Powering the generative AI era: how Scenario scales asset discovery with Meilisearch

Scenario is an enterprise AI platform that enables studios and brands to train custom models on their visual libraries, create workflows, and leverage agents in their creative process.

By building a "creative memory" unique to each customer, Scenario generates unlimited on-brand assets, from images to video, 3D, and beyond, while maintaining full governance over creative IP. With over dozens of million fully indexed assets and 35 million monthly searches, the platform requires search infrastructure that matches the speed and intelligence of its AI capabilities.

“Our enterprise customers need to find the right asset from millions of possibilities in milliseconds. Whether by keyword, visual similarity, or both, Meilisearch lets us deliver that while we focus on what makes Scenario unique: building AI that truly understands each customer's creative DNA. Meilisearch is really in the right spot - both because of its product and their team. They listen to us and adapt the roadmap to help us push the boundaries of what’s possible in AI powered search.” — Hervé Nivon, Co-Founder & CTO at Scenario.

Overview: Scenario

Scenario’s modern platform enables businesses and creators to generate any kind of media using state-of-the-art generative AI models and streamline content creation. Scenario's community-led library provides a common language for teams to more easily access generative AI tools for content, including image, audio, video, and 3D.

Trusted by leading studios and brands, including Unity, Tripledot Studios, InnoGames, Fastory, and Alan, the platform manages a vast and growing volume of creative assets requiring a modern Asset Data Management (ADM) system to maintain an always-on platform ready for the generative era.

The challenge: scaling search for the AI era

Before adopting Meilisearch, Scenario relied on OpenSearch on AWS to power their asset discovery. As the platform scaled toward tens of millions of indexed assets and 35 million monthly searches, the team needed search infrastructure purpose-built for AI-native workloads:

• Performance at scale: Delivering instant search across millions of assets required more than tuning a general-purpose engine. Users expected real-time results across both text and visual queries.
• Cost and operational overhead: The AWS-managed setup demanded constant manual optimization, diverting engineering time from core AI and product development.
• Growing user expectations: As the asset library expanded, users needed faster, more relevant discovery, making search a product-critical capability rather than a backend utility.

The solution: a modern hybrid search engine

Scenario’s team needed a solution that offered the speed of a dedicated vector database with the rich feature set of a traditional search engine. After evaluating Pinecone and Qdrant, Scenario chose Meilisearch based on a recommendation from the founders of Hugging Face, thanks to Meilisearch’s superior support for hybrid search.

Meilisearch provided:

• A developer-first experience: Clear documentation and a straightforward onboarding process allowed the Scenario team to move fast.
• Enterprise readiness: SOC2 compliance and seamless AWS Marketplace integration streamlined billing and ensured baseline security.
• Hybrid search: The ability to combine Scenario's proprietary embeddings with keyword and multi-modal (image) search in a single interface made it easier for users to navigate the vast library of assets.

Implementation: building a scalable search infrastructure

Scenario’s implementation initially focused on creating a seamless discovery experience, leveraging multi-modal search to allow users to navigate public and private asset libraries via both text and visuals.

• Continuous indexing: the system handles a high-velocity environment where documents are constantly being ingested, updated, and deleted, ensuring the library remains real-time.
• Custom embeddings: rather than using default models, Scenario leverages its own AI capabilities to generate text and image embeddings.
• Vector search integration: these embeddings are then indexed in Meilisearch, powering an "Exploration" feature that lets users find similar assets by directly clicking on images.

The result: scaling volumes with minimal maintenance.

The transition to Meilisearch transformed Scenario’s search into a core product advantage:

Enterprise teams can now discover and reuse existing assets through visual similarity search, reducing duplicate asset creation and accelerating production cycles. — Hervé Nivon, Co-Founder & CTO at Scenario.

• Dramatic reduction in support load: support requests related to search performance have virtually disappeared. Scenario’s team now focuses mainly on fine-tuning vector results.
• Exceptional scale: Meilisearch effortlessly handles upwards of 35 million searches per month, with 80% of those being complex vector searches.
• Productivity gains: by offloading search infrastructure to Meilisearch, Scenario’s engineering team has been able to focus entirely on their core generative AI and API features.
• Increased user engagement: the “exploration” feature, powered by visual vector search, has become a primary driver of time spent on the platform.

Conclusion: pushing the boundaries of AI search

Scenario's partnership with Meilisearch is entering its next phase. As the platform's library of AI models grows past 200 and counting, search must go beyond asset discovery: intelligently matching each task to the most appropriate model becomes just as critical as finding the right asset.

The team is building toward AI agents that autonomously surface relevant reference assets during the creative process and recommend the best-fit model for any given task, delivering context-aware results in real time.

By choosing a partner that values adaptability and direct feedback, Scenario has built a search foundation that not only meets the demands of today’s tens of millions of documents but is ready for the next stage of scaling.