AI-Powered discovery: how MotionElements elevates creators

MotionElements is an AI-powered one-stop marketplace for video creators to access and customize creative assets. Time is money for MotionElements’ users, and search is essential in helping them quickly find what they need.

With a global user base speaking multiple languages, MotionElements relies on powerful multilingual search capabilities to deliver fast, accurate, and relevant results across diverse creative content.

“MotionElements’ federated hybrid search empowers creators to seamlessly explore diverse creative assets across our one-stop marketplace—all in their own language. By combining keyword search with zero-shot video search, visual & music similarity search, we deliver an intuitive, AI-enhanced discovery experience that fuels creative workflows.” - Mark Sun, CTO at MotionElements

Challenge: Scaling search

Previously, MotionElements relied on Solr, which became increasingly difficult to maintain. With millions of new content pieces added each month, Solr consumed excessive RAM and struggled with scalability. MotionElements' search needs have also evolved, requiring support for keyword search, visual and music similarity vectors, and image embeddings—capabilities that are difficult to implement efficiently with Solr.

As our content grows, we need a search solution that can handle both traditional and AI-enhanced discovery while remaining scalable and performant, said Mark Sun, CTO of MotionElements.

Solution: Choosing a superior price‑to‑performance ratio, scalability, and multilingual federated

• Price‑to‑performance ratio: MotionElements explored Elasticsearch, Algolia, and Typesense, and ultimately chose Meilisearch because of its price‑to‑performance ratio when handling large catalogs with millions of products.

• Feature fit: Meilisearch’s federated search capabilities and multilingual support were also key factors in MotionElements’ decision.

• Addressing migration concerns, Mark Sun said:

“Initially, we had concerns about migrating its image similarity vectors, but Quentin, the CEO of Meilisearch, provided invaluable support through a Proof of Concept (PoC), resolving all MotionElements’ migration challenges. Meilisearch offered the scalability, speed, and flexibility we needed while keeping infrastructure management minimal. Its federated search and hybrid search capabilities make it a powerful tool in MotionElements’ AI‑enhanced workflows. Federated search lets us retrieve results across multiple product types, making search in MotionElements’ one‑stop marketplace seamless. Hybrid search combines keyword search with external AI‑powered vector search, allowing us to enhance discovery across images and audio."

These features significantly improve search relevance, speed, and user experience, making it easier for the MotionElements team to deliver fast and intuitive search results.

Implementation

Implementing Meilisearch was straightforward, thanks to its user‑friendly API. MotionElements wrote code to set up indexes based on its needs and pre‑generated NDJSON files of its products, which are stored in an S3 bucket. Meilisearch makes it easy to ingest data from NDJSON, dramatically improving MotionElements’ indexing workflow.

Mark Sun said: “Previously, re‑indexing on Solr took weeks, but with Meilisearch, it now takes just hours—saving us countless hours of engineering time. Since MotionElements uses the enterprise cloud version, the MotionElements developer team focuses on search optimization while Meilisearch handles the infrastructure.”

Results

The biggest impact has been faster search performance, improved accuracy, and higher engagement:

• No more worrying about Solr and Zookeeper crashes. "Meilisearch is stable and scalable. Now, we can focus our efforts where they’re needed most, knowing our search infrastructure is stable and dependable.” — said Mark Sun.

• Re-indexing time drastically reduced – "what used to take weeks in Solr now takes just hours."_

• Increased search-based conversions. Faster, more relevant results lead to better engagement and retention.

Conclusion: vision

Going forward, MotionElements expects further improvements in personalization, more efficient AI-powered product recommendations, and continued optimization of multilingual search, all of which are key drivers of its business growth.

As MotionElements continues investing in AI-powered search enhancements, Meilisearch remains a core search infrastructure component for the MotionElements platform. MotionElements’ vision includes:

• Advancing hybrid search – enabling seamless queries across keywords, text embeddings, image embeddings, and music similarity to deliver the best results across multiple vectors.
• Enhancing language‑specific optimizations – for example, in Japanese, when searching in kanji, results should also include matches in hiragana and katakana, while properly handling stop words for better accuracy.
• Optimizing performance at scale – indexing, updating, and searching must remain fast and efficient as MotionElements’ content library grows. Meilisearch should continue to augment vector‑based search, rather than replace dedicated vector databases—providing a fast, flexible, and cost‑effective search infrastructure that evolves alongside MotionElements’ AI‑powered features.