Product, shelf, checkout and customer-feedback annotation that powers visual search, inventory AI and smarter stores.
Use cases
Training data built around the use cases our clients take to production.
Product relevance
Product relevance enhances the shopping experience by ensuring AI algorithms match user queries with relevant items. Through precise data annotation, Intellisane AI boosts your AI product development to optimize search relevance, reduce product discovery time, and improve sales performance.
Inventory management
We leverage precise product annotation to analyze stock trends and product demand, facilitating timely restocking and minimizing excess inventory. This approach enhances your team's ability to focus on strategic initiatives; and boosts operational efficiency and customer satisfaction.
Surveillance & anti-theft
Through precise data labeling, Computer vision can provide traffic, and behavior analytics using CCTV footage in shops, allowing AI systems to detect suspicious activities. This proactive method reduces theft and ensures a safer shopping experience, empowering retailers to address potential threats and enhance overall security measures swiftly.
Self-checkout
Self-checkout systems enhance retail efficiency but face challenges like item scanning accuracy and theft prevention. Intellisane AI specializes in precise data annotation, ensuring high-quality labeled images for accurate item identification and pricing to improve customer experiences that will generate more sales using fewer resources.
AR/VR product staging
Augmented Reality (AR) and Virtual Reality (VR) staging of products allows customers to visualize items in real-world settings before making a purchase. Our data labeling services accurately annotate products for AR/VR applications, enabling more immersive and interactive customer experiences. This helps retailers enhance product engagement, reduce returns, and boost conversion rates by offering a virtual try-before-you-buy experience.
Annotation types
Pick an annotation type to see what it's for and how it looks on real data.
For retail and e-commerce datasets, accurate labeling is vital for detecting and understanding complex objects like products, shelves, and aisles. Our expertise in polygons, instance, and semantic segmentation ensures precise labeling of complex retail objects, enhancing the accuracy and reliability of AI models for tasks like product recognition and shelf analysis. This meticulous approach contributes directly to the success of our clients' AI models, helping them achieve superior performance and customer insights.
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
Thousands of near-identical SKUs and new packaging every season need fine-grained, versioned classes.
Facings, gaps, and products hidden behind others are labeled consistently for planogram and stock checks.
Price tags and labels are tiny and angled; OCR annotators transcribe them exactly.
In-store footage is handled under strict access rules, with faces blurred where required.
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
“Our fashion AI model required pixel-level segmentation across 72 garment categories, and Intellisane AI handled it flawlessly. They quickly adapted to our complex annotation guidelines and delivered consistent, high-quality labels at scale. Their domain focus, speed, and attention to visual detail were exactly what we needed.”
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.
