Enterprise AI Managed Services That Keep Production AI Working
Your AI workflows break quietly after launch: a model drifts, an upstream API renames a field, an alert lands in an inbox nobody reads. Ovidius AI runs what we ship inside your stack, so the engineers who built each workflow watch it, fix it and show you what it saves.
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Six monitoring categories on every workflow we run for you.
Why Enterprise AI Stalls After the Pilot
A pilot proves a model can do the work once. Production asks it to do the work every day, against live data, after the people who built it have moved on. Each figure below maps to the part of the managed service that closes the gap.
What Our AI Managed Services Cover
We run the AI systems we build for you, from the customer-facing chatbot to the invoice workflow behind your ERP. The service has four parts, and each one has a named owner on our side.
- Monitoring across six categories Functionality, operations, human factors, security, compliance and business impact, the categories NIST set out in AI 800-4. Pre-launch tests cannot show how a model behaves on live data, so the checks keep running after go-live.
- Error handling that stops bad writes Every workflow carries a global error handler. When an upstream API changes or a model returns something malformed, the workflow pauses, holds the records and alerts the engineer who built it in Slack.
- Review before output reaches people A second agent checks answers against your business rules, and anything it rejects goes to a human. It is the same pattern that runs compliance checks for IES Limited across four insurance brands.
- Weekly improvement sprints Fixes, prompt and model updates, and new steps ship on a weekly cadence through staging, with every result reported in what the workflow saved.
How a Managed Workflow Runs
The same five steps repeat for every workflow we operate. Nothing changes in production without passing through staging first, and you see the result of every cycle.
Systems We Built That Are Still Running
Both of these went live and kept running after launch. The pipelines are on n8n, the same orchestration layer we deploy and maintain for enterprise clients.
In-House AI Operations vs. Ovidius AI Managed Services
Running AI in production takes engineers who know the models, the orchestration layer and your systems at once. NIST names resource requirements as one of five challenges that apply to every monitoring program. Here is how the three common options compare.
Frequently Asked Questions About AI Managed Services
What is included in Ovidius AI managed services?
Monitoring across the six categories in NIST AI 800-4, an error handler and Slack alerting on every workflow, a second-agent review step wherever output reaches customers or staff, and weekly sprints for fixes and improvements. You also get a report of what each workflow saved, the same way every system we ship reports its own results.
Where does our data live while you run the system?
In your own cloud. We deploy self-hosted, containerized n8n as the orchestration layer, so workflows, logs and records stay inside your environment, and compliance scope stays inside your infrastructure. For systems that need governed access to company data, the Enterprise Context Layer scopes what each agent can read, and our AI governance work sets who signs off on what.
Which systems can managed workflows connect to?
The workflows sit on top of the ERPs, CRMs and helpdesks you already run, including Odoo and SAP, so you do not rebuild a database to add AI. Our n8n and SAP Joule Studio guide shows one purchase-order approval flow end to end, and the Enterprise AI Consulting page covers an Odoo scheduling build.
How does this fit with an audit or a new build?
Managed services are the last stage of the same engagement. The AI Audit ranks your use cases and ends in a 90-day enterprise AI roadmap, the build ships the first workflow, and the managed service keeps it running and extends it. You can start at any of the three; each stage has one accountable owner.
How is it priced?
We scope it on a discovery call, based on how many workflows we run and how much improvement work you want each sprint, and you know the cost before anything starts. The enterprise overview sets out the other ways to work with us.
Hand Your Production AI to the Team That Builds It
Tell us which AI workflows you run today, or which one you want to ship first. On a 30-minute discovery call we map what monitoring and support it needs and who on our team owns it.