Automotive case

Intro

A multinational enterprise that builds tools for ADAS needed expert annotation of road-scene imagery and video to strengthen AI models. The organization aggregates visual and sensor data from diverse geographies and operating conditions, and requires outputs that could be aligned to its internal pipelines and reviewed under strict quality thresholds.

In this case, the company needed highly accurate traffic imagery and video annotation to train and refine AI models for environmental perception. The goal was to ensure that systems could correctly identify lane markings and conditions across different regions, a critical step in building reliable, real-world-ready mobility solutions.

The challenge

Autonomous vehicle systems depend on precisely labeled training data to interpret and respond to real-world conditions. In this project, the work involved managing multiple layers of image and video data, with variations across many factors. The complexity was intensified by several key challenges:

Continuous model
refinement

The project required regular retraining based on new inputs and field test results. This created an ongoing demand for data validation and labeling, not just any annotation, but expert-level precision aligned with evolving requirements.

Critical precision in an
automotive context

In automotive AI, even minor annotation errors can have serious consequences. Mislabeling a traffic sign or failing to notice an animal on the road might mislead a model controlling a self-driving system. Accuracy was vital.

Region-specific
data

The annotation team needed to accurately identify and label region-specific data because the system was deployed in multiple regions, each with unique traffic signage, layouts, and movement patterns.

High volume,
tight timelines

Workflows often required processing large-scale datasets under tight deadlines — up to 200,000 object instances (appearances of objects in individual images) within a 6-7 day turnaround, all while maintaining strict quality standards.

Scalability
bottlenecks

Part of the annotation workflow was initially executed on an open-source tool. However, when scaling was attempted, the system began to lag and limit productivity. A scalable, flexible solution was needed to support large, concurrent teams efficiently.

Results

The project’s annotation workflow was significantly optimized through a structured validation and labeling process built to handle complex automotive datasets.

50+

operators could
work concurrently

increase in
operational capacity

Scalability limitations were fully resolved through migration to Keylabs.ai. Post-migration, over 50 operators were able to work concurrently without performance degradation, resulting in an 8x increase in operational capacity.

Keylabs’ architecture ensured readiness to scale at any point, enabling the customer to respond to changing data volumes and project phases without workflow disruption.

Labeling speed was improved using Keylabs.ai platform features. Tools such as macroshots enabled annotators to instantly locate and preview specific object classes (e.g., traffic signs), reducing instruction-related errors and increasing throughput.

80+

trained
specialists

500k+

data
units

Throughout the engagement, strict deadlines were consistently met, with a team of over 80 trained specialists working across multiple annotation phases. In total, more than 500,000 data units were processed with high precision, supporting the ongoing development of ADAS and traffic detection systems for real-world deployment.

🍪 We use third party cookies to personalize content, ads and analyze site traffic. Learn more

Reviews
on

"Delivering Quality and Excellence"

The upside of working with Keymakr is their strategy to annotations. You are given a sample of work to correct before they begin on the big batches. This saves all parties time and...

"Great service, fair price"

Ability to accommodate different and not consistent workflows.
Ability to scale up as well as scale down.
All the data was in the custom format that...

"Awesome Labeling for ML"

I have worked with Keymakr for about 2 years on several segmentation tasks.
They always provide excellent edge alignment, consistency, and speed...

How a global technology company used Keymakr for traffic detection at scale

Automotive

Intro

The challenge

Results

How a global technology
company used Keymakr
for traffic detection at
scale