Ongoing Managed AI Support
After launch, models need patching, upgrading, re-evaluating and watching. Our managed plans keep your AI infrastructure healthy, current and cost-efficient, with engineers who already know your environment.
Why teams bring us in
New open models ship every few weeks, GPU drivers need patching, and usage patterns shift. Without someone responsible, a well-built system slowly drifts into an expensive, outdated one.
Managed support covers the day-to-day work that keeps an AI platform healthy: watching dashboards, responding to alerts, patching drivers and serving software, and keeping an eye on cost. It also covers the part that is easy to neglect: evaluating new model releases against yours and upgrading when they are better.
- Monitoring: uptime, latency, error rates and cost with alerting to your channels
- Incident response: defined response times and a named engineer
- Model lifecycle: evaluating new releases and upgrading when they win
- Cost reviews: monthly spend review with savings actions
- Security patching: drivers, CUDA, serving stack and base images kept current
Teams that get the most from this service
Teams without a platform group
You have AI in production and nobody whose job it is to look after it.
Clients after an Imgori build
The natural next step once your deployment is live and handed over.
Lean platform teams
Your engineers want to build features, not babysit GPU drivers at 2 a.m.
Four phases, each with a sign-off.
Every client has a named engineer who knows the environment, backed by the wider team for incidents and specialist work. Each month you receive a plain-language report covering availability, incidents, spend, and the changes we made or recommend.
| Phase | What happens | You receive |
|---|---|---|
| Onboard | Access, runbooks, alert routing | Support playbook |
| Operate | Monitoring and incident handling | Monthly ops report |
| Improve | Model upgrades, tuning, cost work | Quarterly roadmap |
| Review | Business review with stakeholders | QBR deck |
What you keep when we're done.
Monthly ops report
Uptime, incidents, cost trends and actions taken, in plain language.
Upgrade program
New models evaluated against yours and rolled out when they're better.
Named engineer
One person who knows your stack, backed by the rest of the team.
Tools we use
Chosen per client. We work with what your team already runs wherever it makes sense.
Questions about Ongoing Managed AI Support
What response times do you offer?
Standard plans cover business hours in your time zone. Enterprise plans add 24/7 monitoring with a Sev-1 response target under one hour.
Can you support a stack you didn't build?
Yes, after a short onboarding review. We document the environment and fix anything that blocks reliable support.
Is there a minimum term?
Plans are typically quarterly, so you can adjust scope as your usage grows.
Often paired with
Private & On-Prem LLM Deployment
Run open-weight models on your own servers or private cloud, with no data leaving your network.
Explore service →SVC-02GPU Infrastructure Optimization
Cut inference and training spend with better scheduling, quantization and right-sized clusters.
Explore service →SVC-03Cloud-to-AI Migration
Move legacy workloads and data platforms onto infrastructure that is ready for AI.
Explore service →Talk to an engineer about Ongoing Managed AI Support.
A 45-minute call with an engineer, not a sales script. You'll leave with a rough architecture, a cost range and the next three steps.