AI in production and operations
Move from the proof of concept to a service your teams use every day, with monitoring of answer quality, costs and incidents.
From prototype to service · quality, costs, incidents · Québec by default
- Format
- Go-live, then ongoing operations
- Who is involved
- Your IT team and the service owners in the business lines
- What you leave with
- An AI service in daily use, with indicators for quality, costs and incidents
- What comes next
- Evolving the service with the AI dev team, or a new use case
Who needs AI in production
Production is for organizations whose proof of concept showed the idea works, but that cannot yet rely on it day to day.
- A conclusive proof of concept remained a prototype, with no controlled access and no operations behind it.
- Users are asking for the tool every day, and IT hesitates to support it without safeguards.
- Model usage costs are a worry, and nobody knows yet what widespread use would cost.
- You need to track incidents and answer quality, but no indicator exists at the moment.
From prototype to service, in four steps
- Taking over the proof of concept: what holds up is kept, the rest is hardened for daily use.
- Go-live: integration with your tools and access controls, on the hosting you chose.
- Monitoring: answer quality, usage costs and incidents, measured continuously.
- Operations: incident handling, model updates and regular review of the indicators.
Hosted in Québec by default, in France if your activity requires it.
See the sovereign models →How an AI service is operated day to day
The takeover starts with sorting: what holds up in the proof of concept is kept, and the rest is hardened to withstand daily use. The service is then connected to your tools and your access controls, on the hosting you chose, with Québec as the default. A prototype tolerates outages and uneven answers; a service that teams use every day does not, and that is where most of the hardening effort goes.
Operations rest on three measures tracked continuously: answer quality, model usage cost and incidents. They tell you when to fix, when to adjust a setting and when to update a model, instead of reacting to user complaints. A regular review of these indicators with your service owners keeps a record of how the service evolves and makes costs visible before they surprise anyone.
What you get
An AI service in daily use
The prototype becomes a service connected to your tools and access controls, with the safeguards of a tool used every day, hosted in Québec by default.
Quality, cost and incident indicators
Continuous tracking of how accurate the answers are, what model usage costs and which incidents are open, shown to your service owners at each regular review.
Ongoing operations
Incidents are handled through to resolution, models are updated and indicators are reviewed regularly, so the service keeps the quality you expect.
After putting AI into production, evolving the service
A service in use attracts requests: a feature to add, a source to connect, a new group of users. The AI dev team can take on the development, under human validation, while operations keep tracking the indicators. For a different need, you start from a proof of concept on the new case rather than stretching the existing service blindly.
Frequently asked questions
What is the difference between a proof of concept and a service in production?
The proof of concept shows the idea works on one case. A service in production holds up every day, with controlled access and monitoring of answer quality, costs and incidents.
What do you monitor once the service is running?
Three things: answer quality (are the answers accurate and in line with your rules), model usage costs and incidents. Those indicators tell you when to fix, adjust or update.
Where is the service hosted?
In Québec by default. Sovereignty is an option based on your needs: in France if your activity requires it, or on your own infrastructure. The sovereign models page details the options.
What happens when an incident hits a running AI service?
It is logged, followed through to resolution and compared with the quality and cost indicators, which shows whether it reveals an isolated defect or a drift in the service. Recovery and availability terms are defined with you when the service goes live.