If your AI bot takes support calls, runs web chat, or handles a voice channel, there's a moment in every conversation where the customer has to pay. That's the moment most people get wrong. Card details end up in an LLM's context window, a transcript, a call recording, or a log no one's sure is ever deleted.
We built a different pattern. Your AI keeps running the conversation. When the payment moment arrives, we take over the card capture on our PCI DSS Level 1 platform, process it, and hand control back. The AI never sees, hears, or stores a card number.
One API call keeps the AI out of PCI scope.
AI tooling wasn't built with PCI DSS in mind. LLM providers log inputs and outputs by default. Transcription services keep audio. Plenty of platforms route data through regions and services you don't control. None of them give you a clean “captured, processed, deleted, here's the log” chain of evidence.
If your bot hears a card number read out, or sees one typed into a chat window, you've got card data in places you can't certify as compliant. It might be sitting in an LLM provider's logs with a retention window you can't override, embedded in a transcript that feeds fine-tuning, recorded in a voice archive with no guaranteed deletion, or routed through a sub-processor your lawyers haven't bound contractually.
That's not a PCI DSS problem you fix with a policy document. It's a data-handling problem that needs a different architecture.
Web, WhatsApp, in-app bots
Your AI handles the conversation as normal. When it's time to take payment, your code calls our API with the amount, reference, and any metadata you want logged against the transaction. We return a payment link or an iframe. The customer enters their card on our hosted form, which runs on our PCI DSS Level 1 infrastructure — not yours, not your AI provider's. We tokenise the card, process the payment, and send you the outcome by webhook. The AI never sees the card, the transcript stays clean, and your logs never hold anything you'd need to redact.
Call-based bots and voice agents
When your AI voice bot reaches the payment step, the call splits into two channels. The customer keys their card on their own phone's keypad, guided by voice prompts from our platform. The DTMF tones are suppressed so they never reach the AI's transcription engine or the call recording. Your AI is off the audio path for those few seconds. Once the payment authorises, the channel reconnects and the AI carries on with the conversation. Same mechanism agent-assisted call centres already use, adapted for a call where the “agent” is a bot.
A clean line between payment data and conversation data. That's what makes the DPA defensible.
| Data | Handled by |
|---|---|
| Card details | Paytia |
| CVVs | Paytia |
| Bank account numbers | Paytia |
| Cardholder name and address (if captured) | Paytia |
| The conversation itself | Your AI |
| Session state, intent, business logic | Your AI |
| Order references, customer IDs, metadata | Your AI |
| Payment outcome (token, status) | You, via webhook |
A Data Processing Agreement defines who's responsible for each category of personal data, where it's stored, how long it's kept, how it gets deleted, and what happens if something goes wrong. Under UK and EU GDPR, it's not optional for any product touching personal data.
The problem is that AI platforms often have DPAs that don't cover sensitive payment data properly. Some explicitly exclude it. Some use blanket language that would pull the AI provider into PCI scope — which they don't want either. Some sub-process through LLM providers whose retention policies you can't override. If you're building on top of that and a regulated customer asks you for a DPA, you're stuck.
Our architecture takes that problem off the table. Card data never enters the AI scope, so your AI platform stays out of PCI scope. We're the data processor for payment data, with a defined environment, defined retention, and a defined deletion path. Your DPA becomes easier to draft because the responsibilities are actually separate in the architecture, not just on paper.
One API call starts a payment session. Webhook events cover every stage of the lifecycle. It works alongside whatever AI stack you're running — custom agent frameworks, managed voice platforms, in-house bots. Delivery is flexible: an iframe inside your UI, a redirect link over chat, or call-channel injection for voice. Sandbox and live share the same integration.
We don't change pricing based on whether a human or an AI kicked off the transaction. If you're a platform selling AI products into regulated industries, the combination of our PCI Level 1 certification and a clean DPA story is often what lets the deal close.
If you already have a capture layer and just need the AI-safe hand-off, see our Capture Assist API.
If you're running an AI-assisted contact centre, our contact centre page covers the operational side of the same architecture.
The hub for chat, Zoom, WhatsApp and AI agent payment flows.
Read moreVoice-specific deep dive — how AI call analysis and DTMF-masked capture work together on a live call.
Read moreThe wider AI telephony picture — Teams, WhatsApp, speech IVR, and secure payments on the same call.
Read moreWe'll show you the safest way to wire it up, and walk your legal team through the payment-data side of the DPA.