Key facts
| Payment boundary | Keep card and payment fields out of prompts; tools return status, not credentials |
| Access control | Scoped API keys with rotation; enterprise SSO and RBAC options |
| Data grounding | Embeddings and RAG are live for policies, shipping rules and catalog data |
| Deployment | Cloud, VPC, on-prem or air-gapped for restricted data |
| Auditability | Request, model and response logging for support review |
| Structured output | JSON mode returns categorized tickets and enrichment records in schemas |
| Pricing model | Flat monthly self-serve plans; no per-token billing on self-serve |
| Endpoint roadmap | Moderation, images, audio and batch are coming soon |
TL;DR
- Never let card or payment credentials enter a prompt.
- Authenticate customers in your own service before any order lookup tool runs.
- Isolate sessions so one customer can never retrieve another's data.
- Review AI-generated copy for claims, pricing and compliance.
- Log support interactions for dispute handling and quality review.
How it works, step by step
- Map where customer, order and payment data flows, and mark what may reach the model.
- Keep payment fields out of prompts; design tools that return only the status needed to answer.
- Enforce authentication and authorization in your services before any account tool executes.
- Scope keys per storefront, environment and integration, with rotation and a central inventory.
- Index policies, shipping rules and product data, and require citations on answers.
- Add a review queue for AI-generated customer-facing copy and promotional claims.
- Log request IDs, model versions, sources and outputs for dispute and audit review.
Try it yourself
Open the AI data residency checklist →
Keeping payment data out of prompts
Payment data expands your compliance scope the moment it reaches a model. The control is simple to state and worth enforcing strictly: the model never sees card numbers, security codes or full payment tokens. Expose a tool that returns only what the answer needs - paid, pending, refunded - and keep the underlying data in systems you already govern.
Apply the same rule to logs. If a field should not be in a prompt, it should not be in a log either.
Customer isolation in support AI
Support assistants fail in a predictable way: one customer's session retrieves another customer's order. Prevent it structurally. Authenticate in your own service, pass a scoped session context to the tool layer, and never let the model choose the customer identifier. Log the authenticated principal with every tool call so disputes can be investigated.
Where a storefront serves regulated regions, use region selection or private deployment to match local expectations.
Keys, integrations and peak load
Ecommerce runs many integrations: storefront, helpdesk, order management, marketing. Give each one its own key so a leak or bug has a bounded blast radius, rotate on schedule, and revoke when a vendor changes. Flat monthly self-serve plans keep platform cost stable through promotional peaks, which matters when traffic multiplies for a weekend sale.
Fraud workflows and human review
AI can summarize a fraud case or draft a customer explanation, but the block-or-allow decision needs a human with authority and evidence. Use JSON mode so case records land in schemas your fraud tooling validates, and keep an audit trail for every recommendation and decision. Never let generated copy promise refunds, credits or policy exceptions without review.
Honest comparison
| Control area | Plugsky capability | Common gap | Owner |
|---|---|---|---|
| Payment data | Tools return status only; prompts stay free of credentials | Card data pasted into chat | Payments engineering |
| Customer isolation | Auth enforced in your service before tool execution | Model chooses the account ID | Platform security |
| Key management | Per-integration keys with rotation | One key for the whole stack | Security engineering |
| Copy review | Structured output plus human approval queue | Generated claims published directly | Brand and legal |
| Residency | Region selection plus VPC, on-prem and air-gapped | Unknown processing location | Privacy |
| Audit trail | Request, tool-call and response logging | No dispute reconstruction | Support operations |
Frequently asked questions
Can AI process payment data?
Keep it out of the model path. Use tools that return a status or outcome, and leave card data in systems that already meet your payment obligations.
How do we stop cross-customer data exposure?
Authenticate in your own service, pass a scoped session to the tool layer, and never let the model select or modify the customer identifier.
Is the moderation endpoint available for reviews and chat?
Moderation is coming soon. Until then, apply your own filters and human review to user-generated and AI-generated content.
How do we handle peak-season traffic?
Self-serve plans are flat monthly with fair-use usage, so bills do not spike with traffic. Test latency before campaigns. See the live pricing page for plan details.
Should generated product copy be reviewed?
Yes. Claims, pricing references and regulated categories should pass a human review step before publishing, with the source recorded.
What about regional privacy expectations?
Choose region selection or a private deployment according to where your customers are and what your policies promise.
What is live today?
Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Moderation, images, audio, files, batch, assistants, responses and fine-tuning are coming soon.