Managed AI for systems that have to keep working after launch
We monitor and maintain AI agents, assistants, and automations in production. That means watching accuracy and costs, handling failures, applying model and platform updates, and reporting each month on what changed.
The service covers systems we built and systems built by others, after a review of how they are set up.
Based in Lahore, we work with businesses across Pakistan and internationally.
Treat a live AI system like any other production system
An AI system changes after launch even when nobody touches it: the data drifts, the model provider updates, usage grows, and costs move with it. Left alone, it degrades in ways nobody notices until a customer does. Managed AI is the routine of checking, so that changes are seen and handled.
Each system gets a review first, so we know how it is built, what it costs to run, and what a failure looks like. From that we agree what is monitored, who is alerted, and what the monthly report covers.
Quick answers
What is monitored?
Accuracy on sample cases, response times, failures and retries, usage and cost, and the behavior of any handoffs to people. The list is agreed per system.
Can you manage a system you did not build?
Yes, after a review of how it is set up. The review may recommend changes before the service starts.
What is in the monthly report?
What was monitored, what changed, what failed and how it was handled, what it cost, and what we recommend next.
What managed AI can include
System review
Document how each system is built, what it costs, and what a failure looks like.
Monitoring and alerts
Checks on accuracy, failures, response time, and cost, with alerts to the agreed people.
Failure handling
Investigate and fix failures within the agreed response time, and record the cause.
Updates
Test model and platform updates before they reach production, and roll back if needed.
Monthly report
A written account of what changed, what it cost, and what to do next.
Monitoring built around the systems you run
Monitoring uses the logs and metrics the system already produces where possible, with additional checks added only where they are needed.
- Logs and metrics: Collected from the system, the model provider, and the platform
- Accuracy checks: Sample cases run on a schedule and compared with expected results
- Cost tracking: Model and platform usage tracked against the agreed budget
- Alerts: Sent to the agreed people by email or chat within the agreed times
Monitoring
Evaluation
Infra
Compare two scenarios for time your team spends keeping an AI system running
Enter your own figures to compare the hours your team spends checking and fixing an AI system now with a second scenario you choose. This shows the arithmetic difference between the two; it does not forecast what a managed service will achieve.
These figures compare only the assumptions you enter. They do not include the cost of the service, and they do not predict time savings.
Review the system
Document how it is built, what it costs, and what failure looks like.
Agree the service
Set what is monitored, the response times, and who is alerted.
Set up monitoring
Connect logs and metrics and add the accuracy and cost checks.
Run the service
Watch, handle failures, and test updates before release.
Report monthly
Deliver the written report and agree any changes.
Who managed AI is for
Businesses with a live assistant or agent
Customer-facing systems where a silent failure costs inquiries.
Teams without an in-house engineer
Somewhere to send the failure, and someone to test the update.
Systems built by another provider
A review first, then the same monitoring and reporting.
Growing usage
Costs tracked as volume grows, with changes recommended before the bill surprises anyone.
See the change before the customer does
Most AI failures in production are quiet: an answer that drifts, a cost that creeps, an integration that stops after an update. Regular checks and a written monthly report make those changes visible while they are small.
- Checks on accuracy, failures, and cost
- Alerts within agreed times
- Updates tested before release
- A written report each month
Managed AI pricing
Our pricing page does not list a separate starting price for managed AI. The service is quoted per system after the review, based on the checks, the response times, and the reporting agreed. Model and platform usage charges are separate.
Managed AI: your questions
What response time do you offer?
Do you handle model provider changes?
Can you reduce our running costs?
What happens if the system fails outside working hours?
How much does managed AI cost?
Do you work with businesses outside Lahore?
Tell us about the system
Describe the AI system you run, who built it, and what worries you about it. We will tell you what a review would cover and what a service could include.