Bike Totaal drives in-store conversions with Algolia AI Search
Dynamo Retail Group's Elasticsearch-based search delivered slow, irrelevant results — a search for a 'black bike' surfaced a black bag first — and required external developers for even small configuration changes, while the customer base was shifting to more digitally-savvy users.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Product data sync via Alumio
Data is pushed to Algolia via APIs from Alumio's low-code integration platform.
After switching to Algolia, Dynamo saw increases in search usage, click-through rates, and conversions from search, received overwhelmingly positive feedback from customers and store owners, and can now manage all search and discovery in-house without external intervention.
What failed first
The existing Elasticsearch-based search engine produced irrelevant results and required external developers for every small change, making it impossible for the ecommerce team to iterate independently.