AI Service & Support
Give your support team an AI that can search manuals, SOPs, historical cases and equipment records — then troubleshoot, create cases, schedule work and hand off to the right engineer.
The problem
Product manuals live in folders, SOPs live somewhere else, and historical service records are stored in another system.
The fastest answer often comes from asking the same experienced engineer again.
Similar problems are diagnosed from scratch because previous resolutions are difficult to find and reuse.
When AI or frontline support cannot solve an issue, someone still has to manually create a case, explain everything again, schedule service and assign an engineer.
The problem isn't that your company lacks knowledge.
The problem is that knowledge and action are disconnected.
The approach
DYNARA connects company knowledge and operational data through QSyn, allowing the AI service agent to understand an issue, retrieve relevant context and decide what action should happen next.
Walkthrough
“The machine keeps disconnecting every few minutes. What should we check?”
The agent extracts what the message is actually about.
It looks across everything the company already knows.
The agent provides troubleshooting steps based on the retrieved information.
If the problem cannot be resolved, a service case is created — and nothing has to be re-typed.
From the same case, the system can:
After resolution, the final diagnosis and solution become reusable service knowledge.
The next engineer doesn't start from zero.
The difference
Search folders manually
Ask AI across company knowledge
Ask senior engineers repeatedly
Reuse historical experience
Diagnose similar issues again
Retrieve similar cases
Copy customer information manually
Preserve context automatically
Manually create service cases
Agent creates structured cases
Call people to find availability
Schedule and dispatch through workflow
Resolution disappears into chat
Resolution returns to knowledge
Knowledge transfer
Most service organizations lose knowledge when experienced engineers leave, switch roles or simply forget how an old issue was solved.
DYNARA turns service activity into reusable organizational knowledge.
Your service system should get smarter every time your team solves a problem.
Division of labour
Give engineers context before they enter the case.
The platform underneath
The service solution is built on DYNARA's QSyn platform, connecting knowledge, structured data, workflows and AI agents.
Integration
QSyn can connect with existing systems through APIs, HTTP workflows, data imports and custom integrations.
Adoption path
Import manuals, SOPs and historical cases so the AI can answer technical questions first.
Connect the operational side so the AI can help carry the work out.
Connect your existing systems to complete the AI service operations loop.
Control
The agent operates according to the user's authorized access.
Actions and workflow execution can be recorded and reviewed.
Sensitive or destructive actions can require confirmation.
Company data is separated by workspace and tenant.
Enterprise deployment requirements can be discussed, including private or self-hosted environments.
Who this is for
Machines, production equipment and automation systems.
Service robots, AMRs and intelligent equipment.
Connected devices, controllers and smart hardware.
Complex customer environments involving multiple systems and vendors.
FAE, field service, maintenance and technical support organizations.
We'll help identify where AI can reduce repeated troubleshooting, preserve technical knowledge and automate the service workflow.
Start with one workflow. You don't need to replace your existing systems.
Yes. Existing documents can be used as part of the QSyn knowledge layer and combined with structured service data.
Not necessarily. DYNARA can be introduced as a knowledge and AI layer first, then integrated with existing systems where APIs or suitable integration methods are available.
Yes. Agents can work with QSyn Tables and workflows, and external systems can be connected through APIs or custom integration.
Yes. Access control, tool availability, workflow design and confirmation policies can be used to limit agent actions.
Private or self-hosted deployment requirements can be discussed for enterprise projects.
No. The goal is to reduce repeated information retrieval, troubleshooting and administrative work while giving engineers better context for complex cases.