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Intellisane AI

Perception data for autonomous driving

LiDAR, camera and sensor-fusion annotation that helps self-driving and ADAS models see every lane, vehicle and pedestrian.

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  • 3D point cloud
  • Bounding boxes
  • 3D cuboids
  • Semantic segmentation
  • Polygons
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Motorway traffic seen from an overpass
auto-detecting objects…
  • MindEarth
  • Taranto
  • Felucca
  • Virtual Staging
  • Algolight
  • Innodata
  • Stay22
  • Toothlens
  • Agnitu
  • Shaip

Use cases

Where our data makes a difference

Training data built around the use cases our clients take to production.

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    Self-driving vehicles

    3D Multi-sensor, 3D Point Cloud & LiDAR for AV & ADAS.

    3D point cloud annotation allows you to visualize an object for more detailed detection in order to get the dimension exactly correct. Detecting vehicles, vulnerable road users, traffic signs, traffic lights, etc for self-driving cars and smart cities, AI models rely on accurately annotated and labeled data to enhance object detection precision. Intellisane's data expertise helps build robust computer vision systems for self-driving vehicles.

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    In-cabin monitoring

    Driver and Occupant Monitoring

    Data annotations for In-Cabin/Driver or Occupant Monitoring Systems involve precise labeling of facial expressions, eye movements, head orientation, and body posture. These annotations are essential for training AI models to recognize drowsiness, distractions, or risky behaviors, improving driver assistance and enhancing passenger safety in both autonomous and semi-autonomous vehicles. At Intellisane AI, we deliver precise labels for facial expressions and body posture, enhancing AI model accuracy to detect fatigue and distractions, improving safety in autonomous vehicles

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    03

    ALPR & traffic analysis

    Automated License Plate Recognition and Traffic Analysis

    Our Automated License Plate Recognition (ALPR) and Traffic Analysis data annotation services provide precise labeling of vehicle license plates, traffic signs, and road conditions. This enables AI systems to accurately identify license plates, monitor traffic patterns, and improve road safety. Our expertise ensures high-quality data for enhanced vehicle tracking, traffic flow optimization, and effective enforcement of traffic regulations in smart cities and transportation networks.

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    Parking management

    Efficient Parking Management Solutions

    Parking Management data annotation services offer precise labeling of parking spaces, vehicle locations, and entry/exit points, enabling AI systems to monitor availability and optimize operations. With accurate data, parking solutions become more efficient, reducing congestion and improving traffic flow. This service supports smart parking technologies, enhancing user experience and ensuring seamless parking management in urban environments.

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    Custom use-cases

    Tailored Data Annotation for Custom Use-Cases

    Custom Use-cases data annotation services are tailored to meet the unique needs of specialized projects. Whether you're working on advanced AI models for niche industries or handling complex datasets, our expert team provides precise annotations that align with your specific requirements. From healthcare imaging to geospatial data or autonomous systems, we offer flexible solutions to ensure your AI models are trained with the highest accuracy and reliability, empowering innovation across diverse sectors.

Annotation types

The right label for each job

Pick an annotation type to see what it's for and how it looks on real data.

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3D point cloud

3D Point Cloud Annotation for LiDAR Sensing is essential in the AV and ADAS sectors, transforming LiDAR data into detailed three-dimensional representations of the environment. This technique captures precise spatial information about objects, terrain, and obstacles, enabling AI systems to analyze surroundings effectively. By leveraging 3D point clouds, autonomous vehicles gain enhanced perception and situational awareness, improving navigation accuracy and safety in complex driving scenarios.

What makes it hard

The hard parts of autonomous and adas data

These are the problems that quietly break models in this domain, and how our guidelines handle them.

Edge cases we label

  • Emergency vehicles
  • Construction zones
  • Faded lane markings
  • Cyclists between lanes
  • Reflections & glare
  • Parked vs moving

  • Long-tail scenarios

    Rare events like debris, emergency vehicles and unusual road users decide safety, so guidelines cover them explicitly.

  • Occlusion and truncation

    Partly hidden and cut-off objects get consistent extents and attributes instead of being skipped.

  • 2D–3D consistency

    Camera boxes and LiDAR cuboids share IDs so fusion models never see conflicting labels.

  • Night and bad weather

    Low-light, rain and glare frames are reviewed by senior annotators, not left to guesswork.

How we work

Calibrated on your data before we scale

Every project follows the same five steps, with multi-layer QA backing our 98%+ accuracy guarantee.

  1. Step 01

    Consult

    We review your data, edge cases and success criteria, then write annotation guidelines with you.

  2. Step 02

    Annotate

    A trained team labels a pilot batch, following the agreed guidelines to the letter.

  3. Step 03

    Calibrate

    You review the pilot. We fold your feedback into the guidelines before scaling up.

  4. Step 04

    Multi-layer QA

    Every batch passes peer review, expert QA and automated checks before it leaves us.

  5. Step 05

    Deliver

    Labels ship in your format on your schedule, ready to drop into training.

Proof

Trusted by AI teams in 23 countries

Felucca

“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.”

Raphael Lopez
Co-Founder & CTO, Felucca

Case studies

See how other teams used our data in production.

Browse case studies

Get started

Let's talk about your data

Tell us what your model needs to learn. We'll scope the work and label a free pilot batch.

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