enforced in the system prompt and reinforced by the guideline filter, and it holds under leading questions because the refusal is grounded in policy rather than caveats. The bot routes financial-advice questions to the user's own CFO, financial advisor, or relationship manager.

Yes, if you publish it on the Compliance page. The bot reads from your real Compliance entries, including KYC vendor (Persona, Alloy, in-house), AML monitoring approach, sanctions-screening cadence, and regulatory posture. It does not invent regulatory claims, because false compliance statements create direct regulatory exposure. The system prompt explicitly forbids claiming certifications or licenses that are not on the page, and the conversation log captures any place where a prospect's expectation does not match published reality, which is useful intelligence for the compliance team.

Yes. The system prompt can encode rules like 'companies with under $5M in monthly volume route to self-serve, $5-50M route to mid-market sales, $50M+ route to enterprise treasury'. The bot asks one qualifying question (monthly volume or balance range) and routes accordingly. Enterprise treasury intake captures different fields than SMB intake (entity structure, banking partners, expected liquidity profile), and the bot can capture them conversationally rather than through a long form.

Display conditions exclude the chatbot from disclosures, prospectus, and regulatory-filings pages. Those pages are governed by specific regulator requirements about presentation and notification, and a chatbot adds risk without adding value on those surfaces. The bot stays on product, pricing, and compliance pages where it accelerates the buying conversation. This kind of page-level exclusion is exactly what display conditions are built for, and it is a standard pattern for any fintech site that has a public investor-relations or regulatory-disclosures section.

Yes. SleekAI is multibot, so the corporate-card product page can run a card-specific bot and the bill-pay page can run a bill-pay bot. Display conditions scope each bot to the right URL pattern, post type, or UTM source. This matters for fintech because the product-specific KYC, fee, and compliance details differ enough that a single umbrella bot ends up with a cluttered system prompt and weaker answers. Multibot keeps each product's voice clean and the answers specific.

The chatbot does not handle card numbers, PINs, or any cardholder data, so it stays out of PCI scope. The system prompt explicitly refuses to accept payment details in chat, and the guideline filter blocks numeric patterns that look like PANs. If a customer pastes a card number, the bot apologizes, refuses to process it, and routes them to the secure checkout or account-management UI. That posture is the standard for any chatbot on a fintech site, and it keeps the chatbot surface decoupled from the PCI-scoped systems.

Yours. SleekAI is BYO API key, so you bring your own OpenAI, Anthropic, Google, or OpenRouter key. For fintech specifically, the BYO posture matters because security review will want to see exactly which model provider sees prospect conversations, under what data-processing agreement, and with what retention posture. BYO key lets you choose providers your security team has already vetted, and switch providers if their posture changes. There is no per-message markup, which also makes the chatbot economics line up with the rest of your usage-based infrastructure costs.

On your WordPress install. You control retention and exports, and you can pipe interesting conversations to a Slack channel for the GTM or compliance team via webhook. For fintech sites that the conversations never touch a third-party SaaS, except the LLM API you chose, is the deciding factor over hosted chat tools in security review. Logs include model name, token usage, and the page URL, which makes attribution and compliance auditing straightforward when a regulator or auditor asks for a sample of marketing-site interactions.