The Infrastructure Behind Self-Driving Clean-In-Place and Changeover Optimization.
One sensor that manages every SKU
A single Laminar sensor captures broadband spectral data across your entire product portfolio — no reconfiguration, no recalibration between runs. That same data feeds multiple ML models simultaneously, so one install point can power CIP optimization, product changeover detection, and quality analysis at the same time.Industrialized and deployed across 130 factories, Laminar sensors operate on fillers, batching systems, tanks, heat exchangers, and pasteurizers. Laminar sensors are currently running on lines ranging from beer and dairy to mayo, nutrition products, and more.
Data from factory floor to ML models
Our edge gateway stores proprietary ML models built specifically for your processes. It enables factory-floor data to be processed by our ML models, optimization decisions to be made in real time, and those decisions to be transmitted directly to PLC/SCADA/BMS systems for immediate implementation.
One sensor that manages every SKU
Laminar ML models are tuned to constantly improve your process and honor all your quality requirements.For Clean-In-Place (CIP) and Changeover optimization use cases, we have proprietary ML models that can be deployed instantly for time, utility, and yield savings — and can add additional models for chemical recirculation optimization and chemical overdose detection.We also support diversified use cases beyond CIP and Changeovers, such as Filtration, Underwash Detection, and Emulsification in F&B manufacturing facilities globally.
Certifications & Compliance
Our sensors are meticulously tested to meet worldwide emissions and safety standards. Our design process prioritizes industrial performance, utilizing industrial Ethernet and IEEE 802.3 PoE standards for robustness and interoperability.
.png)
.png)
.png)
Specifications
View the detailed specifications and engineering drawings for Laminar sensors and Edge Gateway. Learn more about how our Models and Insights Platform work.
Self-Driving Clean-In-Place (CIP) Model
Automatically advances CIP steps when clean is achieved. Eliminates overwash and underwash instances. Reduces time and chemicals spent on CIP cycles while meeting quality standards.
Self-Driving Product Changeover Model
Detects the exact product-to-product or product-water transition interface— reducing product loss at the tail and head of every run. Cuts down on time and water use during changeovers.
Trend Analysis
Visualize and analyze data-driven trends from your CIP and changeover cycles. See the time, water, chemical, and product saving opportunities Insight has identified, and implement recommendations directly.
Inefficiency and Anomaly Detection
Get proactive alerts on anomalies occurring in your line — whether a valve left open or chronic overwashing during a specific changeover sequence — so you can act instantly.
Granular Data on Each Run and Line
Access detailed data on each of your lines and runs to see exactly how much time, water, chemical, or yield loss occurred, and reveal opportunities for savings.
Cross Facility Insights
Compare how your facilities perform across CIP and changeovers, utility usage, and savings to surface comparative insights at scale
There's no technology out there like Laminar.
As a company that's always pushing for innovation, Monin sees this as the kind of AI-driven solution that gives manufacturers a real edge. The fact that Laminar delivers on both sustainability and profitability makes it an easy decision to pilot and scale.
We've been making quality food products for over a century, and one thing that's kept us ahead is a willingness to invest in technology that actually moves the needle. Laminar is a unique application of technology, it lets us optimize our cleaning processes in ways we couldn't before. We're piloting ML models and sensors now and already planning to scale, because the technology checks both boxes that matter most to us: sustainability and profitability.
Winland Foods is committed to exceeding consumer expectations with a high-quality food experience from the field to the table. Innovation is a critical pillar of our mission to provide superior value, and we've partnered with Laminar to enhance production flexibility with insights previously invisible to the naked eye.
Leveraging their machine learning technology, we expect to enhance our production agility, reduce waste, and gain greater capacity so we can continue to deliver more world-class quality and value to our customers.
Our partnership [with Laminar] has allowed for an 18% reduction in downtime during cleaning, which increases productivity and lowers costs in the supply chain. We have also saved 40% of a cleaning cycle's water consumption by using the technology.
Laminar has industrialized exceptional technology that delivers results not found anywhere else in the market. The ability to generate real-time spectral fingerprints for each fluid and determine the liquid signature enhances our efforts to develop the best products and meet market demands.
Their team is a true partner in our shared goal to identify productivity opportunities and use cases that are distinctive to their technology. We're excited to leverage Laminar in creative ways that support our innovation goals, which give us our advantage in the marketplace.
.svg)
At its core, INX is a chemistry company—where precision, performance, and science converge. Laminar’s mastery of chemical intelligence, paired with its ability to transform real‑time chemical data into actionable insight, immediately set them apart. Their technology represents a new frontier in protecting the formulation integrity and product excellence that define who we are.As we push the boundaries of Industry 4.0 and reimagine what’s possible in modern manufacturing, Laminar stands out as a truly differentiated solution. We haven’t seen anything else in the market that delivers this level of chemical insight and control. We’re energized by where Laminar’s technology enables us to go—today and as we shape the future of intelligent, data‑driven chemistry.
