BigData Boutique was able to deliver the new platform, live in production, despite several
“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 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
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 improvement.
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.