Saving USD 10 Million with AI Video Intelligence

About the Client

The client is a global media and entertainment organization with a broad portfolio spanning linear television networks, studios, and direct-to-consumer streaming platforms. Its content reaches audiences across multiple regions and platforms, supporting both subscription-based and ad-supported distribution models.

From a direct-to-consumer perspective, the client operates one of the world’s leading streaming ecosystems. The organization reaches a 100 million + global streaming audience, reflecting a significant and highly engaged digital audience base across mature and emerging markets.

To support evolving business models, increasing content volumes, and a rapidly growing digital audience, the client sought to modernize its content intelligence and metadata management capabilities. The objective was to reduce dependency on manual processes while establishing a scalable, AI-driven foundation capable of supporting large-scale content discovery, monetization, and compliance needs.

Industry Trends

As content libraries expand rapidly across linear, streaming, and ad-supported environments, traditional manual content tagging and discovery approaches are increasingly unsustainable. Many modern enterprises are transforming content operations through automation to:

• Improve productivity and reduce operational costs associated with manual metadata creation.
• Accelerate content discovery and reuse across marketing, programming, and advertising teams.
• Strengthen compliance with industry-standard brand safety and ad suitability frameworks.
• Enable scalable monetization and analytics through rich, structured metadata.
• Create a future-ready foundation for AI-driven innovation across the content lifecycle.

Challenges

The client faced multiple operational and technology challenges in managing video content at scale:

• Manual, labor-intensive content tagging processes that led to high recurring costs and long processing cycles
• Difficulty locating specific scenes or moments within large content archives, resulting in productivity loss
• Inconsistent application of industry-standard taxonomies for brand safety and compliance
• High effort and cost associated with identifying people, cast, and characters at scene level
• Limited ability to fully monetize content due to metadata gaps and low discoverability

These constraints slowed speed-to-market, increased compliance and revenue risk, and limited the organization’s ability to maximize the value of its content assets.

LTM’s Solution

LTM implemented an AI-driven video intelligence solution to automate end-to-end analysis of video assets and generate rich, standardized metadata at scale. The solution leverages multimodal analysis of visual frames, audio cues, and transcript information to extract descriptive and contextual insights directly from the video.

Key elements of the AI video intelligence solution included:

• Multimodal Video Analysis

Automated analysis of video content across visual, audio, and textual dimensions to extract contextual signals and insights.

• Automated Shot and Scene Segmentation

Genre-aware segmentation logic to accurately identify shots, scenes, and clips across different content types.

• AI-Driven Metadata Generation

For each detected scene, the system produces metadata describing the nature, context, and characteristics of the content, enabling improved understanding without relying on predefined industry taxonomies or manual annotation.

• Compliance and Brand Safety Classification

Consistent application of content suitability guidelines to support brand safety, ad suitability, and content governance considerations.

• AI Model Experimentation and Model Evaluation

A core principle of this engagement was an experimentation‑first approach. Multiple solution strategies were designed, implemented, and evaluated before selecting the final approach.

The team experimented extensively with Google Gemini multimodal models, including Gemini Pro, Gemini Flash, and different model versions and prompt strategies.

Three to four distinct approaches were compared based on:

1. Quality and consistency of generated scene‑level metadata
2. Processing latency and overall throughput
3. Cost efficiency at scale
4. Effectiveness of individual and combined multimodal inputs (video, audio, transcripts)

This structured evaluation enabled data‑driven decision‑making and resulted in the selection of a primary solution that provided the best balance between accuracy, scalability, and cost.

• Visual Validation and Comparison Tool

To address the challenge of validating AI results produced as large, structured JSON files, a custom visual validation tool was also designed and implemented.

The custom validation tool transforms raw AI outputs into an interactive visual interface, enabling easier inspection, comparison, and validation of results across different approaches.

Key capabilities include:

• Frame‑by‑frame navigation of video content for scene‑level validation
• Side‑by‑side comparison of outputs from multiple AI approaches
• Faster identification of accuracy gaps, regressions, and improvements

This tooling significantly improved iteration speed and confidence in selecting the final AI approach.

Tech Stack

Python, Multiple models in Google including Google Gemini Flash, Gemini Pro 2.5, Airflow for workflow orchestration

Business Benefits

The AI video intelligence solution delivered significant business impact including:

• ~USD 10 million cost reduction by eliminating manual processing for 16 million shots across 45K hours of video.
• 40 analyst-years of effort compressed into ~16 weeks through parallel automation (equivalent to 20 analysts working ~2 years).
• ~80% reduction in QA effort enabled by a custom validation tool.
• Faster content discovery and reuse across marketing, programming, and advertising teams.
• Improved consistency and scalability of compliance and brand safety classification.
• Enhanced metadata quality supporting monetization, analytics, and personalization.
• The solution was validated at scale across a large volume of video content and demonstrated strong accuracy across key metadata outcomes.
• A future-ready platform enabling ongoing AI experimentation and innovation.

These outcomes improved productivity, reduced risk, and enabled the organization to unlock greater value from its content library.

Conclusion

By implementing an AI-driven video intelligence solution, LTM helped the client modernize content operations and establish a scalable foundation for intelligent content monetization. The transformation reduced operational inefficiencies, strengthened compliance and governance, and significantly enhanced content discoverability. This initiative positioned the organization to confidently scale content operations, accelerate time-to-market, and embed AI-driven capabilities across the media content lifecycle to support long-term growth and competitiveness.

Ready to leverage AI-Powered Content Intelligence?

Contact us at info@ltm.com to learn more.