Run
Operate and improve your systems and your teams over time.
Your AI systems are in production. But a model in production without supervision is a time bomb. We ensure the operation, monitoring and continuous improvement of your Data and AI applications so that they remain efficient, reliable and compliant over time.
+150 companies supported
— 5/5 Google reviews
Problems
The problems you are having
Your AI systems are in production but no one is monitoring them. Models are drifting, performance is degrading, and your teams don't have the bandwidth to keep what's running.
01
Nobody is watching your models in production.
The model was delivered 18 months ago. Since then, the data has changed, the performance has deteriorated, and no one has detected it. Decisions made based on this model are no longer reliable.
02
Your teams don't have the bandwidth to maintain AI systems.
Your Data Scientists want to work on new projects, not maintain old ones. Your IT teams know how to manage traditional applications, but not ML pipelines and derived models.
03
An incident on a model and it's panic.
The pricing model crashes on a Friday evening. The data pipeline is no longer running. Nobody knows who to call or where to look. There is no procedure, no penalty, no plan B.
04
Your AI systems are stagnant instead of improving.
The model has been doing the same job since it went into production. No retraining, no optimization, no adaptation to new data. The value created at launch is eroding month after month.
Answer
Our approach
We work both in Consulting and in Structured Projects with a commitment to results.
Management and audit of the existing
We audit all of your AI systems in production: model status, pipeline quality, infrastructure, documentation, existing alerts. We identify immediate risks and areas for improvement.
Setting up monitoring
We deploy a comprehensive monitoring system: model drift, incoming data quality, latency, error rates and automatic alerts. You know in real time if your AI systems are working as expected.
Ongoing operation and maintenance
Our team operates your systems on a daily basis: incident management, anomaly correction, model retraining when performance changes and data pipeline updates.
Optimization and continuous improvement
We don't just maintain. Every month, we analyze performance and propose optimizations: improving models, reducing infrastructure costs, adding new features. Your AI systems gain value over time, not the other way around.
Benefits/Impacts
What you gain
AI systems that remain reliable over time, performance that improves instead of stagnating, and Data Scientists freed up to innovate.
01
AI systems that remain reliable over time. Monitoring detects drifts before they impact your operations.
We deploy a comprehensive monitoring system: model drift, incoming data quality, latency and error rates. You know in real time if your systems are working as expected, before your users find out at your expense.
02
Operational peace of mind. Incident management, on-call, escalation: you have a dedicated team that knows how to intervene when something breaks, without mobilizing your internal teams.
A model that crashes on a Friday night, a pipeline that no longer runs: we have the procedures, requirements and SLA levels adapted to the critical nature of each system. Your IT teams are not awake in the middle of the night.
03
Performances that are improving instead of stagnating. Regular retraining of models, continuous optimization and integration of new data so that the value created increases quarter after quarter.
We don't just maintain. Each month, we analyze performance and propose optimizations: improving models, adding new features, adjusting thresholds. Your AI systems gain value over time, not the other way around.
04
Controlled infrastructure costs. We optimize cloud resources and pipelines to prevent your infrastructure bills from ballooning as the volume of data increases.
Rightsizing instances, optimizing requests, cleaning up unused resources: we treat infrastructure as a cost item to manage, not as an invisible expense that drifts silently.
05
Your Data Scientists freed up to innovate. They focus on new use cases instead of spending their time debugging old ones.
Maintaining systems in production is a full-time job that your most qualified profiles should not do. We take care of the operation so that your Data team can focus on what creates the most value: the next projects.
Your questions, our answers
All the answers to understand our approach, how we work and what you can expect from our collaboration.
What is the Run phase at Diametral?
The Run phase operates and improves your AI systems in production over the long term. Diametral takes care of daily operation, monitoring, incident management and continuous improvement, via a monthly or annual contract with contractual SLAs and monthly business performance reporting.
What is model drift and how do you detect it?
Model drift is the gradual degradation of an AI model when production data moves away from training data. Diametral detects drift through continuous monitoring of distributions, performances and user feedback, with automated alerts and a retraining protocol triggered as soon as a critical threshold is crossed.
Why maintain an AI in production?
An unsupervised AI model is a time bomb: its performances degrade silently, its predictions drift and the decisions it feeds become hazardous. Diametral's Run phase guarantees the reliability, compliance and evolution of the system in the face of business, regulatory or technological changes.
What SLAs does Diametral offer in the Run phase?
Diametral offers SLAs adapted to the criticality of each system: detection time, incident resolution time, availability, drift threshold and reporting frequency. Commitments are contractualized in advance and reported monthly via a dashboard shared with your business and IT departments.
How to optimize the costs of AI in production?
FinOps optimization of AI in production involves adjusting compute sizing, streamlining model calls, and automating the stopping of unused resources. Diametral performs an initial FinOps audit and then applies continuous optimizations, with a typical reduction of 20% to 40% in the AI cloud bill over the first few months.
contact
Your AI systems deserve better than autopilot.
Describe your perimeter. A Diametral expert will work with you to assess the appropriate operating system.