GenAI text to video engine delivers exponentially faster
video production at scale
For a leading AI content technology provider
“A pressing need to finetune the AI-powered article to video-gen engine with intent, context and accuracy to accelerate adoption and scale digital production.”
– An editor receiving low-grade quality responses
A leading AI content technology provider wanted to accelerate text to video production, Movate’s GenAI-based curation engine delivered transformational impact:
Self- Learning
that met client expectations with minimal rework
Scalable
solution for future growing needs
Business problem
Disruptive consumption shifts to video equivalents of text-based content has placed immense pressure on publishing houses. This has triggered a rise in demand for GenAI-powered video production to scale publishing operations with reliability, speed and at scale.
While the overall objective is clear, there are technical and creative challenges that hinder delivering compelling video equivalents of textual content.
GenAI-powered text to video production demands extensible datasets, robust and intensive model training, and closed loop feedback systems to embed intent and context to minimize output errors. Further verification and validation to vet the video creation process for quality, reliability and scalability.
Customer challenge
Our client is a leader in providing Generative AI (GenAI) technology solutions for enterprises. Hindered with trustworthiness and quality challenges, they aimed to enhance their GenAI powered text-to-video generation platform.
To achieve this, they required assistance to finetune and optimize their GenAI content curation engine. The main goal was to ensure that the engine could effectively handle user prompts, understand scene logic, curate scripts and manage pre-defined video roll sequences. They wanted to train and strengthen their AI solution to produce accurate, trustworthy, and consistent videos, while also reducing feedback cycles and minimizing manual intervention. Additionally, they sought to scale content delivery to meet the exponential growing demand for video formats across digital channels.
Movate’s AI solution
Movate built a GenAI content curation engine that transforms URLs and briefs into structured scripts and scene layouts, using advanced prompt engineering to align tone, intent, context, and output quality from the start. Editors can define creative rules via an intuitive interface, while AI-integrated scene mapping ensures each script is paired with accurate, licensed visuals from Getty and other approved media libraries.
To elevate precision, we integrated a human-in-the-loop validation layer—a network of media-savvy experts who peer-review and refined AI-generated outputs. Their editorial feedback feeds directly into the training and reinforcement learning pipeline, continuously enhancing the model’s ability to interpret nuance, brand context, and cultural alignment. This hybrid learning loop dramatically reduces manual rework and accelerates go-to-market timelines.
The final layer is a self-learning QC engine that validates voice emphasis and clarity, pacing, content fidelity, and visual coherence that are in line with client expectations before publishing.
By combining custom model checkpoints with peer-trained evaluation standards, the system ensures every video meets consumerization-standard quality—transforming generative AI from a creative aid into a scalable, reliable production pipeline.
Movate AI differentiator
Movate AI designed an elevated solution to process user input, that drives more accurate search results reducing feedback cycles. Prompt engineering and AI model training ensured content mapping edge while maintaining quality and scalability across interactions.
With deep domain expertise, rapid deployment accelerators, and enterprise-grade integrations like Azure AI and Cognitive Services, Movate AI empowered this leading AI enterprise scale smarter.
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