Fabric AI is at the forefront of integrating AI into the media supply chain, enabling practical real-world results that deliver value and efficiency on a level never before possible.
Localization
Automated Localization is one of the most mature applications of AI/LLMs and machine learning, offering huge savings compared to human translation, with integrated QC processes and governance controls.
Transformation
From cleaning up text, removing cast and crew from synopses, and createing a consistent brand voice, to synopsis cut-downs to specific character lengths, and syntax and formatting validation, Fabric does it all.
Generation
Synopses can be generated by AI directly from scripts, production notes, and other production materials via a simple ‘drop-box’ style application, and delivered directly into Fabric Studio for validation by client teams.
Image Analysis
Fabric AI can perform various tasks to collections of imagery, such as accurately categorising the imagery to determine usage, or extracting text and adding tags to improve discoverability and increase value.
Effective data validation is crucial for maintaining the accuracy and integrity of your media metadata. Fabric AI’s LLM-enabled pipelines automate the validation of large media datasets against a master source - such as Fabric Origin.
Fabric AI can identify and flag any discrepancies, missing fields or erroneous data for correction. By integrating a 'human-in-the-loop' approach, your team retains oversight and governance control while AI handles the labor-intensive work, ensuring high accuracy at a fraction of the previous time and cost.
Generate major cost-savings, automate repetitive manual cut-and-paste processes
Retain full control of data governance, with staff approval of all changes in the application
Remove process bottlenecks, speed up the process of QC and validation
QC is an essential process, but it is both costly and time-consuming, and creates bottlenecks and delays in delivering time-sensitive content. Yet without Quality Control, bad data could easily end up in front of customers.
Fabric AI can streamline the checking, validation and QC of content metadata by adding a QC portal application onto any data pipeline. Leverage the commercial and cost advantages of LLMs, whilst also deploying the knowledge and expertise of your staff, to drive efficiencies and remove process bottlenecks.
Internal QC captures changes in the review app, creating client-specific training data
Third-party QC uses API integrations with partners, offering global language quality control
AI QC enables one LLM to review another’s output, delivering valuable time and cost savings
Matching records to unique industry IDs across disparate sources is often complex and time-consuming. Fabric AI simplifies this by leveraging advanced AI models to rapidly and accurately match media assets to their corresponding metadata.
Whether aligning cast and crew details, matching synopses, or categorizing image data, the AI enhances the speed and precision of the matching process. This can be customized to your unique dataset requirements, significantly reducing manual effort and improving overall data consistency across your organization.
Accurately match titles to avoid duplication in the supply chain.
Create an authoritative source of truth in your organization
Enable accurate data-enrichment with confidence
Content producers face an overwhelming, time-intensive task of adapting their source data to meet delivery requirements. This laborious and repetitive process not only hampers productivity but also introduces the risk of errors and delays, hindering the timely release of content.
Fabric AI’s distribution pipelines orchestrate the entire process, including intricate data preparation and precision format mapping, all while seamlessly ensuring content is dispatched to a myriad of diverse streaming platforms worldwide.
Automatically transform your metadata into distributor compliant datasets
All mappings and transformations automated
Simply supply schemas and required business rules for delivery
