Ongoing AI Quality Monitoring
Establish recurring evaluations and review routines for the quality of your live AI system.
The opportunity
A good launch evaluation does not last forever. Source information changes, new questions appear and updates alter the behaviour of the system. Without recurring checks, quality problems can accumulate unnoticed.
Voxd helps establish an ongoing evaluation routine for your agent or assistant. We agree representative tasks and quality criteria, review selected evidence and track findings over time. The result is a practical feedback loop that supports better decisions about maintenance and improvement.
Compare performance over time using agreed tasks and criteria rather than relying only on occasional complaints.
Identify patterns in unsupported answers, missing knowledge or poor escalation that need attention.
What we deliver
Develop or review the evaluation set and expectations for acceptable behaviour.
Run agreed evaluations and review samples appropriate to the system and its use.
Report trends, examples and recommended actions for the responsible team.
Why Voxd
A problem may sit in an instruction, a knowledge source, an API or the application around the model. Voxd’s work spans these layers, from connected customer agents to bespoke software and operational workflows.
We can investigate the whole journey and implement improvements within the agreed scope. You get continuity between understanding the business purpose and maintaining the technology, with clear expectations about what is covered and what needs a separate project.
How we work
We assess the system, dependencies, access and existing documentation. We identify any work required before ongoing support can begin.
We define coverage, priorities, response expectations and the split between maintenance, incidents and new development.
We set up appropriate monitoring and quality checks, with a baseline that makes changes in performance easier to investigate.
We handle agreed support work and review the evidence with you. Tested changes and a prioritised backlog keep improvement deliberate.
Before you get started
Not necessarily. Coverage can combine test cases and selected samples, with the scope and limitations agreed for your system.
Yes. Comparing proposed changes against the baseline is a useful part of a controlled update process.
Tell us which answers or tasks your users rely on most.
Connect the maintenance backlog to observed issues and the importance of the affected tasks.