Trains production-grade visual inspection models with only hundreds of images

LandingLens uses a data-centric loop that helps teams label, clean, and curate images systematically. Instead of gathering millions of examples, quality engineers can focus on covering edge cases and correcting label noise, enabling reliable defect detection even for low-volume or high-mix production lines.

Business outcome: Achieves factory-floor accuracy with a fraction of the data, shortening time-to-value for new inspection tasks.

Speeds up image annotation with collaborative, QA-centric labelling

The guided labelling workflow enforces label consistency across operators and includes tools for reviewing disagreement. This is critical in manufacturing, where slight differences in how a scratch or dent is marked can undermine model reliability.

Business outcome: Reduces the human annotation effort and improves model precision by eliminating inconsistent ground truth.

Moves from manual spot checks to automated, always-on inspection

Once trained, models can be deployed to edge devices or cloud endpoints to scan parts in real time. Detected defects are logged with confidence scores, allowing teams to set thresholds and automatically divert faulty items.

Business outcome: Lowers escaped defects and costly rework while providing a digital audit trail of every inspection decision.

Extracts structured data from operations forms, invoices, and certificates

Landing AI’s document extraction tools handle semi-structured documents commonly found in supply chain and compliance workflows. The system is designed to work with the same data-centric principles, letting domain experts adjust extraction rules without developer help.

Business outcome: Turns paper-based processes into searchable, analysis-ready data to accelerate order processing and quality certification.

Runs inference directly on factory-floor hardware

Models can be exported to edge gateways or industrial PCs, ensuring low-latency inspection without relying on constant internet connectivity. The platform supports containerised deployments and integration with existing PLC and MES systems.

Business outcome: Maintains real-time inspection speed while keeping data on-premises, meeting strict manufacturing uptime and security requirements.

Provides an enterprise backbone for model management and continuous improvement

Version control, performance monitoring, and role-based access ensure that visual inspection models can be validated, staged, and rolled back by authorised personnel. This governance layer is essential when inspection decisions affect product safety or regulatory compliance.

Business outcome: Enables systematic improvement of AI quality checks while meeting internal audit and change-management policies.

• True data-centric model trainingLandingLens prioritises improving the dataset, not tweaking the model architecture, which directly addresses the biggest barrier in manufacturing AI — lack of high-quality labelled images.
• Domain expertise baked into the workflowThe platform is designed by a team that deeply understands industrial defect detection, from variability in lighting to the importance of consistent label definitions.
• Guided labelling reduces human varianceCollaborative annotation with built-in review steps makes it feasible for quality operators, not just data scientists, to build reliable ground truth.
• Combines vision inspection with document extractionUnlike many vision-only tools, Landing AI serves broader operational digitisation, which aligns well with Industry 4.0 initiatives.

• Significant deployment effortIntegrating Landing AI into a live production line requires camera setup, lighting control, and IT/OT networking; it is not a software-only lift.
• Enterprise pricing with no transparent tiersSmall manufacturers may find it difficult to budget without a clear entry point, and the total cost of ownership must account for change management and training.
• Requires disciplined image captureModel accuracy degrades quickly if production conditions change and new images are not fed back into the data-centric loop; dedicated operators are needed.
• Not designed for consumer or low-tech use casesThe platform assumes an industrial IT environment and skilled users; teams without engineering support will struggle to realise value.

End-of-line quality inspection for automotive parts

A Tier‑1 supplier replaces manual visual checks with LandingLens to detect surface defects on machined components. Using data-centric training, they build a model from 150 labelled images and deploy it on an edge gateway, cutting inspection time by 70% while improving defect catch rate.

Electronics assembly verification

A contract electronics manufacturer uses the platform to identify misplaced or missing components on PCBs. The guided labelling feature allows multiple quality inspectors to reach consistent annotation standards, leading to a model that achieves over 95% accuracy on a high-mix production line.

Pharmaceutical packaging and labelling compliance

Landing AI’s document extraction module digitises batch records and certificate-of-analysis forms, reducing manual data entry errors. Visual inspection models verify label placement and lot codes, helping the company meet 21 CFR Part 11 traceability requirements.

Weld-seam inspection in heavy machinery

A heavy-equipment manufacturer trains LandingLens on weld images that previously required ultrasonic testing. The data-centric approach allows them to incorporate domain feedback iteratively, resulting in a model that reliably flags inconsistent weld profiles before components reach assembly.

Capture a representative set of 100–200 images covering good parts and typical defect types, ensuring consistent lighting and framing.

Onboard your quality team into the LandingLens free trial and upload the images, then use the guided labelling tool to annotate defects as a group.

Run the data-centric training loop — review confusion matrix, identify labelling errors, and add more images for high-error classes.

Export the model from LandingLens to an edge device or cloud endpoint, connect it to a camera feed, and validate performance against manual inspection before deploying across shifts.