Resolve the request. Keep the relationship.
A customer asks about a renewal. The answer is in one system; the subscription change is in another. When AI can connect the two, the request can be completed in the conversation instead of becoming another task for your team.
Varex connects AI agents to Zendesk, your product knowledge and approved account actions. They can answer, investigate and act, choosing the next step from the context of the conversation rather than a rigid decision tree.
AutoFlow makes this approach practical to implement and maintain. It turns your ticket history into tested support flows, then uses results to improve how the agents handle requests. Your team reviews real cases and proposed changes instead of designing every flow from scratch.
AI action accuracy
Correct actions—not just convincing answers.
Savings on refunds
With the Negotiator agent.
Average CSAT increase
After switching from human-managed support workflows.
Reported operational results. Outcomes vary by deployment, business policy and workload; these figures are not a guarantee for every project.
Zendesk at the center. Your product connected.
Keep Zendesk as your team’s workspace. Varex connects the conversation to the account, subscription and billing systems needed to finish the request. Routine checks and approved changes can happen together, leaving your specialists the exceptions that need their judgment.
Work from the relevant context
Combine the conversation with approved product knowledge and permitted account information. Check identity and access before retrieving private details.
Carry out the permitted action
Connect each request to the checks and actions needed to finish it:
- Billing and renewals: check the subscription and account history, then explain the charge using the relevant context.
- Account and plan changes: verify permissions, make an approved change and confirm the result in the conversation.
- Cancellations and refunds: check eligibility, complete the permitted action and pass exceptions to your team with the context attached.
Your business rules determine what the agent can do at each step.
Hand over without starting over
Pass the conversation, relevant account context, completed checks and unresolved decision to a person. Failed tools, policy exceptions and a customer's request for human help should lead to a useful handoff, not another loop.
Choose the next step from the context.
A fixed workflow assumes you already know every turn a conversation will take. Varex agents use the customer's intent, available context and tool results to choose what to ask, check or do next.
That can mean resolving a product issue before discussing renewal, asking one necessary question instead of repeating a script, or skipping an irrelevant step when the answer is already known.
The path can adapt. Your permissions, refund policy and approval requirements remain the boundaries. Adapting a conversation does not give an agent permission to rewrite your rules.
Meet Negotiator. Retention with a reason to stay.
A customer may want to leave because a feature is unclear, the plan is a poor fit or a problem remains unresolved. Negotiator uses the conversation to understand the reason before choosing a relevant response.
When an approved alternative genuinely fits, Negotiator can offer it in the same conversation. A solvable problem need not become a lost subscription simply because the customer received a generic cancellation script.
When cancellation or a refund is the right outcome, the agent respects the decision and follows your policy. Retention is measured alongside customer satisfaction, repeat contacts and correctly completed actions.
Build from your tickets. Improve through real use.
Effective AI support needs a way to get started and a way to keep improving. AutoFlow is how we put that into practice within Varex: it connects the work your team already does to the support flows your AI agents use, from the first rollout through ongoing operation.
Start with the support work you already do
AutoFlow imports your Zendesk ticket history and uses agents' replies and actions to create a candidate support flow. We connect it to your approved knowledge, account tools and business rules. Your specialists review a concrete starting point instead of documenting every conversation branch for a developer.
Test before customers rely on it
Test the candidate on historical cases to check answers, permitted actions and handoffs. Your specialists and our engineers review the results before it enters a live comparison with the current flow. Start with a defined scope and expand when the evidence supports it.
Keep improving the support you run
AutoFlow compares the current and candidate flows, refines the new version and switches only when it is reliably better against the agreed criteria. If it is not, the current flow stays in place. Customer satisfaction, correctly completed actions, cancellations, refunds and human effort are evaluated together, with your policies and approval requirements as the boundaries.
This makes implementation and maintenance part of the same process. You spend less time specifying flows and rebuilding them when requests change; ticket-level results show where better context, a different action or a human decision is needed next.
Already using AI, but still doing too much by hand?
We investigate where the workflow breaks down, test targeted changes and improve accuracy, resolution and operating cost together. Bring us the process that needs to work better.
See the work the agent actually removes.
Agree the baseline, request types and success criteria before a pilot. Compare like-for-like cases, including the work still left for people.
- Action accuracy: did the agent complete the permitted action correctly?
- Customer satisfaction: did the resolution help, and did the customer need to contact you again?
- Cancellations and retention: did the customer choose to stay, without obstructing their right to leave?
- Refund impact: how did eligible refund outcomes and refunded value change?
- Operating cost: what human work, model usage and maintenance did the result require?
These measures help you see whether automation is removing work or merely moving it to another queue. They also show where a better answer or action can prevent avoidable customer loss.
Review the work behind each result
Review individual conversations alongside the actions taken, handoffs and final outcome. Check where the agent lacked context, chose the wrong action or needed a person. Turn those findings into test cases and the next AutoFlow improvement, while tracking resolution, time, cost and customer satisfaction together.
Your brand. Our AI support product.
Varex is already sold through white-label partners. We invite integrators and outsourced support teams to offer the product under their own brand.
For integrators: start with an existing AI support product and the engineers behind it. AutoFlow supplies the flow creation, testing and improvement process, so project effort goes into the client’s systems and requirements instead of repeatedly building agent infrastructure, testing tools and support logic. That gives you less bespoke work to price, deliver and maintain.
For outsourced support teams: your customer relationships and operational knowledge remain central. Offer branded automation alongside your services, with Varex supplying the AI product and engineering expertise. You can develop the offering without first assembling a full in-house AI product team.
We will show you the product and discuss branding, integrations, delivery responsibilities and commercial terms for your business. Start with a free demo—there is no need to invent a platform from scratch to explore the opportunity.
See it work on your product. Free.
Bring your product, one recurring support scenario and the policies that shape a good answer. We will demonstrate how Varex could handle that conversation, use the relevant context and decide when a person should take over. We will also show how AutoFlow uses your ticket history to build and test the flow, and how improvements are evaluated after rollout.
The demo is free. Production integrations and rollout are scoped separately, so you can evaluate the approach before committing to an implementation.
Please do not email passwords, API keys or personal customer data. If examples or access are needed, we will agree a suitable way to share them.