Meet Mentiora Copilot: one agent for your customers and your team

Mentiora Copilot delivers:

• In Zendesk: a drafted reply waiting when you open a ticket, with the context gathered and the quality checks applied
• In the Mentiora platform: describe a change in plain language and the copilot wires it in; your team's edits become training for the agent
• In Claude and ChatGPT: the same copilot over MCP, so you can inspect, change, and test your agent from the chat you already use

Beside every ticket in Zendesk

A Zendesk admin connects Mentiora and the app installs itself during sign-in. From then on, when a request reaches your inbox, the copilot gets to work: by the time you open the ticket, it has drafted a reply, gathered the relevant context across your integrated systems, and checked the draft against your internal policies. You can insert the draft as it is, or ask the copilot in the panel beside the ticket to pull more information or rework the draft. It prepares the ticket actions too (tags, fields, status) for your agent to review and apply in place.

Your agents send every customer reply, and nothing touches a ticket until they say so. The edits they make become training for the agent.

One agent, from your customers to your team

Mentiora Copilot is the same agent that talks to your customers, continued into the places where your team works. Mentiora connects it to the systems that hold your context: Slack, Confluence, Notion, your internal backend, and the history of previous conversations.

Some of that context you would not hand to a customer-facing agent automatically: an internal thread about an ongoing incident, a runbook, the way a similar case was resolved last month. The copilot brings it to your people instead, beside the ticket, so they work faster and decide what the customer sees.

AI can make mistakes. What matters is that the mistakes become training. Every suggested reply, along with the quality control corrections and the edits your team made, is preserved with its full context and enters your quality engine. Where QA used to sample, the quality engine sees every assisted reply. The platform analyzes this data to find the gaps and what causes them, prepares the configuration change, and runs simulations to check the fix holds before you release it. Every edit your team makes this week is a mistake the agent does not make next week.

In the platform

Editing by describing the change: the copilot confirms the shape, then wires the step into the workflow.

The same copilot in Claude and ChatGPT

Mentiora Copilot also works where you already think. Connect Mentiora over MCP and drive your project from Claude or ChatGPT: ask what your agent's policy says, describe a change, and the copilot patches the configuration as a draft and opens a live chat with your agent right in the conversation, so you can test the change on the spot and release it when it holds. Changes made here update the same agent that drafts in Zendesk.

In Claude

From Claude: read the policy, patch it, and test the change in a live chat with the agent.

Quality unlocks automation

Closing the quality gap across a scenario unlocks automation: the agent reliably gets to the right resolution, and your team decides which scenarios graduate from reviewed drafts to automated handling. The same analysis shows where the gaps originate, whether it is knowledge that lives in people's heads, undocumented scenarios, or stale docs that contradict practice, and Mentiora recommends the changes and additions to your materials.

Readiness

Readiness by intent, and where the knowledge gaps come from.