Toptal improves real-world AI recognition for Fortune 500 telecom provider.
A global telecommunications leader turned to Toptal to overcome limitations in real-world object detection and automate labeling workflows with advanced AI engineering.
Client
A Fortune Global 500 telecom giant with a multi-billion-dollar market presence and leadership in network infrastructure.
Employees
Revenue
Industry
CommunicationsDelivered Services
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Schedule a CallChallenge
The client’s existing models failed to detect small or rotated objects in real-world conditions and lacked efficient labeling workflows, prompting a full-scale AI overhaul to regain competitive edge.
Solution
Precision Regression Modeling
Toptal developed a custom regression-based object detection model capable of accurately identifying small, rotated, or low-resolution objects in real-world conditions, outperforming prior lab-based systems and prompting the client to explore a patent.
Integrated Labeling Framework
Toptal engineered a unified pipeline that combined PyTorch and TensorFlow models, incorporated a custom evaluation function, and leveraged synthetic data generation to streamline and scale the labeling process.
Outcome
Client-ready Detection Accuracy
The enhanced model significantly improved accuracy across variable environments and object orientations, enabling API-ready deployment and establishing the client’s leadership in next-generation visual recognition services.
Automated Detector Creation
With the new pipeline, the client can now fully automate the training and deployment of new detectors, saving manual effort, accelerating development cycles, and improving consistency across vision models.
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