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Zoomin is an innovative software company and a leader in the product content experience space. Their platform automatically delivers product content to end-users wherever they look for it, all within a unified, personalized and intuitive experience. With enterprises such as Commscope, National Instruments and Sony Playstation using their products, their service must continue to evolve to meet huge customer demand.
“Search and findability is a key aspect of the Zoomin product,”
says Rafi Bryl, the Director of Product Management at Zoomin.
Underpinning the complex and powerful Zoomin product is a search engine built on Elasticsearch. The original search functionality was built on an early version of Elasticsearch, which needed upgrading.
“We realized that to meet our goals of scaling the sophistication of our AI and customer experience, we needed to invest in advancing our infrastructure,”
Rafi noted.
Zoomin was also keen to have an iterative approach to the upgrade, where an entirely new platform would be created using elements from the original platform, but with lots of improvements and upgrades.
Zoomin engaged BigData Boutique to execute these improvements given the positive references they received and, importantly, the flexibility they were able to provide.
Instead of presenting a fixed price, or price per day, BigData Boutique provided Zoomin with a bank of hours that they could draw upon for all work, ensuring that the project wasn’t limited in scope.
“We were confident we were bringing in experts who could help Zoomin modernize and stay best in breed”.
When approaching the engagement, both BigData Boutique and Zoomin had a number of tough architectural and design decisions to make.
“One of the biggest challenges we faced was deciding whether we build something entirely new, or use the old platform”
remarked a member of the Zoomin tech team
The Zoomin search functionality needed modernizing on the latest version of Elasticsearch, but the team was keen to see if any original features could be carried forward from the original platform.
“With the help of Bigdata Boutique, we preserved the basic schema and underlying architecture, but we took fundamental design decisions that significantly improved the existing platform.”
shared a member of Zoomin’s product team. The process of working out what to retain and what to change was in itself complex, and BigData Boutique’s experience in similar projects was key.
One critical requirement was to ensure that the engine’s use of Elasticsearch produced highly relevant and tailored results, a practice also known as search relevance engineering. Given the nature of Zoomin’s existing search platform, we used Big Data Boutique’s unique toolset and expertise to optimize results to the most common queries. This task would encompass both the legacy search engine, and any new work carried out.
During the collaboration process between Zoomin and BigData
Boutique, it became clear that the existing platform needed to be
stabilized for current customers, and that the new search platform
would be introduced in parallel. This would give Zoomin the
opportunity to migrate its customers gradually. Crucially, Zoomin
needed to ensure that the new platform rollout did not negatively
impact Zoomin’s customers who were still using the legacy search
platform. This would require complex integrations to address
interoperability between new features on the new platform and users
of the existing platform.
It became clear during the engagement that the new platform needed
to handle many different natural languages in a fundamentally
different way to Zoomin’s original search engine. As linguistic
processing and complex search technology are pillars of BigData
Boutique, the team was able to provide invaluable guidance on how to
best build the solution, before starting development. This approach
would allow the Zoomin platform to expand into multilingual
processing, future proofing their investment and the solution.
BigData Boutique was able to deliver the new platform, live in production, despite several operational setbacks.
“After they brought in the expertise to make the architectural decisions, they then wrote the code to our timeframes and exactly as we needed.”
Rafi states.
Whilst the global pandemic was changing timelines across the world, it didn’t hamper BigData Boutique’s delivery of this complex engagement.
“The whole project was executed as we desired and to a very high standard, even though we never met anyone from BigData Boutique face-to-face”, notes Rafi.
The ability to deliver the entire project, within the timeline and as requested without having the traditional consultancy facetime, was critical in keeping up with Zoomin’s organizational roadmap.
The new platform has full backward compatibility with the previous version, ensuring that customers can have a seamless migration to the new engine at a time of their choosing.
“Achieving functional parity between the two platforms was one of the main focuses for us, and the way in which it was done was really quite sophisticated”, says a member of the technical team.
BigData Boutique further enhanced the platform to improve multilingual content search functionality; a feature that would be key as Zoomin’s customer base continued to grow and diversify
On top of the backward compatibility, BigData Boutique’s work was crucial in introducing several new features.
Whilst Zoomin already had a boolean search capability, the Elasticsearch upgrades and architectural changes made by BigData Boutique enabled the introduction of more complex search terms. For example, the new platform has a “not” operator, allowing users to exclude content based on particular keywords.
The refinement of the curated search functionality is a real force multiplier for the new Zoomin platform, made possible by BigData Boutique’s development work. It takes any randomness out of search results, allowing users to specify what sorts of results, in what order, they would like returned for any given query.
Whilst not yet live, BigData Boutique facilitated the rebuild of Zoomin’s existing auto-suggest function. This complex machine learning-derived feature is critical for customer experience and allows for more seamless navigation of the platform. Although small, autosuggest functionality is exceptionally difficult to engineer, and even more so with the amount of text that Zoomin’s customer base is supplying. BigData Boutique were able to use machine learning to enhance the performance and relevance of the autosuggest and autocomplete features, whilst adding a data pipeline for continuous improvement.
The search relevance requirements presented a unique challenge, as significant effort was needed resolving tokenization issues that existed in the legacy search engine. BigData Boutique also used their expertise to carry out extensive field boosting, as well as adding synonyms and other relevance related rules. As, in search technology, those rules are often dynamically altering and developing, it was a substantial effort to guarantee the platform’s longevity. All of that was achieved with the help of unique methodology and bleeding edge tooling developed by BigData Boutique over the years.
“We’ll absolutely maintain a relationship with BigData Boutique,”
says Rafi.
Moving forward, BigDataBoutique and Zoomin will continue to collaborate as the new platform is rolled out to more customers. BigData Boutique will continue to play a role in new feature development at Zoomin, all while knowledge transfer takes place.
There are plans for BigData Boutique to continue consulting at Zoomin across a range of different areas. There have been obvious successes in the new platform development and launch, and this is something that both parties are keen to continue.
“We found the relationship very useful - BigData Boutique was helping us solve production issues on the old platform whilst developing the new platform simultaneously, even if it wasn’t in their remit”.