Your Search Ranks Keywords. Ours Understands Intent.
Product Relevance Optimizer uses AI to rank products by what customers mean, not what they type — delivering 10-20% conversion lift without manual tuning.
Connect your catalog in under an hour. See intent-aware results on your own data. No credit card required.
Search That Fails Your Customers
Intent Lost in Keywords
Customers searching "comfortable shoes for standing all day" get results ranked by keyword frequency, not intent. They scroll. They leave. They buy somewhere else.
Manual Tuning Cannot Scale
Your merchandising team manually tunes relevance for the top 500 queries. Your catalog has 50,000 products and millions of unique query combinations. The other 99% of queries run on stale keyword matching.
Testing Takes Longer Than Learning
You run A/B tests on search relevance but spend more time configuring experiments than analyzing results. By the time you have data, the catalog has changed.
Most AI search tools expand keywords into synonyms. Product Relevance Optimizer interprets intent — it understands that "wireless earbuds for running" and "wireless earbuds for office calls" require different rankings, not different keywords.
How Product Relevance Optimizer Works
Connect Your Data
Ingest your product catalog, user behavior signals (clicks, add-to-cart, purchases), and search query data into Elasticsearch.
Connects via Elasticsearch dense vector search (kNN).LLM Interprets Intent
Each search query is interpreted semantically. The model understands what the customer means, not just the words they typed.
ELSER for semantic query encoding. LLM integration supports OpenAI, Anthropic, and open-source models.Dynamic Relevance Scoring
Relevance weights computed per query in real time. Products ranked by how well they match the customer's actual intent — not static keyword rules.
Elasticsearch Learning to Rank (LTR) extended with LLM-generated features.Measure and Improve
Built-in A/B test framework measures conversion lift with statistical significance. User behavior feedback continuously sharpens the model.
Statistical significance testing built in. Kibana reporting dashboards.
Six Capabilities That Change Search
LLM Intent Understanding
Goes beyond keyword matching. Understands that "wireless earbuds for running" and "wireless earbuds for office calls" require different rankings — and delivers them.
Dynamic Relevance Scoring
Relevance weights computed per query in real time. No static boost rules that go stale when your catalog changes.
User Behavior Feedback Loop
Click-through, add-to-cart, and purchase signals continuously train the relevance model. Search gets smarter with every interaction.
A/B Test Framework
Built-in experiment infrastructure with statistical significance testing. See the conversion lift before full rollout. No external testing tools required.
Merchandising Override Layer
Business rules and manual overrides preserved. AI relevance is the default; your merchandising team controls exceptions for specific products, categories, or promotions.
Long-Tail Query Coverage
Every query gets intent-appropriate results. Not just the top 1,000 queries your merchandising team has time to tune — every one of the millions your customers actually search.
Your Deployment, Your Way
Not SaaS. Not drop-ship. Every deployment includes engineering services.
Start with Free Trial
Connect your catalog. See intent-aware results on your own data. Upgrade to production when you're ready.
Start Free TrialExpert-Deployed Implementation
Our Elasticsearch engineers implement, tune, and validate relevance for your catalog. Custom integration with your existing search infrastructure.
Schedule 15-Minute DemoThe Business Impact
- Merchandising team manually tunes 500 queries
- 99% of queries use static keyword ranking
- Long-tail conversion: poor
- LLM handles all queries — head and long-tail
- Conversion lift across the full catalog
- Merchandising team focuses on strategy
Part of the Enterprise Search Stack
Product Relevance Optimizer sits within SquareShift's Enterprise Search accelerator portfolio. It pairs with Ticket Knowledge Base for organizations running both product search and support search. Works within the AI Search Pilot engagement for e-commerce relevance use cases. Integrates with SquareShift Atlas to monitor search quality and detect relevance degradation in production.
Customer Testimonial
"We stopped manually tuning relevance rules and our conversion improved. The AI finds what customers actually want."
Frequently Asked Questions
See Intent-Aware Search on Your Data
Connect your catalog. Measure the conversion lift. Decide with data.