Quality monitoring
Tracking of accuracy signals, exception rates, reviewer corrections and user feedback, with alerts when something moves outside agreed limits.
Managed AI Care Plan · Canada
Managed AI care plan: we keep your AI systems accurate, secure and improving after launch
AI systems change after launch even when your code does not: model providers release new versions, your documents and processes evolve, and usage grows. A managed care plan keeps an eye on quality, cost and security every month, fixes what drifts and keeps improving the system as your team uses it.
Why it matters
Why AI systems need care after launch
A model update can quietly change how an agent follows instructions. A new document layout can lower extraction accuracy. A popular feature can push usage costs up. None of this shows up as an outage, so without monitoring and regular evaluation it goes unnoticed until someone stops trusting the system. A care plan makes someone accountable for catching it early.
What you get
- Monitoring and alerting for quality and cost
- Monthly report and review call
- Evaluation re-runs on every change
- Prioritized fixes and improvements
What we deliver
What's included
Evaluation re-runs
Your evaluation set is re-run before and after every model, prompt or tool change, so updates are adopted only when they keep or improve quality.
Model and prompt updates
We test new model versions as providers release them and retire old ones before they are withdrawn, so the system keeps working without surprises.
Fixes and small improvements
Bug fixes, new document layouts, extra fields, prompt refinements and small workflow changes, prioritized with you each month.
Usage and cost reporting
A monthly view of volumes, time saved, model usage and hosting costs, with recommendations to keep running costs in check.
Security and access reviews
Dependency and library updates, permission and API-key reviews, and checks that logs and data retention still match your policies.
Use cases
Systems we look after
- Document intake and extraction pipelines
- AI agents connected to business systems
- RAG search assistants over company documents
- Private LLMs running in your cloud or on-premises
- Customer-facing chat and email assistants
- Reporting and reconciliation automations
Process
How a project runs
- 01
Discover
We map the workflow with the people who run it, find the highest-value automation points and agree measurable success criteria.
- 02
Prototype
A working prototype on your real data in weeks, not months, tested with real users against the agreed criteria.
- 03
Harden
Security review, evaluation suite, monitoring, permissions and integration with your systems, ready for production.
- 04
Run and improve
We hand over with documentation and training, or stay on to operate, measure and extend the system.
Managed AI Care Plan: frequently asked questions
Why do AI systems need looking after once they are live?
Because the parts around them keep changing. Model providers update and retire models, your documents and processes evolve, and new users ask questions nobody tested. Each can lower quality without causing an obvious failure. Regular monitoring and evaluation catch these changes early, while they are still small fixes.
What is included in the managed AI care plan each month?
Monitoring of quality, usage and cost, evaluation re-runs before any model or prompt change, model version updates, bug fixes, small improvements agreed with you, security and access reviews, and a monthly report with a short review call.
How is the care plan priced?
As a monthly plan sized to the system: how many workflows it runs, how much it is used and how much change you expect. We quote it at launch, or as part of the AI readiness assessment if you are planning a new build.
Can we hand the system over to our own team instead?
Yes. Every build ends with documentation, runbooks and training. Some clients take over fully, some keep the care plan, and some start with the plan and move to their own team once they are comfortable.
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