Precise annotation for inspection, picking, navigation and warehouse automation, so your models run safely and efficiently on the factory floor.
Use cases
Training data built around the use cases our clients take to production.
Industrial automation
Industrial Automation Data Labeling by Intellisane AI involves precise annotation of sensor and visual data to train AI models for automating manufacturing processes. Intellisane AI ensures accuracy in quality inspection, workflow optimization, and predictive maintenance, enabling smarter and more autonomous industrial operations tailored to your needs.
Inspection & quality control
Inspection and quality control data labeling plays a vital role in ensuring consistent and high-quality manufacturing output. By annotating images and sensor data, Intellisane AI helps train AI systems to detect defects, identify anomalies, and automate quality checks. Our expertise ensures the production line meets rigorous standards, reducing errors and improving overall efficiency.
Predictive maintenance
Predictive maintenance is crucial for minimizing downtime and optimizing operational efficiency in manufacturing. By annotating sensor data and operational metrics, Intellisane AI enables AI systems to forecast equipment failures and schedule timely maintenance. This proactive approach reduces unexpected breakdowns, enhances productivity, and extends the lifespan of machinery, ensuring smooth manufacturing processes.
Perception & navigation
Robotic perception and navigation are essential for enabling autonomous robots to understand and interact with their environment. By annotating sensor data and visual inputs, Intellisane AI enhances the capabilities of AI systems in recognizing obstacles, mapping surroundings, and making real-time decisions. This improves the efficiency and safety of robots in various applications, from manufacturing to logistics, ensuring they navigate complex environments seamlessly.
Warehouse inventory
Warehouse inventory management is crucial for optimizing storage efficiency and streamlining supply chain operations. Intellisane AI enhances inventory tracking by annotating visual data and sensor inputs, allowing AI systems to monitor stock levels, identify discrepancies, and predict inventory needs in real-time. This approach improves accuracy and reduces waste, ensuring that warehouses operate smoothly and respond effectively to demand fluctuations.
Annotation types
Pick an annotation type to see what it's for and how it looks on real data.
Forklifts, pallets, people and obstacles are labeled in camera feeds from robots and AMRs, so they can detect what is around them and move safely through busy warehouses and factory floors.
What makes it hard
These are the problems that quietly break models in this domain, and how our guidelines handle them.
Edge cases we label
Scratches, dents and misalignments are tiny and rare; annotators work from defect catalogs and reference images.
Metal and plastic surfaces confuse both people and models, so we label with zoom and multiple views.
People near machines are labeled with poses and proximity zones for safety systems.
Taxonomies evolve as SKUs change; guidelines are versioned and re-calibrated with you.
How we work
Every project follows the same five steps, with multi-layer QA backing our 98%+ accuracy guarantee.
We review your data, edge cases and success criteria, then write annotation guidelines with you.
A trained team labels a pilot batch, following the agreed guidelines to the letter.
You review the pilot. We fold your feedback into the guidelines before scaling up.
Every batch passes peer review, expert QA and automated checks before it leaves us.
Labels ship in your format on your schedule, ready to drop into training.
Proof
“Intellisane AI played a key role in helping us reach 97% accuracy in automating foundation layout detection. Their deep understanding of spatial data and labeling precision brought measurable improvements to our AI pipeline. The team was communicative, detail-oriented, and delivered everything ahead of schedule.”
Case studies
See how other teams used our data in production.
Get started
Tell us what your model needs to learn. We'll scope the work and label a free pilot batch.
