Artificial intelligence
Managed artificial intelligence services
The part almost nobody contracts and everybody needs: operating the system after launch.
In summary: ongoing operation of AI solutions in production, with human supervision, quality measurement, incident management and the documentation the European framework requires. The implementation project is the short part; this is the long one.
Most AI projects do not fail during implementation, they fail six months later. The system degrades, nobody measures the degradation, and by the time someone notices the team's trust has already gone.
A managed service exists to prevent exactly that: someone measures quality continuously, reviews edge cases and responds when the system stops behaving as it did on launch day.
What is included
According to the system and use case.
- Human supervision of system outputs in production
- Continuous quality measurement against criteria agreed with you
- Review of edge cases and decisions flagged as uncertain
- Detection and notification of performance degradation
- Maintenance of technical and governance documentation
- Periodic reporting on volume, quality and escalated cases
What is not included
The limits of responsibility.
- Legal liability as an AI system provider, which depends on your role and the system
- A guarantee of accuracy from a probabilistic system's outputs
- Business decisions taken from the system's outputs
- Replacement of the human supervision the rules require in your case
Where it is delivered from
From Poland where the system processes data that must stay inside the European Economic Area, which is the most common case in this service.
From the Latin America corridor where review happens in Spanish and the data type allows it, with the corresponding transfer instruments.
Governance and traceability
The transparency obligations of Article 50 of the AI Act have applied since 2 August 2026: people must know they are interacting with an AI system, and synthetic content must be labelled.
Obligations applying to high-risk systems run on a later timetable, deferred to December 2027 for Annex III cases and August 2028 for Annex I. That is worth knowing because it determines what must be ready now and what can be planned.
In operational practice, what sustains both is the log: what the system decided, on what input, when, and which person reviewed the case where there was review. Without that log, answering a complaint becomes a reconstruction.
This is general information about the rules and not legal advice. We work alongside your advisors.
Frequently asked questions
Why is a managed service needed?
Because an AI system in production degrades and nobody notices until the damage is done. Continuous measurement is what turns a successful pilot into a system still working two years later.
Which models do you work with?
We work with whatever models and platforms your case requires, and we do not recommend specific providers on this site. The choice depends on data type, language, cost per operation and where the information may be processed, and it is reasoned through in the proposal.
Who is liable if the system gets it wrong?
It depends on each party's role and the type of system, and is worth settling in the contract with your advisors. What we do guarantee is the traceability needed to establish what happened.
Can you operate a system you did not build?
Yes, and it is common. The first phase documents the system's actual behaviour and establishes the baseline quality level, which is almost never measured.
Have an AI system in production with no supervision?
Tell us what it does, what volume it processes and how you measure its quality today.