MEDICALCITY — The AI Factory for Healthcare
Healthcare is our starting point. MEDICALCITY’s AI Factory develops the digital multi-agent foundation and robotic interfaces to support teams, with a vision to explore across health, food, housing and mobility. Pilot target in France: January 2027, with timing and scope still to be confirmed.
For healthcare teams
Care staff · Medical secretaries · Administrative teams · Healthcare facilities · Physicians
Food, housing and mobility uses still need to be defined, developed and tested with teams, according to the site and hardware. These ideas concern the working or living environment, with no care procedure or physical contact with a person.
Explore moving small equipment or supplies for healthcare teams to a defined point within a checked environment.
Explore moving meal containers or related supplies, with loads and handling conditions to validate.
Assess tidying or cleaning in defined areas of a living space, with objects, surfaces and limits to specify.
Consider moving objects between points on the same site, with a route to check and no transport of people.
From digital work to physical execution
The robotics programme is the AI Factory’s physical execution track. The digital foundation prepares the request and follow-up; robotic interfaces must pass on only an authorised task and return its outcome. This chain still needs validation in each pilot.
Connect specialised agents to AI models, reference knowledge, memory and business software. Workflows organise document preparation, administrative processes and follow-up, with access and approvals defined for each project.
Research direction: “Multi-Agent AGI”, towards artificial general intelligence. A development ambition; the systems presented rely on specialised agents.
Prepare robot connections, operator supervision and execution feedback. Each pilot must confirm hardware capabilities, permitted tasks, site conditions and ways to stop or take over the work.
Robotics: pilots in preparation. Drones: a longer-term development prospect. Scope, timing and service conditions remain to be confirmed for each project.
An AI draft never triggers a physical action. If information is missing or a condition is not met, the task waits for clarification and a human decision.
The AI Factory designs interfaces between professionals, the digital agent workforce and supervised physical execution. Teams define the need; connections, permissions and feedback must be checked for each project.
Care staff, physicians, medical secretaries and administrative teams specify the need and the information that may be used. Medical decisions remain with authorised professionals.
Drafting, administration and coordination roles work together to prepare a task sheet, identify missing information and suggest follow-up. Every result needs review.
The operator checks feasibility, permissions and operating conditions before approving the task. Human takeover and incident handling must be defined before any trial.
Proposed scenario · to validate in a trial
Example: moving supplies to a receiving point at a pilot site. This sequence describes a way of working to test.
The professional identifies the objects, location, recipient and constraints. The administrative agent prepares a sheet highlighting details to complete.
They check the route, load, equipment and stopping arrangements. The robot receives only the approved task within the trial’s scope.
In this pilot scenario, the operator supervises the authorised task and can stop it or take over. Events and task status feed into an outcome report that needs checking.
The recipient checks receipt. The drafting agent can suggest a report; the responsible person validates the facts and any next steps.
AI drafts can contain errors. The responsible person checks and corrects the information before anything is sent or carried out. Missing information or unmet conditions call for clarification and a human alternative.
Each trial needs an agreed scope, named responsibilities and success criteria defined with the team involved. In particular, name the person responsible for approving the final report.
Check the space, movement, objects handled and the robot’s limits. These examples promise no ability to provide physical assistance to a person.
Define trained personnel, information for the people concerned, required consent, stopping and human takeover. The robot complements the team’s work.
Use only necessary data and define access and records to keep. Evaluate reliability, incidents and total preparation and supervision time compared with usual practice.
Robot-as-a-Service (RaaS), a robot provided as part of a service, is a model to explore with teams and operators. Hardware, maintenance, responsibilities, costs and funding must be agreed case by case; no public funding is guaranteed here.
Share a task related to health, food, housing or mobility. The AI Factory can explore its digital preparation, robotic interfaces and human approvals to define what still needs to be developed and tested with your team.
A free expression of interest, with no rental commitment. Programme dates, prices, funding and any future service terms remain to be confirmed separately. The form and its terms are in French.
