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.”
Whilst the global pandemic was changing timelines across the world,
it didn’t hamper BigData Boutique’s delivery of this complex
“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.
Complex Boolean Search
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
Search Relevance Engineering
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.