How Bobidi and MLtwist Delivered Faster AI Validation and Higher Quality at Lower Cost

Introduction

Scaling AI validation demands speed, accuracy, and efficient data processing. Bobidi, an AI validation platform founded by Google and Meta veterans, partnered with MLtwist to meet aggressive validation goals on a complex, high-volume audio project.

By automating the data pipeline, Bobidi accelerated deployment by 90%, cut costs by 50%, and exceeded quality targets—freeing resources to focus on model improvement instead of manual processing.

THE CHALLENGE

Validating Large Amounts of Data on an Aggressive Deadline

Bobidi, an AI validation platform founded by Google and Meta veterans, set out to validate 5,000 audio files totaling 10GB of data. The project required reviewing 600,000 existing audio labels and creating 50,000 new ones—all within a one-month timeframe.

To meet these goals, Bobidi needed to:

• Manage a 9-stage processing pipeline involving 100,000 file transformations.
• Maintain high data quality standards while handling large volumes of data.
• Control operational costs while accelerating delivery to customers.

Building and maintaining a custom data pipeline from scratch would have consumed critical time and resources, putting the timeline and budget at risk.

MLtwist’s Approach

Bobidi partnered with MLtwist to automate their AI data pipeline using an out-of-the-box, scalable solution. Key capabilities included:

• Automated Data Preprocessing and Transformation: MLtwist handled complex file transformations at scale across a multi-stage pipeline.
• Rapid Integration with AI Tools: MLtwist’s pipelines pushed processed data directly into Bobidi’s AI validation workflows.
• Built-In Quality Control: Proprietary QC processes ensured that data met or exceeded accuracy requirements even at high volumes.

Impact & Benefits

• 90% Faster Deployment: Automation slashed deployment time, dramatically accelerating Bobidi’s validation workflows.
• 50% Cost Savings: Bobidi reduced its data processing spend by half, freeing up $25K per month—equivalent to $300K annually—for other strategic initiatives.
• Improved Data Quality: Final datasets achieved a 98% accuracy rate, exceeding the project’s 95% quality target.

Conclusion

By automating complex data processing with MLtwist, Bobidi not only met an aggressive timeline but also raised the bar on data quality while cutting operational costs. With scalable, out-of-the-box pipelines powering their AI validation platform, Bobidi can now reinvest more resources into advancing model performance—keeping them at the forefront of AI testing and validation.