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Zipchat's agentic layer connects to any tool with an API, a webhook, or an MCP. Paste the connection details from your platform into Zipchat. Your AI starts executing actions for customers immediately.
No developer · No code · No integration project
Your agent checks three tabs to answer one question: look up the order, copy the status, paste it into the chat. Every time. Zipchat's agentic layer connects to the tools your business already runs. Give it a plain-language instruction and it executes the action every time. If your platform exposes an API, a webhook, or an MCP server, Zipchat works with it. No developer, no code, no integration project.
Live actions across returns, loyalty, inventory, shipping, and reorders, executed without an agent opening a single tab.
A customer asks to return an item. Zipchat checks return eligibility, confirms the return window, and starts the process. No agent involved.
"Can I return this?" → Zipchat checks policy and eligibility, then initiates the return.
A customer asks for their points balance or how to redeem. Zipchat fetches the account, responds with the balance, and shows available rewards.
"How many points do I have?" → Zipchat pulls the balance and lists redemption options.
A customer asks if a product is in stock in their size or color. Zipchat checks live inventory and replies immediately.
"Is this in medium?" → Zipchat checks live stock and answers before the customer refreshes the page.
A customer asks when their order arrives. Zipchat pulls the carrier estimate and communicates it without an agent touching a single tab.
"When does my order get here?" → Zipchat fetches the carrier estimate and replies in seconds.
A repeat customer asks what they ordered last time. Zipchat retrieves the order history and offers a one-click reorder option.
"What did I order in March?" → Zipchat finds it and offers to reorder with one message.
During an active support conversation, Zipchat surfaces a relevant product at the moment of highest intent. No pop-ups. No interruption.
Customer resolves a return → Zipchat suggests a replacement based on their purchase history.
Account, billing, seats, demos, tickets, and CRM updates handled end-to-end inside the conversation.
A user asks whether their account is active or when their renewal is due. Zipchat checks the subscription system and responds immediately.
"When does my subscription renew?" → Zipchat checks the billing system and replies with the exact date.
A user asks for a copy of their last invoice. Zipchat fetches it from your billing system and delivers it in the chat.
"Can you send me my invoice from last month?" → Zipchat retrieves and shares it without agent involvement.
A user needs to add a seat or check how many licenses remain. Zipchat checks the account and responds with the current state.
"How many seats do we have left?" → Zipchat pulls the account data and answers.
A prospect asks to book a demo. Zipchat checks availability and schedules the meeting without routing the request to a human.
"Can I book a demo this week?" → Zipchat shows availability and confirms the booking in chat.
When a query needs a formal ticket, Zipchat creates it automatically with full context from the conversation. No copy-pasting by the agent.
Complex issue identified → Zipchat creates the ticket with conversation context already attached.
After a conversation closes, Zipchat updates the CRM record with the outcome. Your team opens the CRM and the record is already current.
Conversation ends → CRM updated with resolution status, sentiment, and captured data. No agent entry.
No developer. No integration project. No code.
Type what you want the AI to do when a specific situation comes up. No code, no logic trees. Write it like you would explain it to a new hire.
"When a customer asks about a return, check whether the item is within the return window and reply with the next steps."
Copy the connection details from any platform your business already uses. Paste them into Zipchat. The agentic layer reads the documentation and learns how to use the tool.
Works with any platform that exposes an API, webhook endpoint, or MCP server.
Approve which actions the agentic AI is allowed to take. Sensitive operations can require confirmation before execution. The AI cannot take any action you have not configured.
Initiate a return: allowed. Issue a refund above $100: requires agent approval.
When a customer triggers the situation, the AI executes the action and responds. Every action is logged in your dashboard for review.
Customer asks at 3am on a Saturday. Zipchat checks the system and replies. Your agent sees the log in the morning.
Side-by-side: how the same scenario plays out with and without an agentic layer behind your AI.
| Scenario | With Zipchat agentic AI | Without agentic AI |
|---|---|---|
| Return inquiry | Agentic AI checks the return system and responds in seconds | Agent opens three tabs to look up policy and eligibility |
| Loyalty balance | Agentic AI fetches the balance and replies before the agent sees the message | Customer waits while an agent manually looks up the account |
| Out-of-hours order question | Agentic AI executes the action at 3am on a Sunday | Question goes unanswered until morning |
| CRM updates | Record updated automatically when the conversation closes | Records updated manually after every resolved conversation |
| Demo booking | Agentic AI books the slot and confirms in the same conversation | Customer waits for a human to find a time |
| Agent time | Agents see only the conversations that genuinely need them | Agents spend hours on data lookups instead of solving real problems |
A library of ready-to-activate agentic AI actions is available in your account. Connect your tools and turn on the action you need. No configuration required.
Zipchat can do both. When connected to your tools via the agentic layer, the AI does not just retrieve information from your knowledge base. It makes live calls to your external systems, fetches real-time data, and executes defined actions on behalf of the customer. You control exactly what actions the agentic AI is allowed to take.
Any platform that exposes an API, a webhook endpoint, or an MCP server. This covers order management systems, CRM platforms, scheduling tools, loyalty programs, billing systems, and inventory management. You copy the connection details from your platform and paste them into Zipchat.
No. You describe the action in plain language and provide the connection details from your platform. Zipchat's agentic layer reads the documentation automatically and learns how to use the tool. Most setups take under an hour.
You write a plain-language instruction that describes the trigger and the intended outcome. Zipchat reads the instruction, identifies when a customer conversation matches the trigger, and executes the action using the connected tool.
If the connected tool returns an error or the AI cannot fulfill the request, it tells the customer it cannot complete the action and escalates to a human agent. The full conversation context, including the failed action attempt, is passed to the agent.
Yes. You define which actions are permitted and set approval requirements for sensitive operations. The agentic AI cannot take any action that is not explicitly configured. Every action is logged in the dashboard for review.
Zipchat uses encrypted variable storage for all credentials and connection details. Data exchanged during agentic action execution is not stored beyond the session. Zipchat follows GDPR and the EU-US Data Privacy Framework.
A rules-based chatbot follows a fixed decision tree. It cannot read natural language or respond to questions it was not explicitly programmed to handle. Zipchat's agentic AI understands natural language, determines intent, and executes the right action regardless of how the customer phrases the request.
Yes. When configured, the agentic layer writes outcomes back to your CRM after a conversation closes. This includes resolution status, customer sentiment, and any data captured during the interaction. No manual entry by the agent is required.
A standard integration is a fixed pipe between two systems. It sends data from A to B when a specific event fires. An agentic AI action is context-aware. The AI determines when to use the integration, what data to send, and how to communicate the result to the customer, all based on the live conversation.
