Ecommerce ops · Production

How Algolia helped flaconi transform its search experience at MACH speed

The problem

Flaconi's legacy monolithic e-commerce platform was developed and managed entirely in-house, consuming developer time on maintenance rather than differentiation, and lacking the flexibility to quickly adapt to new industry developments or add personalization features.

Workflow diagram · grounded in source

Re-platform decision

trigger

“In 2020, to better meet its business and stakeholder goals, flaconi decided it needed to re-platform its online store”

MACH architecture migration

integration

“migrating from its monolithic architecture to one using MACH (microservices-based, API-first, cloud-native, and headless) principles”

Algolia search deployed site-wide

integration

“The company implemented Algolia Search across its site, including all product listing pages, categories, brands and sales, and promotion pages. Also, all filters across the site use Algolia Facets & Filters. The initial implementation of…”

Business user self-serve configuration

output

“Algolia's user-friendly backoffice dashboard provides additional features such as pinning products, defining rules, and running A/B tests, this empowers our business users to test their hypotheses without the need for engineering support”

AI-powered dynamic re-ranking

ai_action

“category management uses Algolia Dynamic Re-Ranking to pin and boost products based on stock levels and supplier contracts”

Recommend on cart page

ai_action

“The company has recently begun to experiment with Algolia Recommend on its cart page to share frequent recommendations”

Reported outcome

After migrating to a MACH architecture with Algolia, flaconi reduced developer dependency for search tasks, empowered business teams to self-serve configuration without engineering support, and improved product discovery and customer experience.

Reported metrics

Developer dependencyReduced dependency on developer time

Business team ease of useEase to use and update for business teams

Product discovery and salesImproved product discovery for increased sales

Performance and customer experienceIncreased performance and better customer experience

Show all 5 reported metrics

developer dependencyReduced dependency on developer time

business team ease of useEase to use and update for business teams

product discovery and salesImproved product discovery for increased sales

performance and customer experienceIncreased performance and better customer experience

search implementation timebetween two and four weeks

Reported stack

AlgoliaAlgolia RecommendAlgolia Dynamic Re-RankingAlgolia Facets & FilterscommercetoolsContentful

Source

https://www.algolia.com/customers/flaconi

Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

After migrating to a MACH architecture with Algolia, flaconi reduced developer dependency for search tasks, empowered business teams to self-serve configuration without engineering support, and improved product discov…

What tools did this team use?

Algolia, Algolia Recommend, Algolia Dynamic Re-Ranking, Algolia Facets & Filters, commercetools, Contentful.

What results were reported?

Developer dependency: Reduced dependency on developer time; Business team ease of use: Ease to use and update for business teams; Product discovery and sales: Improved product discovery for increased sales; Performance and customer experience: Increased performance and better customer experience (source-reported, not independently verified).

How is this ecommerce ops AI workflow structured?

Re-platform decision → MACH architecture migration → Algolia search deployed site-wide → Business user self-serve configuration → AI-powered dynamic re-ranking → Recommend on cart page.