[Agora](https://www.searchagora.com/) chose Meilisearch to power lightning-fast, intuitive, and scalable search experiences across millions of ecommerce products.

Agora is a revolutionary ecommerce search platform aiming to become the decentralized "world store,” which aggregates products from thousands of smaller online shops into one cohesive marketplace.

It's built for shoppers looking to effortlessly discover, compare, and purchase products from multiple vendors within one unified cart. Agora has ambitious scaling plans, aiming to index up to 1 billion products by the end of 2025. To do this, the team needed a blazing-fast solution capable of delivering relevant search results instantly and at scale.

>“Meilisearch's combination of semantic search and speed was the best we’ve seen on the market” - Param Jaggi, CEO & Founder at Agora

![Agora gif.gif](https://unable-actionable-car.media.strapiapp.com/Agora_gif_eca98f7605.gif)

**Challenge: building lightning-fast, relevant search across diverse ecommerce platforms at massive scale**
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As Agora's dataset rapidly grew from millions toward a projected billion product listings, their initial search setup faced significant hurdles that threatened user experience and growth:

- **Challenge: aggregating and standardizing diverse data sources** 
Agora pulls product data from thousands of Shopify and WooCommerce stores, each with unique data structures. To create a unified shopping experience, Agora faced a challenge of standardizing complex data into one comprehensive schema that could be seamlessly indexed and searched.
- **Challenge: providing immediate, relevant search results.**
Initially, Agora used Mongo Atlas Search, where search queries often took 5 to 10 seconds. It significantly deteriorated user experience and conversions. Agora required a solution delivering instant results to ensure users discovered the right products rapidly and confidently, even for complex or ambiguous queries.

- **Challenge: maintaining control and flexibility over AI embeddings.** 
Previous search solutions did not provide Agora with sufficient control over their own vector embeddings, limiting flexibility and customization. Agora sought a solution enabling them to leverage their embeddings, fine-tune relevancy, and experiment freely with semantic and keyword search combinations.

**Solution: Leveraging Meilisearch’s hybrid AI search engine for exceptional ecommerce discovery**
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Agora turned to Meilisearch for its powerful combination of speed, intuitive semantic search capabilities, and unmatched ease of customization. Meilisearch addressed Agora’s challenges head-on by:

- **Lightning-fast search-as-you-type results:** Meilisearch processes searches in under 100 milliseconds, allowing real-time, dynamic result updates as users type, significantly increasing relevancy and conversion rates.
- **Hybrid semantic & keyword search** powered by vector embeddings: Agora leverages Meilisearch’s AI-powered semantic search capabilities by integrating OpenAI’s CLIP model embeddings. This allowed customers to intuitively search for products using both textual input as well as search by image.
- **Customizable schema and indexing flexibility**: Meilisearch’s loosely typed document structure enabled Agora to standardize and index complex product data across diverse sources.

**Implementation: how Agora built a blazing-fast product search with Meilisearch**
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To deliver fast, relevant, and intuitive search experiences, Agora implemented Meilisearch as follows:
1. **Multi-source product crawling:** using its own custom crawler called “Hermes”, Agora scraped and standardized product data from diverse Shopify and WooCommerce storefronts into a single structured format optimized for indexing with Meilisearch.
1. **Leveraging advanced embeddings for semantic discovery:** Agora then integrated Cohere for semantic text embeddings and OpenAI’s CLIP model to generate powerful semantic embeddings for products' images. These embeddings augmented keyword searches, enabling a hybrid search that blended semantic depth and keyword precision, drastically enhancing result quality.
3. **Real-time search optimization through filtering, sorting, and ranking rules:** Meilisearch helped Agora refine search relevancy by balancing semantic and keyword search. This was essential for complex e-commerce queries and creative product names. By weighting semantic search, Agora prioritized relevant terms within context, ensuring accurate results even with unconventional naming.

**Results: transforming ecommerce search with AI-powered discovery**
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Adopting Meilisearch helped Agora’s team overcome their biggest search challenges, significantly improving their user experience and driving measurable performance gains.

Some of the results include:
- **Dramatically improved search speeds**: Search queries, previously averaging around 2 seconds, now return in approximately 300 milliseconds (with core Meilisearch responses at just under 100 milliseconds). Agora's team is confident in further reducing this down below 200 milliseconds, significantly improving user satisfaction and search conversion rates.
- **Increased conversions and customer trust:** Since implementing Meilisearch, Agora saw a 1.7x to 2x increase in weekly user retention and a notable uptick in order conversions, directly attributed to the improved search experience and consistently relevant results.
- **Enhanced discovery with semantic embeddings:** Agora’s users can intuitively search through both text and images, easily discovering desired products using natural language or visual descriptions. Embeddings also unlocked new capabilities like strong similarity-based recommendations, expanding product discovery avenues for shoppers.

**Conclusion: empowering Agora’s ambitious vision for a unified "World Store“**
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Agora's mission to create a unified marketplace experience across millions—soon billions—of products demands an exceptional search solution that balances speed, relevance, and flexibility.

Meilisearch proved to be the perfect choice, enabling Agora to seamlessly scale its product catalog while maintaining outstanding search performance, intuitive semantic discovery, and ultimate flexibility.

With Meilisearch’s powerful hybrid search engine, Agora is confidently positioned to deliver a truly transformative e-commerce experience.


Scaling to billions: How Agora elevates e-commerce search with Meilisearch AI

**[Rayon](https://rayon.so/) is a cloud-based design platform for architecture and interior design that enables teams to collaborate in real-time.**

With millions of monthly searches and users in over 100 countries, Rayon needed a fast, multilingual search solution to keep up with explosive data growth. They turned to Meilisearch to power their semantic search and scale seamlessly.

>“Meilisearch is very powerful for our needs, especially its semantic search, which effortlessly helps users find relevant objects - even without precise queries.” - said Bastien Dolla, CEO & Co-founder at Rayon.

**Challenge: delivering fast, intuitive, and scalable search experience, while accommodating rapid growth.**
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With a rapidly growing user base in over 100 countries, Rayon needed to address critical challenges, including improving search functionality and supporting the rapid expansion of its database and usage while enhancing the overall user experience.

**Challenge: Multilingual search for a global audience.**
Traditional keyword-based search systems usually require users to know the exact name of an asset, often in English. Rayon’s non-native English-speaking users make up a significant portion of its global audience, so the platform needed to support searches in multiple languages in order to eliminate friction.

**Challenge: Rapid data expansion.**
Rayon’s platform offers access to over 4,000 assets, which represent real-world objects like furniture or textures. Users can also create and publish their own libraries, which leads to the rapid growth of the repository. With approximately one terabyte of new data added weekly, conventional tools simply couldn’t keep pace.

**Challenge: Scaling demand.**
The platform handles 2 million searches monthly, and expects to hit 20 million by year’s end. This necessitated a high-performance, scalable solution.

**Solution: creating an AI-powered search experience for Rayon’s global user base.**
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Rayon turned to Meilisearch to address its challenges with multilingual search, scaling, and efficient data management. These were addressed by:

- Leveraging AI-powered [semantic search](https://meilisearch.com.cn/blog/semantic-search) for intuitive discovery: rather than relying on exact keywords, Meilisearch understands intent. For example, whether a user searches for “Scandinavian style” or “modern sofa with clean lines,” relevant results are quickly delivered.
- Enabling multilingual search: In addition to understanding the natural meaning of a query, Meilisearch supports non-English language searches, leveraging OpenAI embeddings to eliminate language barriers. For example, a search for 'chaise' in French or 'silla' in Spanish will return relevant chair designs, regardless of the language used.
- Scalable architecture for rapid growth: By indexing only design asset libraries and using token-based access control, Meilisearch efficiently manages data growth while ensuring stable performance and secure, authorized access.
- Open-source flexibility and Rust compatibility: The open-source origins of Meilisearch ensures that Rayon retains control over its search infrastructure, which provides them the flexibility to deploy their own instance if needed. Compatibility with Rust, the programming language Rayon is built on, is an important commonality to Rayon as they have a strong Rust team, making it easier for them to integrate and work with Meilisearch

**Implementation**
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Rayon implemented an efficient indexing process to ensure users could quickly and accurately find the design assets they needed:
1. Library processing and SVG generation: When users publish a library of design assets, Rayon processes the files and generates SVG images for each element. These images serve as visual representations of the assets, making it easier for users to identify what they are searching for.
2. Embedding generation with OpenAI: Rayon uses OpenAI’s advanced AI models to generate embeddings for each design asset. These embeddings capture the semantic meaning of the asset, enabling Meilisearch to perform highly accurate searches based on natural language descriptions.
3. Token-based access control: Meilisearch’s token system allows Rayon to maintain strict control over data visibility while still providing a seamless search experience.

**Results**
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By solving key challenges, Meilisearch has become essential to Rayon’s mission, delivering faster searches, happier users, and a platform built for the future:
- **Multilingual search for a global audience.** Designers can now search for assets in their native language, eliminating the need for English-only queries.
- **Faster, more intuitive search:** Meilisearch’s high-performance engine enables users to find design assets in seconds using natural language, without needing exact names.
- **AI-powered discovery** – With [OpenAI embeddings](https://meilisearch.com.cn/docs/learn/ai_powered_search/getting_started_with_ai_search#generate-embeddings-with-openai), Rayon's search goes beyond keywords, allowing users to find assets based on descriptions, even without precise terminology.
**Scalability for rapid growth** – Meilisearch indexes 250,000–350,000 searchable items, supporting both Rayon’s library and user-generated content. With 1TB of new data added weekly, it seamlessly handles growing search volumes—scaling from 2M to an expected 20M searches per month by year-end.

**Conclusion: Solidifying Rayon’s position as a leader in cloud-based design tools.**
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Meilisearch has become essential to Rayon's mission by solving multilingual access, scalability, and asset discovery challenges.
As Rayon continues to innovate, the team plans to explore new capabilities, such as visual search and deeper data and [RAG](https://meilisearch.com.cn/blog/how-to-build-rag) integration, to evolve into a comprehensive platform for architects and designers.
Looking ahead, their vision goes beyond visual assets and drawings, aiming to integrate procurement details, material specifications, and other essential data into a **truly comprehensive design hub.**




How Rayon leverages AI semantic features for a seamless design UX 

**[MotionElements](https://www.motionelements.com/) 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_search.gif](https://unable-actionable-car.media.strapiapp.com/Motion_Elements_search_50cb3914a5.gif)
>“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**
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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.

![MotionElements_multimodal.gif](https://unable-actionable-car.media.strapiapp.com/Motion_Elements_multimodal_6cfa386d8d.gif)

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



AI-Powered discovery: how MotionElements elevates creators

[TutKit](https://www.tutkit.com/en) is a comprehensive multilingual learning and content platform that offers training materials, marketing resources, and creative assets to its subscribers worldwide. Built by German agency [4eck Media](https://4eck-media.de/), the platform stands as a testament to the power of effective search functionality in educational platforms. 

**TutKit self-hosts Meilisearch to power high-performance search functionality across their Laravel-based learning platform, managing 15,000+ multilingual content pieces with millisecond response times.**

![TutKit.gif](https://unable-actionable-car.media.strapiapp.com/Tut_Kit_f4b038e4a6.gif)


>"With Meilisearch, we have chosen the right company for the perfect search." - Matthias Petri, founder of TutKit.com and managing director of [4eck Media](https://4eck-media.de/).

The challenge: scaling search for multilingual content
---------

TutKit.com's initial search functionality, implemented by an external agency using MySQL, suffered from severe performance issues due to inefficient programming. With the platform hosting tens of thousands of content pieces, users endured unacceptable loading times of up to 10 seconds for search results—a critical flaw for a content platform. The system also lacked proper multilingual support, making it impossible to effectively filter and display results across different languages.

Frustrated with these limitations, the TutKit team decided to take matters into their own hands. They began a comprehensive refactoring project, bringing the development in-house with a clear goal: to create an ultra-fast search system that could handle multiple languages.


The solution: implementing Meilisearch
---------
The search for a solution that would provide complete control over search results led Tutkit to Meilisearch. While the platform's modern UX and compelling website initially caught their attention, the final decision was driven by several additional factors:
- **Lightning-fast search performance**: while the platform's modern UX and compelling website initially caught their attention, it was Meilisearch's exceptional speed that stood out—presenting a dramatic improvement over their existing system
- **Robust multilingual support.** Meilisearch powers a fast search performance across 26  languages on TutKit platform, coupled with straightforward management capabilities.
- **Regular platform updates and improvements.** Meilisearch’s regular monthly updates continue to bring valuable improvements, enhancing the experience for both the internal TutKit team and the website visitors.

Technical implementation
---------
The TutKit development team executed a comprehensive implementation strategy:

 **1. System setup:** the implementation of Meilisearch began with a straightforward server installation from the official website. To ensure reliable operation, the team configured a system to manage the Meilisearch instance, including automatic startup and failure recovery. This configuration guarantees continuous service availability, even after server restarts.
 
**2. Security:** the instance was implemented through a dual-key system: 
- a master key for comprehensive backend access, which allows full access to the Meilisearch instance (including creation and index management), is used exclusively in the backend. 
- A custom API key with limited permissions for front-end operations. 
This separation creates a robust security layer, protecting backend operations while maintaining efficient front-end interactions. The Laravel application connects to Meilisearch using the API key, effectively minimizing the risk of unauthorized access and potential data breaches.

**3. Optimized data flow:** built on the Meilisearch PHP library, TutKit's custom Laravel integration ensures only necessary changes reach the search index. This selective approach minimizes data processing overhead while maintaining complete control over the implementation, resulting in streamlined index updates and improved search performance.

**4. Smart batch processing:** A custom Laravel console command handles large-scale updates and initial indexing processes. By targeting only relevant records and executing selectively for major changes, this tool significantly reduces server load and maintains system reliability through efficient resource utilization.

**5. Real-time synchronization:** The system automatically detects and processes changes in Laravel models, updating Meilisearch indices instantly without full reindexing. This targeted approach keeps search data synchronized with minimal resource usage, ensuring fast, responsive search functionality for users.

Impact and results
---------
The implementation of Meilisearch has delivered significant benefits for TutKit:
**Performance improvements:** thanks to Meilisearch's continuously improving indexing speed, TutKit can process new data into their search index with remarkable efficiency. The platform's lightning-fast search capabilities significantly reduce server load by eliminating the need for resource-intensive MySQL queries. 

**Real-time filtering capabilities** further enhance the system's responsiveness, allowing users to instantly refine and view their search results.

**Cost benefits:** adoption of Meilisearch as an open-source solution has delivered cost advantages for TutKit. Beyond eliminating ongoing licensing fees typically associated with paid providers, the platform's intuitive nature allowed TutKit's in-house development team to handle implementation and maintenance independently, avoiding external developer costs.

**User experience impact:** improved search functionality has directly enhanced the visitor experience on TutKit's platform. Fast, accurate search results have eliminated user frustration caused by long wait times and irrelevant results. This enhanced user experience has proven valuable for business outcomes, as satisfied users are more likely to convert into paying customers.

**Scalability success:** despite managing hundreds of thousands of content pieces across multiple languages, the platform maintains its exceptional performance. Meilisearch consistently delivers precise, relevant results while handling TutKit's growing content library and language variants. This robust scalability gives the team confidence in their ability to expand the platform's capabilities over the long term.

Looking forward
---------

TutKit continues to scale its platform, adding more content and language variants with confidence in Meilisearch's ability to handle growing demands. The team looks forward to the further development of [AI-powered features](https://meilisearch.com.cn/solutions/hybrid-search) that could further enhance search intelligence and simplify the user interface by reducing the need for extensive filtering options.

Matthias Petri, founder of TutKit.com and managing director of [4eck Media](https://4eck-media.de/) said: "A good search is like a stage for large content platforms, where exactly what the user wants to see is always played. Without Meilisearch, our platform would be just one of many - with mediocre search results and without the clear added value that we would like to offer our customers."

For TutKit, Meilisearch has proven to be more than just a search solution – it's become a cornerstone of their platform's success, enabling them to deliver on their promise of effective learning and productive application for users across the globe.


TutKit's journey with Meilisearch: powering multilingual learning at scale.

_Bildhistoria chose Meilisearch to power the search through its quickly growing database of historical photographs and affiliated data, driving the search from multiple sources of metadata and mapsearch._

> _“Bildhistoria's large volumes of historical photos, persons, places and organizations require a fast and easy to maintain search engine, like Meilisearch. Meilisearch is used on so many places around Bildhistoria, enabling our users to explore photos and learn more about ancestors and local history. This makes Meilisearch instrumental in the success of our community.”_ - Per Rohdin, board member of the community, and project manager at Bildhistoria.

[**Bildhistoria**](https://bildhistoria.se/)**, a Swedish non-profit, operates the country's largest collection of copyright-free historical photographs. It focuses on collecting and preserving historical photographs from museums and private collections. While the project has not yet reached full general availability, Bildhistoria already boasts an impressive archive of around 90,000 images dating from the 1950s and earlier.**

The project aims to create a dynamic and collaborative platform where the public can interact with history, and where professional and amateur genealogists alike can contribute by enriching the photographs with additional data.

**Challenge: Bildhistoria tackles data fragmentation and community engagement**
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From the start, Bildhistoria faced three main challenges in bringing their vision to life:

**Challenge: searching across multiple types of data:** Bildhistoria's main challenge with search is effectively searching across the different data types. The platform features not only photographs but also information about people’s names, organizations, and locations associated with each image. The team needed a search solution that could seamlessly navigate through these different data types. To address this, they have implemented Meilisearch’s "[multi-index search](https://meilisearch.com.cn/docs/reference/api/multi_search)" that returns results from all the different indexes, allowing users to find relevant information regardless of whether they are searching for a person, place, or organization.

**Challenge: data scattered across multiple sources:** Historical photographs were scattered across various sources, including museums, archives, and private collections. There was no centralized platform in Sweden to consolidate these copyright-free images, making it difficult for researchers and the public to access this wealth of visual history.

![](https://unable-actionable-car.media.strapiapp.com/AD_4n_Xf_R_Ormjkncn9_Uj8i_Un6vaqs_Wi_I_Uln1hutwuz0h_KI_wm_GIA_P_Wry5j_Rj5_R_Altplg_VV_5_Jy_ANKPL_2_Hayt3_Jv_Ku_A_Gw9ou_P8l0va_9f26862acc.jpg)

**Challenge: Community accessibility and engagement:** Unlike traditional digital archives, Bildhistoria is an interactive platform where users can contribute additional historical facts and enrich the metadata associated with the photographs. The community of genealogists is large and diverse, spanning professional organizations to amateur genealogists and the general public. Bildhistoria’s team knew from the beginning that the only way to make the project work was to make it easily accessible and easy to use for everyone.

**Solution: Bildhistoria chose Meilisearch for simplified operations, team-wide accessibility, and rapid data synchronization**
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After careful consideration, Bildhistoria chose Meilisearch as their search solution for several key reasons:

**Less operational overhead:** Meilisearch proved much easier to set up and connect to existing indexes compared to alternatives like Elasticsearch. This was crucial for a non-profit organization with limited resources.

**Accessibility for all team members:** Meilisearch's intuitive admin interface allowed even non-technical staff to understand and configure search settings. This democratization of the search configuration process was particularly valuable for fine-tuning Bildhistoria’s search experience.

**Fast synchronization:** Bildhistoria required efficient update mechanisms to keep Meilisearch indexes in sync with their database changes. Meilisearch's ability to handle rapid updates and re-indexing was crucial for maintaining an up-to-date and responsive search experience.

**Implementation**
------------------

Bildhistoria's implementation of Meilisearch involved several key steps:

1.  The team initially deployed the open-source version on Azure but faced challenges with the hosting environment.
2.  Bildhistoria team then switched to Meilisearch Cloud, which provided better management and support, allowing them to focus on their core mission rather than infrastructure concerns.
3.  Meilisearch was integrated throughout the platform, powering the main search functionality and an innovative map search feature.
4.  Finally, Bildhistoria invested significant effort in creating an efficient data synchronization pipeline between its database and Meilisearch to handle the constant flow of new information and user contributions.

**Results**
-----------

The implementation of Meilisearch has yielded impressive results for Bildhistoria:

**Multi-index search:** Users can now perform searches that capture results across various types of data—photographs, persons, organizations, and places. This holistic approach has dramatically improved the user experience, allowing for a more comprehensive exploration of historical connections.

**Improved user experience:** The search functionality provides alternative search suggestions for similar queries and provides rich context for each image.

**Interactive map search:** Meilisearch’s [Geosearch](https://meilisearch.com.cn/docs/learn/filtering_and_sorting/geosearch) allows users to explore photographs associated with specific locations in Sweden. As users interact with the map, new searches are performed in real-time, updating the displayed photographs.

![](https://unable-actionable-car.media.strapiapp.com/Bildhistoria_Mapseaechdemogif_ezgif_com_video_to_gif_converter_a70a28b422.gif)

Overall, the team noticed a significant improvement in engagement and user experience since the implementation of Meilisearch.

**Conclusion: Meilisearch is a key part of Bildhistoria's vision for the future**
---------------------------------------------------------------------------------

Looking to the future, Bildhistoria has ambitious plans to further leverage Meilisearch to expand its platform's capabilities to become Sweden's leading platform for historical photographs. While initially focused on the Swedish market, Bildhistoria is open to European collaborations to scale the concept internationally. The next steps for improving the user experience on the platform would include:

*   Integrate AI-driven facial recognition across photographs to link images of the same individuals across different time periods and locations.
*   Add a _houses and buildings_ index, enabling users to trace the lifecycle of specific structures through photographs.
*   Implement features encouraging community collaboration, such as allowing premium users to follow and receive notifications about new edits and contributions.
*   Scale operations to manage a vast archive of 1-3 million photographs by collaborating with more museums and institutions.

As the project progresses and reaches full production mode, Bildhistoria continues to assemble the right technical team to establish a sustainable and long-term setup for the community, laying the groundwork for its future growth and impact.

Meilisearch powers the discovery of history for Bildhistoria

[OCTO Technology](https://www.octo.com/) is a consulting firm specializing in new technologies and the challenges of digital transformation. Headquartered in Paris, it’s recognized for its technological expertise and innovative approach.

> "The searchable page with Meilisearch is really fast and efficient. Meilisearch allows us to meet the customers’ needs with a turnkey solution and reactive support." - Simon Belbeoch, Tech Lead/Manager at Octo Technology

**OCTO Technology uses Meilisearch for its client, an undisclosed customer. The customer runs a platform that aims to connect young people with relevant job opportunities, allowing them to search all kinds of information about integrating into working life. The project involved implementing a search feature to look through data consolidated from various sources.**

Given the specific needs of their sector, the client strongly preferred an open-source solution from the beginning. Other decision-making factors included good performance and greater control over data, allowing users to search through a comprehensive list of jobs to find their ideal offer quickly.

Challenge
---------

The client faced search challenges that required a specific solution, not just standard configurations or a generic search engine. Their website had custom fields that required custom programming. They aimed to replace their previous search engine, [Algolia](https://meilisearch.com.cn/docs/learn/what_is_meilisearch/comparison_to_alternatives#algolia), with a new Meilisearch solution, without compromising on functionality or performance

The task became more complex due to the need to aggregate data from various partners in differing formats and manage incomplete data to ensure a consistent search experience across the platform. OCTO’s customer also required more control over search results and sought integration with their CMS, Strapi, for better data control.

Why OCTO chose Meilisearch
--------------------------

Before settling for a specific search provider, OCTO Technology ran a few different scenarios on behalf of its customer and explored the following solutions:

*   Building a proprietary, in-house solution was briefly considered but ultimately ruled out because of the cost-effectiveness considerations and high-performance expectations.
*   Leveraging their prior experience with the platform, the OCTO team initially considered Algolia integration. However, they ultimately favored the open-source transparency offered by Meilisearch, which provided them with greater control over technology and customization settings.
*   Although the team at OCTO considered Elasticsearch too complex for the given client's use case, they decided to use it for logging and configuring data. Ultimately, despite having previous Elasticsearch expertise, the product's complexity prevented the team from benefitting from the Elasticsearch’s offering fully.

Ultimately, the following factors played the most in the decision-making:

### Preference for an open-source

For transparency and to align with the specific needs of the OCTO's client vertical, there was a requirement for the tech stack to be primarily open-source and publicly accessible.

The stress tests were conducted with Meilisearch, and the performance provided better search results compared to the custom search solution that was initially in place.

### Seamless CMS integration

OCTO’s client utilized [Strapi](https://meilisearch.com.cn/docs/learn/cookbooks/strapi_v4), a headless CMS solution, for data management and discovered that Meilisearch supports seamless integration via the CMS. Adding data and updating the front-end were straightforward, with the Strapi connection functioning smoothly.

### Consolidation of multiple data sources

This challenge highlighted the need for precise control and robust performance.  To demonstrate Meilisearch's capabilities, the OCTO team conducted a comprehensive proof of concept, focusing on aggregating various job offers from diverse sources and formats.

OCTO conducted a series of stress tests to evaluate the search page functionality under extreme conditions, including handling high data volumes and maintaining search functionality during indexing. While measuring response speed, they also tested the system’s stability under heavy data ingestion to assess whether the search capabilities would withstand extreme condition.

* * *

Ready to elevate your search experience?

[Contact sales](https://dub.sh/meili-sales)

* * *

Implementation
--------------

The platform, developed using Next.js and hosted on Github (though not publicly linked at OCTO’s team request for client anonymity), showcases OCTO’s client's self-managed instance. 

The search engine was straightforward to implement, even without specific Meilisearch expertise, and was integrated with Strapi through a [plugin](https://meilisearch.com.cn/docs/learn/cookbooks/strapi_v4). In the front-end, OCTO developed custom Strapi components to allow their client to easily manage UI edits via the CMS.

> _“At the beginning, we had to create custom components, extending from open-source, to meet our client’s specific requirements. Since then, we have consistently reused these front-end components. The early stages of the Strapi to Meilisearch plugin encountered some bugs, but thanks to the responsive collaboration with the Meilisearch team, these issues were resolved swiftly.”_ - Simon Belbeoch, Tech Lead/Manager at Octo Technology.

Further comprehensive stress tests were included in the implementation process to ensure robust system performance. These tests involved data insertion, simultaneous searches, and data insertion while searching.

Results
-------

The outcome of implementing Meilisearch was positive. The comprehensive proof of concept and stress tests conducted before going live showed that the search performed well and the page did not break. OCTO’s customer now fully and independently manages the instance, enhancing the user experience with more relevant search results that reduce user frustration. Although specific metrics were not set beforehand, the implementation successfully met the project's goals, with OCTO’s client observing that Meilisearch's search performance exceeded that of their previous custom search solution.

Vision
------

The experience gained through the completed project with Meilisearch allows OCTO to integrate Meilisearch into its discussions with clients at an earlier stage. It positioned the OCTO team to more effectively meet customer requirements, leading them to consider adopting Meilisearch for internal usage as well as for its new clients. Additionally, the client has expressed strong interest in Meilisearch, prompting considerations for its application in other products.

* * *

Want to stay in the loop with everything Meilisearch? Subscribe to our [newsletter](https://dub.sh/meili-newsletter). Help us shape Meilisearch's future by checking out our [roadmap](https://roadmap.meilisearch.com/#release-v1-8&utm_source=blog) and participating in our [product discussions](https://github.com/meilisearch/product/discussions#blog.meilisearch.com).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/#release-v1-8&utm_source=blog).

Meilisearch is a partner of choice for OCTO Technology

[Symfony](https://symfony.com.cn/) is an open-source web development framework for PHP that enables users to create web applications by utilizing sets of reusable components. Originating from France, Symfony combines a web application framework, a philosophy, and a vibrant community.

**With Meilisearch, Symfony gained better control over parsing and indexing of documentation content to speed up and improve the quality of search results.**

> _Using Meilisearch has been a delight so far. It all starts with its quick and simple installation process, followed by its great and thorough documentation, and it ends with a powerful search engine with mind-blowing performance. Meilisearch feels fun, fresh and uncomplicated._ \- Javier Eguiluz, Symfony Evangelist.

Challenge
---------

Fast and efficient information retrieval from [Symfony’s documentation](https://symfony.com.cn/doc/current/index.html) is essential for the success of its rapidly growing community. The Symfony Documentation, including code samples, comprises well **over 12 million words**, and throughout its history, Symfony has published **30 different version updates**, making smooth information retrieval from the documentation crucial. In fact, to ensure the search function is as accessible as possible for [symfony.com](https://symfony.com.cn/) visitors, the team integrated multiple keyboard shortcuts to open the search input across the entire website.

In the past, Symfony’s team explored various products and services, including Apache Solr, Elasticsearch, Google Custom Search Engine, and Algolia Search. However, the team realized the need for better control over the parsing and indexing of search content in order to tweak the search rules and improve website navigation and the overall search experience as a result. While using a fully hosted service is convenient, the pre-existing [configuration via JSON files](https://github.com/algolia/docsearch-configs/blob/master/configs/symfony.json) proved cumbersome and did not provide Symfony with the flexibility it required.

Secondly, there was potential to improve the overall quality of the search results, with the Symfony team receiving some complaints about inaccurate or missing results from the documentation users. Unfortunately, due to the aforementioned lack of control over the parsing and indexing processes, significant improvements to these results were unattainable and the need for a better alternative became evident.

Why Symfony chose Meilisearch
-----------------------------

Determined to find a better alternative, Symfony’s team started research on search engines. From the very beginning, the preference was for open-source projects developed in Go, because there was existing internal prior expertise in that language. Although there were many search solutions on the markets that matched those requirements, many of them lacked active development, didn’t have an active community, or missed essential features such as support for synonyms.

Finally, the team looked at Meilisearch, which provides an open-source search engine developed in Rust, along with an option to self-host, and quickly found a fit. Meilisearch’s established reputation as an open-source project with a vibrant community played a pivotal role in influencing the final decision.

Implementation
--------------

Meilisearch offers integrations with a wide range of programming languages and frameworks. In addition to hosting Symfony's index data, the Meilisearch team provided valuable assistance during the migration process so that a single developer who had no prior experience with Meilisearch was able to handle an entire migration and the implementation process, using the [Meilisearch PHP integration](https://github.com/meilisearch/meilisearch-php) with the Symfony [HttpClient component](https://symfony.com.cn/http-client).

Here’s a brief overview of the technical integration process:

1.  **Parsing Symfony Documentation:** For Symfony’s documentation, the process included splitting the entire page contents into multiple small documents in order to produce better search results, closely related to the query terms.
2.  **Indexing Documents** was the simplest part of the process and included configuring [stopwords](https://specs.meilisearch.com/specifications/text/0123-stop-words-setting-api.html#_3-functional-specification) (list of words to be ignored in search queries) and synonyms to produce better search results.

For a full walkthrough of how Symfony implemented parsing of their documentation, documents indexing, and search engine interface, we recommend taking a look at their blog post on [migrating symfony.com search engine to Meilisearch](https://symfony.com.cn/blog/migrating-symfony-com-search-engine-to-meilisearch).

Results
-------

The Symfony team focuses primarily on two different aspects of performance when evaluating the results:

*   **Performance of indexing contents:** Meilisearch can index approximately 800 full documents per second, which includes the processing of hundreds of stopwords and tens of synonyms.
*   **Performance of queries:** Symfony, including all versions of Symfony Docs and documentation for bundles, parses 12,754 files and generates 85,146 Meilisearch documents. The total time taken to parse, create, and index all these documents is 17 seconds. The search index consumes approximately 1.2 GB of disk space, with most queries returning results in less than 10 milliseconds.

> Nicolas Grekas, Core team member at Symfony, summarizes it as follows: _“These numbers are quite impressive and offer an excellent experience when updating search results in real-time as you type."_

To complete the evaluation process, the Symfony team conducted a comprehensive comparison between the previous search engine results and the new Meilisearch engine. They tested typical queries, rare queries, long queries, and queries with typos. Below is a [comparison of the search results](https://symfony.com.cn/blog/migrating-symfony-com-search-engine-to-meilisearch#evaluating-the-result) obtained before (left) and after implementing Meilisearch (right):

![](https://unable-actionable-car.media.strapiapp.com/lp_X3_F9zw6h_F_Ka_8_Wrm3h_W4_X2_O8_KL_8_OA_4hfb_C9igir_cs_F_Pwp_M9_NQA_Rs_A_Rm_Nqdb1_Sjozss6q4_L_Pcg_n5_Vp_Pj_Selc_J_Ms_Wk_Cn_Zrbm_C_eb917263f4.jpg)

![](https://unable-actionable-car.media.strapiapp.com/Yyk95_A_Bhdkqzb_E2nia_BG_Rth_F_Iq_C_yu_Ek4_N_Duoya_AGIL_4ld_Xvi_Nn_TNJ_2_Ybw_R7_A2_M_Xtij4g_Xi_Hemu5t_H4_HBWAMBL_Zmv_Z6hv_Eks_Gzv_459f570ba3.jpg)

Since the new search experience was deployed, the team hasn't received any negative feedback from the community, which normally served as an indicator of the quality of the search experience.

Vision
------

Currently, Symfony's team is satisfied with Meilisearch's feature offering, which offers all the essential features needed, including indexing arbitrary information, sorting, result weighting, handling stopwords, synonyms, facets, filtering, and more.

> Javier Eguiluz, Symfony Evangelist, advises: "_Before integrating it into your projects, be aware of some_ [_known limitations_](https://docs.meilisearch.com/learn/advanced/known_limitations.html)_. If your project requirements are complex (e.g., cross-faceting between multiple indexes, etc.), make sure to check if these advanced features are already supported. Additionally, it's a good idea to review the_ [_Meilisearch public roadmap_](https://roadmap.meilisearch.com/)_._"

For a full walkthrough of how Symfony implemented parsing of their documentation, documents indexing, and search engine interface, we recommend taking a look at their blog post on [**migrating symfony.com search engine to Meilisearch**](https://symfony.com.cn/blog/migrating-symfony-com-search-engine-to-meilisearch)**.**  
  
Ready to optimize your documentation search? [Start a free trial](https://meilisearch.com.cn/cloud) with Meilisearch today.

Cracking the code: Symfony's search solution for 12 million words of documentation

Headquartered in New York and Paris, Hugging Face is an open-source provider of machine learning technologies, allowing users to train, deploy, and share AI models using Hugging Face open-source libraries and the Hub.

**By partnering with Meilisearch Cloud, Hugging Face actively fulfills its visionary commitment to advancing the democratization of AI.**

![](https://unable-actionable-car.media.strapiapp.com/ezgif_com_video_to_gif_1_e697b647e1.gif)

> _“Today, Meilisearch is used by Hugging Face to power the discoverability of 300,000+ AI models, datasets, and demos. It’s really important for AI democratization because if the knowledge is out there, but you can’t access it - what’s the point?” -_ Mishig Davaadorj, software engineer at Hugging Face

Challenge
---------

On its platform, Hugging Face enables users to upload their AI models, datasets, and demos. With a vibrant community of users constantly engaging with the [Hugging Face Hub](https://hugging-face.cn) - a platform for sharing and searching for Machine Learning artifacts - the question of discoverability takes center stage.

The Hugging Face Hub hosts over 220,000 AI [models](https://hugging-face.cn/models) catering to a variety of [machine-learning tasks](https://hugging-face.cn/tasks), all neatly stored in repositories. These model repositories are designed to make the exploration and utilization of models as seamless as possible. Each AI model in the Hugging Face repository is accompanied by a model card, a project file containing valuable metadata, which plays a crucial role in enhancing discoverability, reproducibility, and sharing. Model cards also provide important information about AI models’ biases and limitations, model descriptions, and training guidelines, and serve as comprehensive guides for users seeking models on the Hub or uploading their own.

Before the introduction of Meilsearch, Hugging Face relied on a simple filtering and keyword search solution. However, it became evident that there was a growing demand for a more flexible and typo-tolerant full-text search solution. This solution needed to ensure higher default relevancy for every search query and leverage the additional attributes and metadata stored within model cards.

Why Hugging Face chose Meilisearch Cloud
----------------------------------------

Before integrating Meilisearch into its ML model repository, Hugging Face had already been utilizing Meilisearch's free-of-charge open-source solution as a search engine for its [open-source libraries](https://hugging-face.cn/docs)’ documentation, including transformers & diffusers. This documentation comprised approximately 500 pages and had been in use for over a year. Because the team had already gained positive experiences from implementing and operating Meilisearch, when the need for enhanced discoverability in model card searches arose, there was no requirement for additional testing or proof of concept.  

**1\. Customizability of the ranking rules**

During the evaluation process, the Hugging Face team also considered Mongo Atlas Search. However, they weren't satisfied with the customizations available for ranking. Several factors were taken into account in order to enhance model card discoverability, such as project names, descriptions, and the number of likes or downloads on each card. Meilisearch has demonstrated greater flexibility and adaptability in accommodating these search criteria.

**2\. Transitioning to Meilisearh Cloud for its ease of use**

Having achieved successful implementation of Meilisearch in its documentation, the Hugging Face team smoothly transitioned to incorporating Meilisearch Cloud into their model cards repository, benefiting from out-of-the-box relevancy.

**3\. Quality of support and infrastructure outsourcing**

The Hugging Face team chose Meilisearch Cloud, in part due to its dedicated support capabilities. Given that achieving highly relevant search results within the repositories is of utmost importance to Hugging Face, the decision to opt for the Cloud version was further influenced by entrusting the infrastructure to the expertise of Meilisearch Cloud specialists. Outsourcing the infrastructure allows the Hugging Face team to enhance their development speed.

Implementation
--------------

With Meilisearch already implemented for Hugging Face documentation, the expansion of the search solution to other use cases was smooth, once the ranking and internal rules were in place.

Today, the Meilisearch engine powers the discovery of 220,000 model cards, 38,000 datasets, and 60,000 demos in the Hugging Face repository. The keyword filtering mechanism has already been implemented on the Hugging Face homepage for users who are already familiar with specific model names.

However, with the implementation of Meilisearch, users are given the option to perform full-text searches as well. This search functionality operates not only on the model names and IDs but also includes the entire content of model cards.

The additional button to “Try full-text search” was implemented in order to minimize changes in the search behavior, where users are free to choose the search experience that most fits their preference.

![](https://unable-actionable-car.media.strapiapp.com/ezgif_com_video_to_gif_bce9ae142e.gif)

Vision
------

With the ongoing evolution of AI, Hugging Face anticipates a shift in search behavior and the methods people use to find the projects they need. Hugging Face is looking forward to incorporating Semantic Search into its documentation and model card searches. Currently, the platform is actively testing Meilisearch [VectorDB](/blog/langchain-semantic-search-tutorial/ as one of the potential solutions.

Hugging Face also anticipates gaining more detailed usage insights, encompassing information about users' daily search patterns and the level of activity certain model card projects receive over time.

Hugging Face facilitates AI accessibility with Meilisearch

[HitPay](https://hitpayapp.com/) is a one-stop payment fintech platform from Southeast Asia on a mission to enable small and medium businesses to accept payments easily.

**HitPay chose [Meilisearch Cloud](https://meilisearch.com.cn/cloud) to power its customers' search securely and reliably, all while maintaining a lean engineering team and staying focused on its core offering.**

<Callout>
  ___"Using Meilisearch's open-source version for proof of concept, a single developer was able to quickly implement the complete flow, including UI and backend changes. This validated our use case and confirmed that Meilisearch was the right solution for our requirements. After that, transitioning to the Cloud was effortless."__ - Nitin Muthyala, Co-Founder and CTO at HitPay_
</Callout>

Challenge
---------

As an small and medium business-focused company, HitPay strives to simplify the learning curve for small business owners who lack time to learn and adapt complex payment tools.

Merchants utilizing HitPay often have sizable catalogs and a broad customer base, requiring efficient search functionality for e-commerce and in-store purchases. With search functionality not being within HitPay's core suite of solutions, it was decided to integrate a third-party tool. While searching for a suitable solution, the HitPay team came across Meilisearch.

Why Hit Pay chose Meilisearch
-----------------------------

### 1\. Easy to integrate

Historically, HitPay has been able to maintain a lean engineering team by actively seeking out nimble and adaptive tools to streamline its developers' work. When searching for a search solution to integrate into their existing back-end system built on PHP with the Laravel framework, HitPay prioritized simplicity and compatibility. They discovered that Meilisearch offered a driver for [Laravel Scout](https://laravel.net.cn/docs/master/scout) that perfectly matched their requirements. Additionally, HitPay’s team was well-acquainted with the open-source space, and adopting an open-source search solution seemed like a logical initial step.

Nitin Muthyala, Co-Founder and CTO of HitPay, emphasized: _'We always begin with the open-source version of a solution, which has proven successful for us thus far. After testing Meilisearch's open-source solution on the internal use case, we realized how powerful it is.”_

### 2\. Developer experience

During the initial evaluation, Meilisearch was integrated into HitPay's internal dashboard, which contains a database of all its customers. The dashboard serves HitPay’s own account managers who need to search through their accounts by business owner names, business names, and affiliated emails. This presented challenges with long and repetitive names that included variations in spelling. With the efforts of a single developer, supported by helpful documentation and Meilisearch’s [out-of-the-box relevancy](https://meilisearch.com.cn/docs/learn/core_concepts/relevancy), the pilot project didn’t require many tweaks and was successfully completed within a couple of days.

Implementation
--------------

After successfully testing the open-source version of Meilisearch, HitPay made the decision to switch to the Meilisearch Cloud. A highly relevant and reliable search solution was crucial for HitPay's customers as it directly impacted their sales. To avoid the additional burden of constantly monitoring the production environment, HitPay transitioned to the Cloud version. This decision provided peace of mind, knowing that the business-critical search activity would be in safe hands at all times.

Nitin Muthyala, Co-Founder and CTO of HitPay, explained: _"Search is a fundamental piece in our production process, and HitPay couldn't afford for it to ever go down. So we opted for Meilisearch Cloud."_

During the transition, other competitors were briefly considered but eliminated early on. Meilisearch Cloud stood out as the ideal choice due to its ease of integration and the ability to check all the boxes right from the start.

Since the implementation of Meilisearch, HitPay's customers have especially appreciated the ability to search for amounts within an order, typo recognition, and the **increase in search API response speed by at least 50%** for free-text searches.

![](https://unable-actionable-car.media.strapiapp.com/Transactions_Search_gif_d6e3b9c704.jpg)

Transactions search

Results
-------

Today, Meilisearch is a valuable asset for HitPay’s customers and is integrated into two of HitPay's commerce products. The first is an **[online eCommerce shop](https://www.hitpayapp.com/online-store)**, where businesses can create their own storefronts. The second is **[HitPay's POS](https://www.hitpayapp.com/pos-software)** (point of sale), the product that allows businesses to search, catalog and **sync products across online and offline sales channels, and manage unlimited locations** for brick-and-mortar stores, where sales assistants need to **locate specific products** quickly to ensure the overall customer experience.

![](https://unable-actionable-car.media.strapiapp.com/Products_Search_gif_9639f112fe.jpg)

Meilisearch in HitPay's Point of Sale

To always keep the search relevant, HitPay offers a feature where merchants can pin or favorite specific products, which are then prioritized in search results. For customers who opt-in, HitPay sets up a workflow to automatically update the index with the most recent SKUs (Stock-keeping units) or favorited products, ensuring highly relevant search results that are always up-to-date.

Vision
------

In the future, HitPay aims to provide customizable user-facing analytics that caters to their customer's specific needs. This is particularly important for small and medium businesses, whose analytics requirements vary due to seasonal business patterns. HitPay envisions offering customization options for running analytics on any software, allowing customers to identify their most sought-after products and analyze unsuccessful searches. This aligns with Meilisearch’s product vision and HitPay's mission of powering product search for online shops and simplifying catalog searches for sales assistants in physical stores.

HitPay streamlines customers’ search capabilities while staying laser-focused on its core offering

[Qogita](https://www.qogita.com/) is a leading global wholesale B2B platform that is trying to revolutionize how products are discovered and procured. On a mission to simplify B2B trade, Qogita helps its customers generate higher margins and turnover while saving time with the help of its innovative technology.

**By partnering with Meilisearch, Qogita is able to ensure high exposure for sellers and increased relevancy for buyers, resulting in an excellent buyer-seller interaction experience.**

> _"Meilisearch has empowered us to deliver lightning-fast and highly relevant search results tailored to our customers' needs. Its seamless integration with our systems has positively impacted developer experience, making local environment setup, testing, and CI/CD automation significantly more efficient. Additionally, Meilisearch has proven to be a cost-effective solution, resulting in substantial savings for Qogita."_  
> **_\- Ivo Silva, Engineering Director at Qogita_**

![](https://unable-actionable-car.media.strapiapp.com/ezgif_com_video_to_gif_87c9670025.gif)

### Challenge

The wholesale industry of today, due to its size and maturity, remains highly traditional in terms of business practices and procurement methods. This means suppliers are hard to find, hard to work with, and often resistant to even taking on new customers.

Qogita aims to address this challenge by making it incredibly easy for sellers and buyers to connect through its platform, all in one place. By offering innovative solutions such as AI-powered pricing and cost optimization, tailoring product search capabilities to users’ actual needs, and providing an excellent buyer-seller interaction via search.

### Why Qogita chose Meilisearch

Prior to Meilisearch, Qogita used Algolia as their search provider and was satisfied in terms of functionality and performance. However, Qogita's team strove for further optimization when it came to both technology and costs. An evaluation was launched to assess alternatives, and a thorough comparison between Meilisearch, Typesense, and Algolia was performed.

The evaluation primarily focused on relevancy, ensuring that the search results were not significantly disrupted with Algolia's results serving as the baseline for comparison. Key searches were conducted for popular products and categories to determine the sorting order and overall performance. Additionally, the evaluation considered the handling of typos and common misspellings, which frequently occur in user searches.

The other factors that contributed to the choice included:  

1.  ******Cost-effectiveness******  
    All the alternatives performed well in the key areas of evaluation, but Meilisearch emerged as the preferred choice due to its cost-effectiveness. Since Qogita has a relatively lean DevOps team, selecting Meilisearch Cloud for production offered a desirable combination of affordability and provided services, including confidence in terms of infrastructure reliability and the high availability assurance.  
    
2.  ******Developer experience******  
    Throughout the entire internal proof-of-concept (PoC) process, the developer experience was a key aspect for evaluation. Meilisearch proved to be advantageous for Qogita as it provided a convenient Python library which simplified the migration process. Setting up containers on Meilisearch Cloud was also a straightforward task. Additionally, Meilisearch's vibrant and engaged open source community served as a significant advantage, establishing trust within Qogita's team early on.  
    
3.  ******Product cadence and roadmap******  
    Qogita’s team was impressed by the speed with which Meilisearch was able to achieve feature parity with the alternatives, despite being one of the youngest search solutions on the market. Additionally, Meilisearch presented a solid [roadmap](https://roadmap.meilisearch.com/tabs/1-under-consideration) that included several features aligned with the value envisioned by Qogita's team for its search initiatives in the near future.

### Implementation and moving to Meilisearch Cloud

Qogita initially self-hosted to evaluate if Meilisearch fulfilled its requirements in terms of development experience. Following the evaluation, production was migrated to Meilisearch Cloud, providing Qogita's team with peace of mind regarding infrastructure outsourcing. The transition from Algolia and the rollout of Meilisearch production went seamlessly, maintaining a high level of internal developer satisfaction.

### Results and Vision

Qogita highlighted higher developer satisfaction as one of the main benefits of choosing Meilisearch Cloud as their search provider, with ease of integration and out-of-the-box relevancy being key contributing factors.

The engineering department's goal was to ensure accurate data and product delivery, as well as providing the best user experience in connecting buyers and sellers through the platform. Meilisearch empowered Qogita team to deliver lightning-fast and highly relevant search results, tailored to meet the specific needs of their customers.

In terms of their vision for search efforts, Qogita expressed that the upcoming facet value search feature (coming to Meilisearch in v1.3) would enhance the user search experience even further. Facets allow customers to refine search results by multiple categories simultaneously. For a company like Qogita, which carries tens of thousands of brands in its catalog, facet search will enable the display and exposure of a greater number of brands to users, with further improved relevancy.

Globally, AI innovation is gaining prominence, and Qogita's team envisions a future that involves contextual search, meaningful search suggestions, and personalization. Meilisearch's upcoming [vector search release](/blog/vector-search-announcement/ marks an exciting venture into the AI space, aiming to fulfill these requirements and provide fast and relevant search for AI-powered applications.

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