Ecommerce ops · Production

Nosto Semantic AI: NLP, LLM, and vector search for on-site commerce search experiences

The problem

Brands need to bridge the gap between natural human query language and machine search understanding, while handling typos, long-tail queries, and zero-result pages that reduce conversions and revenue.

Workflow diagram · grounded in source
1
User types search query
trigger
“With intelligent auto-suggestions as users type their queries, dynamic filtering options, and tolerance for typo errors”
2
Query normalization and parsing
ai_action
“Semantic AI analyzes the customer's query, breaking it down into its constituent parts, and matches it against all product textual fields, including titles, categories, attributes, tags, and descriptions”
3
Vector search semantic matching
ai_action
“creating semantic-based vectors. It extends beyond keyword-based search by finding similar data using approximate nearest neighbor (ANN) algorithms on the vectors. This gives faster, more accurate results even for complex, long-tail queries”
4
Merchandising reranking
ai_action
“incorporating a merchandising layer that reorders results based on product performance metrics, attributes, and user preferences, you can align with your business goals”
5
Relevant results delivered
output
“users get a relevant search experience, even for complex queries, akin to what they are accustomed to with popular search engines”
6
UGC sentiment detection
ai_action
“Semantic AI identifies negative sentiment in user-generated content and automatically deletes inappropriate posts, reducing the need for manual curation and the risk of human error”
7
Behavioral synonym learning
feedback_loop
“Nosto's AI generates synonym suggestions through behavioral data analysis, identifying patterns. It links search queries with purchased products to create synonym pairs, ranked by frequency”
Reported outcome

Semantic AI delivers highly relevant on-site search results for complex queries, automates UGC curation through sentiment detection, and reduces engineering expenses compared to in-house search development.

Reported metrics
Revenue impact from searchsignificant impact on revenue
UGC curation time investmentminimal time investment
Reported stack
NLPLLMANNHuginn
Source
https://www.nosto.com/commerce-experience-platform/artificial-intelligence/semantic-ai/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Semantic AI delivers highly relevant on-site search results for complex queries, automates UGC curation through sentiment detection, and reduces engineering expenses compared to in-house search development.

What tools did this team use?

NLP, LLM, ANN, Huginn.

What results were reported?

Revenue impact from search: significant impact on revenue; UGC curation time investment: minimal time investment (source-reported, not independently verified).

How is this ecommerce ops AI workflow structured?

User types search query → Query normalization and parsing → Vector search semantic matching → Merchandising reranking → Relevant results delivered → UGC sentiment detection → Behavioral synonym learning.

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