About iFit

iFit is a leading at-home fitness company combining premium connected equipment with world-class digital workout content. With a large back catalog and a steady stream of new video production, metadata quality and scalability are critical to iFit’s content distribution strategy.

With new content added weekly and distribution across multiple consumer platforms, video is not just a marketing asset for iFit—it is core product infrastructure.

The Challenge: Scaling Metadata for Platform Distribution

Through a partnership with Samsung Health, iFit began distributing workouts to a significantly larger audience. This expansion required every video to include detailed, standardized metadata aligned with Samsung’s taxonomy.

The metadata requirements went well beyond basic descriptors and included:

• Workout type and category

• Intensity and difficulty levels

• Equipment requirements

• Body parts targeted

• Trainer identity and session attributes

Meeting these requirements introduced a set of operational and technical challenges.

Scaling Metadata Across a Large Back Catalog

iFit needed to apply detailed metadata to hundreds of existing workout videos while continuing to release new content weekly. Manual tagging workflows could not scale to meet both backfill and ongoing production demands.

Maintaining Consistency and Accuracy

Metadata needed to be consistent across videos to meet partner requirements and support downstream discovery. Manual review processes introduced variability across reviewers, increasing the risk of inconsistent tagging.

Aligning with a Fixed External Taxonomy

Samsung Health required strict adherence to its predefined taxonomy. Mapping internal content understanding to an external schema manually was time-consuming and error-prone, especially as definitions evolved.

Reducing Time to Distribution

Content needed to be tagged and platform-ready quickly to avoid delays in distribution. Existing workflows introduced long turnaround times that slowed go-to-market velocity.

Preparing for Ongoing Scale

Beyond the initial integration, iFit needed a system that could scale with future partnerships, new content formats, and evolving metadata requirements—without increasing operational overhead.

The Solution: AI-Powered Automatic Metadata Extraction

To address the scale, accuracy, and compliance challenges of metadata tagging, iFit implemented Flowstate AI’s video understanding platform to automate metadata extraction across its content library.

Flowstate analyzes video content at the frame level, combining visual, audio, and temporal signals to generate structured metadata aligned with predefined schemas. This approach allowed iFit to replace manual editorial workflows with an automated system capable of operating consistently across both legacy content and new productions.

Custom Schema Alignment

Flowstate was configured to generate metadata directly aligned with Samsung Health’s taxonomy. Rather than producing generic tags, the system applied iFit-specific schema definitions, ensuring that outputs met external platform requirements without additional mapping or transformation.

As taxonomy definitions evolved, iFit could update schema configurations and reprocess existing videos without manual relabeling—maintaining compliance while reducing operational effort.

Parallel Processing at Scale

Flowstate processed iFit’s video library using parallel analysis, enabling hundreds of videos to be analyzed simultaneously. This architecture reduced total processing time from months to days and ensured consistent metadata generation across the entire library.

Parallel processing also allowed metadata extraction to scale with ongoing content production, eliminating backlogs and enabling continuous readiness for distribution.

Human-in-the-Loop Quality Assurance

To ensure production-grade accuracy, Flowstate surfaced AI-generated metadata in a review interface designed for rapid human validation. Reviewers could quickly approve, adjust, or flag tags as needed, combining the speed of automation with editorial oversight.

This human-in-the-loop approach built trust in AI outputs while minimizing manual intervention.

Integration with Existing Workflows

Flowstate integrated into iFit’s existing content operations, allowing metadata to flow directly into downstream systems without disrupting established processes. The platform supported both batch processing for archival content and continuous tagging for newly produced videos.

The Result

Before vs. After: iFit’s Metadata Workflow

Before Flowstate, metadata tagging was a manual, time-intensive process that struggled to keep pace with iFit’s growing library and distribution requirements.

After implementing Flowstate, metadata extraction became automated, consistent, and scalable—enabling iFit to process large volumes of video quickly while maintaining platform-compliant accuracy.

The shift from manual workflows to AI-driven extraction reduced operational overhead, eliminated bottlenecks, and ensured new content could be distributed without delay.

Measurable Results

By deploying Flowstate AI across its video library, iFit achieved measurable operational improvements:

• 95%+ tagging accuracy, meeting external platform quality requirements

• ~90% reduction in processing time, cutting turnaround from months to days

• 1,000+ videos processed during the initial deployment

• A scalable workflow capable of supporting ongoing weekly content production

These results enabled iFit to meet partner deadlines confidently while establishing a repeatable, future-proof approach to video metadata management.

Hear from Our Customer

“Flowstate allowed us to move from a manual, time-consuming tagging process to an automated system we could trust. We were able to meet Samsung’s metadata requirements at scale while dramatically reducing turnaround time—and without increasing operational overhead.”
— Content Operations Lead, iFit

Expanding the Use Case

Automated Workout Program Creation

With structured metadata in place, iFit began using video understanding to automatically assemble workout programs. By analyzing progression, difficulty, and complementary movements, Flowstate enabled more efficient program design and personalization.

Library-Wide Intelligence

iFit expanded tagging across its full library of 5,000+ videos, improving internal discovery, content reuse, and cross-platform personalization. What began as a compliance solution evolved into a foundational layer for content intelligence.

Why AI Video Understanding Matters for Enterprises

Video is one of the fastest-growing and least-structured sources of enterprise data. From media libraries and training content to security footage and customer interactions, organizations generate and store massive volumes of video—but much of its value remains inaccessible.

AI video understanding allows enterprises to convert video from passive media into structured, searchable, and actionable data. By extracting meaning across visuals, audio, and time, organizations can scale operations, meet compliance requirements, improve discoverability, and integrate video intelligence directly into existing systems.

This shift enables teams to operate more efficiently, respond faster, and unlock new value from content that was previously costly or impractical to analyze at scale.

How Flowstate Can Help

Flowstate helps organizations transform large video libraries into structured, usable systems of intelligence.

Whether you’re preparing content for distribution partners, modernizing legacy archives, or enabling real-time video understanding, Flowstate provides:

• AI-driven video understanding across visuals, audio, and time

• Custom schema support aligned to your business requirements

• Parallel processing for rapid library-wide reprocessing

• Human-in-the-loop workflows for quality assurance

• APIs and SDKs that integrate with existing media and cloud systems

Flowstate enables teams to scale video operations without scaling manual effort.

Bring your video archive to life. Book a demo.