Detection, tracking, thermal and OCR annotation that helps surveillance systems spot people, vehicles and threats reliably.
Use cases
Training data built around the use cases our clients take to production.
Crowd monitoring
Tagging individuals in crowds to analyze movement patterns and detect suspicious behavior. With Intellisane AI's expertise in data annotation, we help enhance crowd monitoring systems by accurately tagging individuals and tracking movement patterns in real-time. This enables AI-driven surveillance tools to detect anomalies and ensure public safety in high-traffic areas.
Facial recognition
Labeling facial features for accurate identity verification and access control. Intellisane AI utilizes advanced data annotation techniques to enhance AI models, ensuring timely threat detection and robust asset protection.
Intrusion detection
Defining boundaries and tagging objects to detect unauthorized entry into restricted areas. This proactive method reduces theft and ensures a safer shopping experience, empowering retailers to address potential threats and enhance overall security measures swiftly.
Traffic management
Effective traffic management is essential for reducing congestion and enhancing safety on the roads. By leveraging data annotation, we enable AI systems to analyze real-time traffic patterns, optimize signal timings, and improve overall urban mobility.
Drone surveillance
Drone surveillance offers a unique vantage point for monitoring large areas and enhancing security. By utilizing data annotation, we enable AI systems to accurately interpret aerial imagery, improving threat detection and response capabilities in real-time.
Annotation types
Pick an annotation type to see what it's for and how it looks on real data.
Used for object detection in surveillance footage to identify people, vehicles, or objects of interest. For instance, a bounding box can be drawn around a person in a video feed to track their movement across the scene.
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
CCTV frames are often dark and grainy; reviewers zoom, adjust and escalate uncertain objects.
Dense groups are tracked with persistent IDs so counting and re-identification models stay accurate.
Strict access control, NDAs and face blurring where your use case requires it.
Intrusions and incidents are rare, so guidelines define them precisely with examples.
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
“For our ADAS project, Intellisane AI delivered precise vehicle annotations across diverse traffic scenes, including multiple object classes and occlusion scenarios. Their expertise in automotive data workflows and quality-first mindset helped us pass all validation checks, with timely delivery and professional communication throughout.”
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.
