[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://www.meilisearch.com/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://www.meilisearch.com/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://www.meilisearch.com/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/?utm_campaign=release-v1-8&utm_source=blog) and participating in our [product discussions](https://github.com/meilisearch/product/discussions?ref=blog.meilisearch.com).

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

Meilisearch is a partner of choice for OCTO Technology.

[Symfony](https://symfony.com/) 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/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/) 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/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/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/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/blog/migrating-symfony-com-search-engine-to-meilisearch)**.**  
  
Ready to optimize your documentation search? [Start a free trial](https://www.meilisearch.com/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](http://hf.co) - 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://huggingface.co/models) catering to a variety of [machine-learning tasks](https://huggingface.co/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://huggingface.co/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](__GHOST_URL__/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/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.com/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://www.meilisearch.com/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](__GHOST_URL__/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.

Qogita simplifies B2B trade with Meilisearch Cloud

[Minipouce.fr](https://www.minipouce.fr/) is an online platform that enables future parents to create a baby registry list, which is a list of items that parents-to-be would like to receive as gifts. With the help of a multi-brand catalog featuring over 150,000 products, Minipouce.fr aims to make navigation intuitive and incredibly smooth for its users, ensuring everything is easy to find.

**By opting for Meilisearch as the search backbone of its marketplace, Minipouce.fr is able to maintain its commitment to delivering the best possible user experience.**

![](https://unable-actionable-car.media.strapiapp.com/gif_minipouce_gif_bf2cb4bae1.jpg)

<Callout>
  __"As a user, it felt like magic compared to other solutions I've tried. I'm not sure whether it's related to how typo tolerance works or to the default configuration. Still, right after setting up Meilisearch with no particular tweaks, I got fantastically relevant results." - Anthony Catel, Co-founder of__ [__Minipouce.__](https://www.minipouce.fr/)
</Callout>

### Challenge

As parents themselves, the cofounders of Minipouce.fr were very familiar with the struggles faced by new parents. It's crucial to create a multi-brand baby registry checklist to enable friends and relatives to shop from various distributors in a single location, rather than being confined to one sole provider.

Launching a new marketplace is a complex task: you need to generate enough traffic to convince distributors to list their products in the first place. On the other hand, you need a diverse and extensive catalog in order to attract customers and drive that traffic.

For Anthony Catel, the co-founder and head of engineering, it was clear that Minipouce.fr needed to offer a competitive and high-quality user experience right from the beginning. It is important to enhance the search functionality for a platform like Minipouce.fr: more relevant search results leads to increased user purchases, which in turn allow Minipouce.fr to collect an affiliate percentage fee from every product purchase originating from its platform. To achieve this, the technology behind the platform needed to be top-notch and enable quicker and smoother product selection by incorporating layers of meaning to accommodate various search terms users might use.

To add to the complexity, each retailer tends to use different formats for its product catalog, which results in mismatched and missing information that Minipouce.fr then needs to address. Synonyms also play a crucial role as one product can have multiple names, and customers may only be familiar with one of them. Without the search accounting for all potential synonyms, users may not find the right products.

All such intricacies needed to be addressed with the help of the most appropriate search solution.

### Why Minipouce.fr chose Meilisearch  

1.  **Higher out-of-the-box relevancy**

At the very beginning, Minipouce.fr considered using Algolia but couldn't find an appropriate plan that would accommodate a young, rapidly growing platform. While searching for an alternative, Anthony discovered Typesense and conducted extensive testing on it. After an initial set of tests, he decided to continue searching for another solution that could offer higher relevance in search results compared to Typesense. Eventually, Minipouce.fr came across Meilisearch and found that its default configuration demonstrated **higher relevancy in search results** than Typesense during the testing phase, without requiring any additional tweaks.

2\. **Lower maintenance**

Anthony chose not to consider ElasticSearch or similar solutions due to his desire to avoid managing numerous Java dependencies. Furthermore, he was drawn to Meilisearch's open-source platform, which operates on **Rust, a modern language in comparison to the C++** predominantly used by its competitors. This choice resulted in less time spent on debugging and infrastructure maintenance, a crucial factor for a smaller-sized team.

3\. **Meilisearch’s support and services bolster its open-source offering**

With high business stakes and the quality of search results being central, Minipouce.fr sought a solid, well-maintained solution. With Meilisearch’s open source engine, the organized handling of tickets and commits hinted at an established business backing it.

### Implementation

Today, Minipouce.fr is using Meilisearch to fully power the entire website. The migration of the marketplace from Typesense to Meilisearch's offering was completed within half a day, making it a quick and efficient process. Anthony particularly appreciated the availability of different SDKs in multiple programming languages, including a Python SDK. This feature made Meilisearch a natural addition to his tech stack, as it facilitated the setup of the platform without requiring additional resources.

To further optimize the search experience, additional tweaks are implemented using Meilisearch. This includes sorting functions that prioritize certain items in the catalog based on popularity. Updates are pushed in real-time, ensuring the search results are always up to date.

### Results

[Minipouce.fr](https://www.minipouce.fr/) was delighted with the **seamless integration and low maintenance requirements of Meilisearch**, thanks to its default configuration working effectively from the start. The relevancy of the search results was impressive, surpassing the performance of other tested alternative solutions. On top of that, since the implementation of Meilisearch, Minipouce.fr has observed a 5x growth in search volume across its platform.  

Looking ahead, Minipouce.fr envisions even greater possibilities, such as the implementation of vector search, which would enable intelligent connections between searches, further enhancing search experience. Considering the future, Minipouce sees the potential benefit of migrating to Meilisearch's Cloud offering to outsource the infrastructure maintenance, allowing for greater efficiency and scalability.

Minipouce.fr provides a top-grade customer experience with Meilisearch's open-source offering

[Bookshop.org](https://bookshop.org/) is an online bookstore based in Brooklyn, New York, with a mission to support local, independent bookstores financially. By design, Bookshop.org gives away over 80% of its profit margin to stores and affiliates of stores: publications, authors, and others who make up the thriving, inspirational culture around books. As more and more people buy their books online, Bookshop.org creates an easy, convenient way for customers to get books and support local bookstores at the same time.

**By partnering with Meilisearch Cloud, the Bookshop.org team can enjoy improved search relevancy, reduce maintenance and operational costs and have its engineering team focus on its core business.**

[![](https://unable-actionable-car.media.strapiapp.com/bookshop_meilisearch_ded1e7997a.gif)](https://bookshop.org/)

The search experience on Bookshop.org

<Callout>
  _"With our prior search engine (Elasticsearch), **14% of searches resulted in a purchase, and with Meilisearch, 20% of searches currently result in a purchase**. That's a huge improvement without a lot of development time: **1 out of 5 customers found the book they were looking for and purchased it.**"_  
\- _Andy Hunter, CEO at _Bookshop.org__
</Callout>

Challenge
---------

Bookshop.org carries an impressive inventory of over **six million books**. It’s essential for the customer experience that anyone searching for a specific book on the website can find it as fast as possible. A considerable number of books available in the inventory means a proportionally high amount of items that need to be **indexed and ranked in the search** by the internal team.

> You only get one shot at it. We have a lot of visitors that care enough about their communities and ethical consumption - those buyers are specifically choosing to use Bookshop, and **we have to make sure that they can find a book as easily as they could find it in other major bookstores or else we're gonna lose them**.

_\- Mason Stewart, CTO at Bookshop.org_

Prior to switching to Meilisearch, Bookshop.org was utilizing a search engine solution provided by [Elasticsearch](__GHOST_URL__/why-should-you-use-meilisearch-over-elasticsearch?utm_campaign=bookshop_story&utm_source=blog). However, with a small team of eight engineers and many unique customers, Bookshop.org needed **a solution that would require less maintenance and fine-tuning**. Additionally, a more streamlined solution would enable workers across the company to contribute to the ranking rules and configurations, improving the search relevancy on a rolling basis.

Why Bookshop.org chose Meilisearch
----------------------------------

**1\. A streamlined process that keeps the decision-makers involved**

For the Bookshop.org team, the advantage of Meilisearch lies in its simplicity and ease of use, making it possible **for decision-makers and non-developers to be very close to the ranking rules**. This was necessary to accommodate the ever-changing book search criteria during the busy holiday period and keep the search results relevant at all times.

> ___The fact that we can iterate back and forth quickly and have speedy re-indexing times when we change our rules was a new way of working with search. Previously, only our engineers had the expertise to tweak the rules for book search.___

___\-_ Mason Stewart, CTO at Bookshop.org__

**2\. Quick iterative product cycle**

The Bookshop.org team also highlighted the quick iterative cycle that has enabled them to solve issues faster. Bookshop's team can directly interact with the Meilisearch team whenever they need to resolve issues or introduce quick fixes. According to CTO Mason Stewart, the company values the combined agility of a compact search engine backed up by a highly responsive team.  
  
**3\. Ability to outsource hosting and infrastructure maintenance to the Meilisearch team**

Ensuring stability in search is essential for the success of the Bookshop.org business. After meeting and interacting with the Meilisearch team, the CTO felt confident in **entrusting them to manage their Meilisearch servers** through the Cloud offer.

> ___Initial value came from the fact that Bookshop started using the Meilisearch open source solution, hosting the infrastructure locally. However, working with it in staging environments and test environments was a harder task for a small team like ours that can't focus on search year-round. We did eventually switch to Meilisearch Cloud because it was better for us to outsource.___

_\- Mason Stewart, CTO at Bookshop.org_

By transitioning from the open-source version of Meilisearch to Meilisearch Cloud, the Bookshop.org team can now concentrate on its primary operations while delegating the management of the search infrastructure to experts.

**4\. Ability to support the scale and business model fit**

The Bookshop.org’s extensive inventory, coupled with a high amount of monthly users, made some pricing models that exist on the market untenable for the bookstore’s distinct business model. For a business with low margins, very high volume searches, very high volume users, and a high number of documents, Meilisearch proved to be a perfect fit.

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

After reassessing its main challenges with book search, the Bookshop.org team conducted research and evaluated alternative solutions to address their need for a more user-friendly search solution that requires less maintenance. They came across Meilisearch, which was recommended on a popular tech news discussion platform. A quick test environment was set up out of the box, and the team found that Meilisearch was a good fit as the test continued. The CTO and engineers presented the working prototype to the management, and it was decided to implement Meilisearch before the holiday season, which typically starts at the end of the year. **The timing was crucia**l, as the end-of-year seasonal influx in sales brings a lot of increased consumer activity and a significant portion of profits for a bookseller as a result of it.

The initial demo included connecting Meilisearch to a book catalog and was quick and easy. Then followed the process of balancing and ranking the data that should inform the quality of a result internally, weighing different factors such as how recently the book was sold or how long ago it was published. The team acknowledged that the transition to Meilisearch would have been seamless if a clean data catalog had existed beforehand.

The process of changing the ranking rules to keep the book search relevant for shoppers is very streamlined. Suppose they want to rank a book's description higher or lower in the ranking rules. All they need to do is move it up or down, reindexing the entire catalog and making any change easy. Having global ranking rules that apply to the entire database proved healthy for a small team that needs to understand how one change can impact search across the repository.

> ___After a few weeks of tuning the rules, we've introduced almost no changes. It's been just about perfect.___

___\-_ Mason Stewart, CTO at Bookshop.org__

To add to the vision of the future with Meilisearch, the Bookshop.org team mentioned that having a multi-index search would be the next most important thing to improve the search experience of the website visitors. Say, a website visitor searches for a specific _keyword_ in a search bar. Multi-index search allows the engine to perform concurrent searches across multiple sources. To that extent, Meilisearch is happy to announce the release of the [federated search](__GHOST_URL__/introducing-federated-search?utm_campaign=bookshop_story&utm_source=blog) in v1.1 as an additional step in boosting user experience and increasing relevancy for Bookshop.org clients.

Results
-------

With an initial Meilisearch deployment, the team could compare the search performance to the metrics they were getting with a previous Elasticsearch configuration.

By bringing Meilisearch to the table, the team introduced a straightforward configuration process, removing the need to increase the team’s bandwidth or expertise. **With Elasticsearch, 14% of searches resulted in a purchase. With Meilisearch, that number jumped to 20%,  increasing overall conversion by 43%**.