The work in plain language
Support is not a dashboard.
Your systems change. Your information changes. The AI workflow needs looking after too. We provide agreed support for live AI tools, integrations and connected knowledge, checking not just whether they are running but whether the results are still useful. We investigate faults, monitor information updates and manage changes against a known baseline. You have a team to turn to and clear responsibilities when something breaks. Coverage hours, response expectations and included work are agreed in the support scope; existing systems may need a review before we take them on.
What this can change for your team
- Less uncertainty about who owns a problem after launch
- Earlier visibility when data or answer quality deteriorates
- Changes managed without leaving your team to figure it out alone
01 / 04Managed AI support
Have a team to call when the workflow matters
We agree what is supported, when support is available and who takes responsibility for each part. You know where to turn instead of chasing several suppliers during a problem.
How we make this work
Support starts with a service inventory and a responsibility boundary. Published support starts from USD 2,500 per month for 10 hours, with faults acknowledged within 4 business hours. Any extended coverage, additional hours or priority response commitment must be agreed in a separate written scope. Paloren identifies the workflows, environments, integrations and information sources covered. The scope distinguishes a defect in the implemented system from a third-party outage, an access request or a new feature. Escalation contacts match the actual business need. A system inherited from another supplier may need an initial review before support is accepted. Clear boundaries make useful response possible without unaccountable dependencies.
- Supported workflow and environment inventory
- Defect, access, supplier and feature boundaries
- Agreed coverage hours and escalation contacts
02 / 04Managed AI support
Catch deterioration before it becomes normal
A system can be online and still give poor answers. We monitor the agreed quality, information updates and connection checks so the business can investigate a change early.
How we make this work
An AI workflow can be technically available while producing poor or outdated work. Support monitoring therefore needs more than uptime. Depending on the scope, checks can include source freshness, failed integrations, rejected outputs, review queues and unexpected operating cost. The business owner helps define what deserves investigation and what normal variation looks like. Representative evaluation tasks provide a way to detect changed behaviour after an update. Monitoring should collect only the information required for its purpose, with agreed access and retention. Sensitive content does not need to be copied into every diagnostic record. The objective is clear evidence, not a dashboard that produces alerts nobody owns.
- Freshness, integration and exception checks
- Representative output evaluation
- Alert ownership with proportionate diagnostic records
03 / 04Managed AI support
Keep the business moving when something fails
We plan the response around the people relying on the workflow. Pausing an action, switching to a manual process and communicating the next step are part of the support design.
How we make this work
Support triage begins with the effect on the workflow and the people relying on it. The response may be to pause an automated action, switch to an existing manual process or restrict a source while evidence is examined. We document who can make those decisions and how the business is informed. Investigation separates the reported symptom from a confirmed cause. Relevant execution records and example outputs are preserved under agreed handling rules. Recovery is tested before normal operation resumes. Where an external action cannot be reversed, the response focuses on containment and remediation rather than pretending every incident can be solved by restoring a previous software version.
- Impact-based incident triage
- Pause and manual-fallback procedures
- Recovery checks and business communication
04 / 04Managed AI support
Improve the system without losing control of it
We test maintenance changes and separate new functionality from routine support. Documentation and access stay usable so you retain the ability to run the system and choose who supports it.
How we make this work
AI maintenance includes reviewing source changes, testing approved updates and keeping operating instructions usable. A change to a prompt, model, integration or permission can affect behaviour, so the support plan defines proportionate regression checks and release approval. Routine service reviews examine incidents, repeated exceptions and maintenance debt. They can also identify opportunities for improvement, but a new workflow or expanded action authority should not enter production as an informal support request. The handover and exit plan cover documentation, access ownership and outstanding work. Support should reduce dependence on undocumented knowledge while keeping the business able to choose who operates the system in future.
- Controlled updates with regression evidence
- Service review separating maintenance and new scope
- Documentation and access handover for continuity
What you take forward
What you get
Supported-service inventory
Coverage and responsibility matrix
Monitoring and evaluation plan
Incident and fallback runbook
Controlled maintenance records
Service review and handover documentation
- 01
Review the service
Inventory workflows, dependencies, documentation and current operating risks.
- 02
Agree coverage
Define support boundaries, hours, contacts and incident priorities.
- 03
Operate and maintain
Monitor agreed signals and apply controlled, tested updates.
- 04
Review continuity
Examine recurring issues, new scope and documentation or exit needs.
| Stage | What it changes |
|---|---|
| Review the service | Inventory workflows, dependencies, documentation and current operating risks. |
| Agree coverage | Define support boundaries, hours, contacts and incident priorities. |
| Operate and maintain | Monitor agreed signals and apply controlled, tested updates. |
| Review continuity | Examine recurring issues, new scope and documentation or exit needs. |
The next move
Who notices when the AI workflow quietly starts producing worse answers?
Tell Paloren what is live and who currently looks after it. Reply from the team within one business day. No deck, no technical brief needed.
- First reply
- From the team, within one business day.
- What to bring
- The problem, the systems involved and what should work better.
- What happens next
- Paloren turns it into a working brief. The team reviews it.
Before we begin
Questions we get asked, answered with numbers
Do you provide 24-hour support?
Our published support starts from USD 2,500 per month for 10 hours, with faults acknowledged within 4 business hours. Round-the-clock coverage is not included in that published scope. Tell us when the workflow needs to operate and how a failure would affect the business. We will discuss support hours, response expectations and escalation arrangements before proposing or accepting a support scope.
Can you support a system another supplier built?
Yes, potentially, after reviewing the code or configuration, documentation, access and dependencies within an agreed assessment. Missing ownership or unsupported components may need remediation first. We should not promise dependable support for a system whose behaviour and operating boundaries have not been established.
Are new features included?
Only if the support agreement explicitly includes them. Routine maintenance and a new workflow involve different planning and acceptance work. Requests that expand data access, integrations or action authority should receive a separate scope and approval rather than being treated as an ordinary support ticket.
How do you handle changes in AI output quality?
The support plan can include representative evaluation tasks and scheduled review of rejected outputs. Fault acknowledgement and the time required to investigate or resolve a fault are separate commitments. A change triggers investigation into sources, instructions, integrations or model behaviour. The response may be a tested update, a restricted workflow or a pause, with the business owner involved in the decision.
Who notices when the AI workflow quietly starts producing worse answers?
