AI workflows break quietly after launch: a model drifts, an upstream API renames a field, an alert lands in an inbox nobody reads. Our managed AI services run what we ship inside your cloud, take over workflows another team built after a technical assessment of their quality, fix them when they break and show you what each workflow saves. We run twelve production systems for Pinkcube alone.
Maciej, our CTO, owns run and improve for every workflow we operate.
CRM API renamed a field. The error handler paused the sync before a bad record was written.
Slack alert to the engineer who built the workflow.
Mapping fixed and tested in staging, then promoted to production.
Held records replayed. Nothing lost.
Clients include
of generative AI pilots show no measurable P&L impact
Source: MIT NANDA, The GenAI Divide: State of AI in Business 2025A 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.
25% of AI initiatives delivered the return CEOs expected. Every workflow we run reports the hours and cost it saves.
16% of AI initiatives have scaled across the enterprise. The team that built your first workflow extends it to the next.
NIST sets out six categories a deployed AI system needs monitoring across, because pre-launch tests miss production behaviour. We watch all six on every workflow.
An API renames a field and bad records start flowing. Our error handler pauses the workflow and holds the records before anything bad is written.
Sources: IBM Institute for Business Value, CEO Study 2025; NIST AI 800-4 (March 2026).
We run the AI systems we build for you, from a customer-facing chatbot to the invoice workflow behind your ERP, and workflows another team built once a technical assessment has checked their quality.
Functionality, operations, human factors, security, compliance and business impact, the categories NIST sets out in AI 800-4. The checks keep running on live data because pre-launch tests cannot show how a model behaves there.
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.
A second agent checks answers against your business rules, and anything it rejects goes to a person. The same pattern runs compliance checks on every draft for IES Limited across four insurance brands.
Fixes, prompt and model updates, and new steps ship weekly through staging, and each sprint reports what the workflow saved.
Nothing changes in production without passing through staging first, and you see the result of every cycle.
The workflow goes live in your cloud with alerts, logging and an error handler from the first run.
Checks run across all six categories, and the second agent reviews output before it ships.
A failure pauses the workflow and holds the records, and the engineer who built it gets the alert.
The change is tested in staging and promoted to production, then the held records are replayed.
You see what changed and what each workflow saved, and choose what the next sprint goes to.
Each one is written up on its case page with the numbers from production. See all case studies
Alerts reach six Teams audiences by noon. IES moves its own thresholds in a Control Centre, and every change is recorded with who made it.
B2B e-commerce, 12 systemsTwo partners carry a workflow from integration into the weekly run, so nothing is lost in a handoff. Meet the team
Into production behind your auth, your governance and your monitoring, with your team trained on it.
Watched, fixed and extended in weekly sprints by the engineers who built it.
Running AI in production takes engineers who know the models, the orchestration layer and your systems at once. NIST AI 800-4 names resource requirements as one of the challenges every monitoring program faces.
| Ovidius managed services | In-house team | |
|---|---|---|
| Who knows the system | The engineers who built it, or assessed it before taking it over | New hires, from documentation |
| When an API changes | Workflow pauses, engineer alerted, fix through staging | Found when someone notices bad data |
| Monitoring | Six categories on every workflow | Built and staffed by you |
| Improvements | Weekly sprints you direct | Compete with every other priority |
| Where data lives | Your cloud, on self-hosted n8n | Your environment |
Monitoring across the six NIST AI 800-4 categories, an error handler and Slack alerting on every workflow, a second-agent review wherever output reaches customers or staff, and weekly sprints for fixes and improvements. You also get a report of what each workflow saved.
In your own cloud. We deploy self-hosted, containerised n8n, so workflows, logs and records stay in your environment. The enterprise context layer scopes what each agent can read, and our AI governance work sets who signs off on what.
The ERPs, CRMs and helpdesks you already run, including Odoo and SAP, so you add AI without rebuilding a database. Our n8n and SAP guide walks through one purchase-order approval flow end to end.
It is the last stage of the same engagement. The AI Audit ranks your use cases and ends in a 90-day 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.
Yes. We start with a technical assessment of the system's quality, then run it in the same weekly loop as the workflows we build.
We scope it on a discovery call, based on how many workflows we run and how much improvement work you want each sprint. You know the cost before anything starts.
Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.
