Automate defect detection and ensure quality before the part moves on
Integrate AI-powered visual inspections directly into your production workflows. Use off-the-shelf cameras to track objects, identify anomalies in real time, and alert operators to issues before they become rework.
When quality relies strictly on a human looking at a part, variability is guaranteed. Operators get tired, and subtle defects slip through. The longer a defect travels down the line before being caught, the more expensive it becomes to fix.
Staring at similar parts for an entire shift causes fatigue, leading operators to accept bad parts and reject good ones. You pay for it in unnecessary scrap, costly rework loops, and customer returns.
Teaching a new hire to accurately spot borderline quality issues is a long process. When experienced operators retire or turn over, that tribal knowledge walks out the door, and defect rates spike while the next group ramps up.
Tulip brings computer vision directly to the station, analyzing parts in real time and alerting the operator if something is wrong. Process engineers can set up visual inspections using inexpensive off-the-shelf cameras, without writing code or relying on a vendor.
Computer Vision
You don't need specialized hardware or a data science team to set up visual verification. Combine human-centered computer vision with no-code apps using inexpensive, off-the-shelf IP cameras. Process engineers train models to track objects, verify orientation, and augment operator workflows directly within the App Editor.
Defect Detection
Embed visual checks directly into the assembly or inspection process. When the camera identifies a potential defect, missing part, or anomaly, it prompts the operator to review the issue and log a nonconformance. Quality issues get contained at the point of work, preventing them from becoming escapes.
Native Integrations
Visual inspection doesn't happen in a vacuum. Connect standard IP cameras for image capture, and link the results directly to your enterprise systems. When an operator confirms a defect based on the visual feed, Tulip updates the ERP work order, adjusts inventory, or triggers a QMS hold automatically.
Edge Execution
Sending high-resolution images to the cloud for analysis introduces lag that slows down production. Run AI models locally at the station using purpose-built edge hardware. Visual processing happens in real time, keeping the line moving and ensuring the operator isn't waiting on a loading screen to make a decision.
Automated Traceability
Every visual inspection creates an immutable digital record. Capture photographic evidence of assemblies, labels, or clearances, tying the images directly to the serial number or batch. This builds a complete device history record (eDHR) or electronic batch record (eBR) automatically, simplifying compliance and root cause analysis.
Customer stories
See how leading manufacturers use Tulip to catch defects early, connect tools to the record, and build an immutable history of every inspection.
Outset Medical embedded guided workflows with built-in error-proofing into their greenfield facility. This eliminated missing signatures completely and cut the time spent on QA review by 93%. Defects dropped by 70% after moving inspections to the point of work.
TICO Tractors started with digital work instructions and saw value within months, reducing errors and taking the load off supervisors. Production doubled over four years as they added tracking, Andons, and quality applications.
"Everything's central. Anybody can look at the defect record. They can look at trends. I think it's going to be one of the biggest impacts we've had with how we're using Tulip."
VEKA caught incorrect orders in real time with ERP-connected checks. Catching issues early at the station contributed to an 88% reduction in mislabeling errors and cut their customer return rate by 60%.
Visual quality inspection software uses computer vision and cameras to automatically analyze parts and assemblies for defects, missing components, or incorrect orientations. Unlike manual inspection, which relies entirely on a human operator's focus, visual inspection software applies consistent, programmed logic to every item, alerting the operator only when an anomaly is detected. In Tulip, these vision capabilities are embedded directly into the operator's guided workflow.
Tulip's computer vision capabilities work with inexpensive, off-the-shelf cameras. You do not need to purchase highly specialized, proprietary vision hardware to run basic defect detection, localization, and classification. The cameras connect to Tulip's edge devices, which process the visual data and feed it back into the app the operator is using.
You don't need a PhD to set up visual inspection with Tulip. Process and quality engineers can combine the latest advances in human-centered computer vision with Tulip's App Editor to build error-proofed workflows. The models are trained by showing the system examples of good and bad parts, without writing custom code or building complex machine learning algorithms from scratch.
Sending high-resolution images to the cloud for machine learning analysis and waiting for a response introduces lag that can slow down a fast-moving production line. Tulip processes visual data at the edge, using purpose-built edge hardware located directly at the station. This means the AI model runs locally, providing real-time alerts to the operator without the latency of cloud round-trips.
When a defect is detected, Tulip can trigger a variety of actions based on how you configure the app. It can flag the part on the operator's screen, highlight the specific xyz coordinates of the issue, prompt the operator for a secondary human verification, or automatically log a nonconformance report. Because the vision system is part of a composable platform, the data instantly updates your quality dashboards and traceability records.
Tulip's vision capabilities are designed to augment operators, not replace them. Visual inspection handles the repetitive, fatiguing task of staring at identical parts, freeing the operator to focus on complex assembly, exception handling, and final judgment. The system flags the anomaly; the human makes the final decision on how to handle the rework.
Every time a camera inspects a part, Tulip captures the result. This creates a real-time dataset of what defects are happening, how often, and on which lines. Quality leaders can use this data to identify the root cause of systemic issues, rather than just reacting to individual defects. Because Tulip classifies and measures the defects, you know exactly what is failing most often and where to focus your improvement efforts.
See how manufacturers integrate computer vision directly into operator workflows to catch defects early and automate traceability.