IN Brief:
- Polysense has raised $10.7m to expand AI quality control across food manufacturing lines.
- The platform uses real time computer vision and synthetic data to inspect products as they move through production.
- Inline inspection is moving from quality assurance into process control, yield improvement, and waste reduction.
Polysense has secured $10.7m in seed funding to accelerate the rollout of its AI quality control and process optimisation platform for food manufacturers.
The Belgian food technology company, headquartered in Ghent, will use the investment to widen the platform across additional production stages, support more customer deployments, and expand its engineering, sales, and customer success teams. The round was led by Felix Capital, with participation from Fortino Ventures, Syndicate One, and angel investors.
Rather than relying on periodic sampling, Polysense has built its system around continuous inline inspection. The platform applies computer vision and synthetic data models to products as they move through production, allowing defects and process drift to be identified earlier, before reject levels rise or product quality moves outside specification.
Raw material variation remains one of the defining constraints in industrial food production. Moisture content, size, colour, density, fat distribution, shape, texture, and ingredient behaviour can shift between suppliers, seasons, and batches. Fixed machine settings rarely absorb that variation perfectly, which is why quality drift often shows up through waste, rework, giveaway, or inconsistent finished goods.
Inline vision systems have been used in food factories for years, but the technical ambition is changing. Earlier systems were often configured as inspection gates, identifying non-conforming products and removing them from the line. Polysense is pushing closer to a control layer that can support process adjustment, allowing inspection data to influence production while the line is still running.
That difference is important in high-volume processing. A rejection system protects customers from defective product, but it does not automatically stop the process creating more rejects. A control-led system aims to close that loop, identifying drift early enough for operators or connected systems to correct the cause rather than simply removing the evidence.
Food factories are becoming more software-defined as quality, planning, traceability, and operations move into more connected systems. The same pressure has been visible in meat processing, where Brainr’s funding for factory software showed how production planning, quality, traceability, warehousing, and operational intelligence are being pulled into a single operating layer. Polysense approaches the same direction from the inspection side, where line data can start influencing the physical process directly.
Waste reduction gives the technology a clear commercial route. Manufacturers are under pressure from labour shortages, energy cost, retailer service levels, sustainability commitments, and tighter production margins. Rejecting less product has an immediate financial effect, but the larger gain comes when better process control reduces giveaway, improves yield, cuts rework, and stabilises output without slowing production.
The technical challenge is food’s natural complexity. A potato, pastry, chocolate piece, vegetable, bakery product, or confectionery item can vary legitimately in ways that would look like defects in a more uniform manufacturing environment. Inspection models need to distinguish acceptable variation from process failure without overwhelming operators with false rejects.
Synthetic data can help build that capability by training systems on a broader set of expected and abnormal product appearances. If the system can learn what natural variation looks like, it becomes better able to detect the defects that should trigger intervention. That balance is critical: too little sensitivity allows quality escapes, while too much sensitivity creates unnecessary waste and distrust.
Scaling across food categories will test the company’s engineering depth. Each application brings different belt speeds, lighting conditions, product surfaces, cleaning requirements, contamination risks, and definitions of acceptable quality. Integration with existing controls, operator workflows, and quality systems will determine how quickly the platform moves from inspection support into wider process optimisation.
The funding gives Polysense room to prove that model at scale. AI in food manufacturing will earn adoption where it connects directly to yield, labour efficiency, product consistency, and line performance. In that sense, the strongest factory AI is not abstract; it is the system that sees a problem forming and helps the line recover before the waste bin fills.
