For more than four decades, Lavance has shaped how vehicle wash infrastructure is built, operated, and maintained across France. From car wash gantries and tunnels to heavy vehicle stations serving transport and industrial fleets, the company has long combined technical depth with a nationwide service footprint.
Now, as wash operations become more automated and expectations around uptime, safety, and consistency continue to rise, Lavance is entering its next phase of innovation. The group has announced a new research and innovation partnership with Score (Subnet 44), a Vision AI company, built on Bittensor, focused on turning live video into real-time operational intelligence.
The collaboration reflects a shared goal: moving wash infrastructure from reactive monitoring to intelligent, predictive systems that scale reliably across thousands of sites.
A Natural Evolution of Lavance’s Operational Model
Founded in 1979, Lavance has built its leadership on end-to-end control of the wash ecosystem. Today, the group brings together more than 300 specialists across equipment distribution, installation and maintenance services, and direct station operations under the Superjet brand.
With over 7,000 wash stations installed nationwide and more than 450 operated directly, Lavance manages environments where small disruptions can quickly translate into downtime, safety risks, or inconsistent service quality. As automation increases, so does the need for continuous visibility into what is happening on the ground.
This is where Score’s Vision AI becomes a strategic lever rather than a technical add-on.
Turning Cameras Into Operational Intelligence
Through the partnership, Lavance will integrate Score’s Vision AI models into wash stations across its network. Rather than relying solely on periodic inspections or manual reporting, stations can be monitored continuously through existing camera infrastructure.
Score’s technology transforms these live video streams into structured, actionable intelligence. For Lavance, this enables earlier detection of issues and faster intervention across a wide range of operational scenarios, including:
a. Malfunctioning or unresponsive wash equipment,
b. Blocked lanes or improper vehicle positioning,
c. Safety risks during wash cycles,
d. Irregular behaviour in water-recycling systems, and
e. Mechanical anomalies that typically require manual inspection.
By identifying these events in real-time with Score’s infrastructure, Lavance can reduce downtime, extend equipment lifespan, and improve service reliability for both independent operators and large enterprise clients in retail, petroleum, rental, and transport.
Building Intelligence for Complex Wash Environments
The research focus extends beyond basic monitoring. Car and heavy-vehicle wash stations present unique challenges, from variable lighting and water spray to diverse vehicle sizes and complex mechanical systems.
Together, Lavance and Score are developing Vision AI models tailored to these conditions, covering gantries, tunnels, high-pressure bays, recyclers, and peripheral equipment.
The objective is to create an intelligence layer that works equally well in light-vehicle locations and heavy-duty environments such as truck depots, bus fleets, and industrial facilities.
Over time, this opens the door to:
a. Predictive maintenance for critical components,
b. Automated detection of wear and performance drift.
c. Continuous quality verification across wash cycles,
d. Improved technician workflows and remote diagnostics, and
e. More efficient water and energy usage aligned with sustainability goals.
The result is a wash infrastructure that is not only automated, but genuinely intelligent.
Looking Ahead
The collaboration with Lavance forms part of Score’s growing portfolio of real-world partnerships across diverse industries including agriculture, transport, retail, and infrastructure. Each deployment reflects a shift from experimental AI toward systems that operate continuously in demanding production environments.
Importantly, these projects also highlight how Bittensor is evolving. Rather than serving solely as a research network, it is increasingly underpinning real products used by established organisations. Decentralised intelligence, tested through open competition, is now being translated into operational tools that deliver measurable value.
For Lavance, the partnership reinforces a long-term strategy of combining operational excellence with modern intelligence. For Score, it strengthens a track record of turning Vision AI into practical, deployable systems. Together, the collaboration points toward a future where physical infrastructure is not just automated, but aware, adaptive, and continuously improving.
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