Case Study

Precision Annotation at Frame Level

Baywatch partnered with IndiVillage to turn raw vehicle inspection video into reliable dent detection data with frame-level precision.

BAYWATCH TECHNOLOGIES

Baywatch is redefining automotive inspections through AI-powered video analysis. But smart models demand smarter data. IndiVillage enabled Baywatch to turn raw video into reliable, actionable dent detection, delivering frame-level precision, consistent classification, and annotation accuracy that scales.

100+

video frames annotated

vehicle damage categories standardized

98%

annotation accuracy maintained

Challenge

Data Gaps Held AI Back

Baywatch set out to transform manual vehicle inspections by detecting dents through AI. But real-world video data proved complex, lighting inconsistencies, motion blur, reflective surfaces, and varied angles created noise that models couldn't parse alone. To move from concept to deployment, they needed structured, granular training data that could keep pace with the model's evolving sophistication.

AI algorithms are only as good as the data behind them. Baywatch struggled with inconsistent labeling, lack of standardized categories, and annotation drift, threatening both accuracy and model trustworthiness. What they needed wasn't just annotation at scale, it was annotation done right, consistent, context-aware, and rigorously QA'd to train smarter AI, faster.

Solution

Precision Annotation at Frame Level

IndiVillage assembled a team of expert annotators with deep experience in automotive datasets. Using the Encord platform, we applied frame-level annotation via Bounding Box and Polygon tools to tag damages with pinpoint precision. Each frame was manually reviewed to identify even subtle imperfections, reducing annotation ambiguity and ensuring high-quality inputs for model training.

Every dent, scratch, and scuff was categorized into one of seven standardized damage classes. With a live QA loop embedded into the workflow, annotation accuracy remained at 98% throughout the project. This ensured consistent labeling across thousands of frames, delivering clean, structured data that AI models could learn from without noise or error.

Results

Smarter AI, Faster Claims

The results were immediate and impactful. With IndiVillage's support, Baywatch rapidly scaled its training data pipeline, leading to faster model iterations and dramatically improved detection performance. AI model precision hit 95%+, and time-to-insight on vehicle condition dropped significantly, cutting manual review overhead and boosting platform reliability.

For Baywatch's customers insurers, this translated into quicker claim processing, better user trust, and a competitive edge. IndiVillage's precision-driven data services helped convert a breakthrough vision into an operational AI solution that's ready for the road, driven by quality, built for speed, and scaled with confidence.