Why Ixvara
An AI services firm with experience running IT
Ixvara plans, builds, and supports hybrid AI systems for mid-market IT teams. Modernizing an MSP under the same ownership gives us direct experience of supporting the systems we build.
The work behind IT Fabric
At an MSP under the same ownership, technicians had to collect context from multiple tools before they could investigate an issue. We connected those systems and made the evidence reusable.
Read the qualified case study →Roughly $60,000 per year
Reported overlapping operating and tool costs removed in one early deployment.
The MSP is a separate operating company under the same ownership as Ixvara. Results vary by environment, configuration, and scope.
The same incident, two operating models
Illustration
Console by console
Before: the technician rebuilds the picture by hand.
- Alerts arrive without context
- Evidence gathered tool by tool
- Complex work climbs the escalation ladder
One connected operating context
After: the evidence is assembled before work starts.
- Related signals evaluated together
- Triage evidence in minutes
- Consequential action routed through approval
Illustration of the operating model change measured in the MSP case study.
Organizations we have worked with across technology services.
Design for the people who use and support it
Software engineering, AI development, infrastructure, and service operations all affect the result. We consider them together when planning a system.
01
Understand the process
Identify the problem and expected improvement, then compare AI, software, automation, and process changes.
02
Work with existing systems
Design around the applications, identities, data, and infrastructure already in place.
03
Plan ongoing support
Agree testing, approvals, operating costs, and support ownership before release.
A team for planning, delivery, and support
Consulting and engineering
Recommendations include architecture and a delivery plan your engineering team can use.
Products selected for fit
Recommend IT Fabric or IXBox when their features and deployment requirements match the job.
Model and infrastructure choices
Compare conventional automation, hosted models, specialized models, and private deployment against the data and performance requirements.
Support planned before launch
Define permissions, approvals, tests, operating costs, and production responsibility during design.
Discuss your project
Tell us what needs to improve, the systems involved, and your data requirements.