Pillar
Intelligent Product Discovery
Search that understands what your customers mean, not just what they type. AI-driven product discovery that turns browsing into buying.
The Problem
Ecommerce search is still stuck in 2010
Up to 30% of ecommerce visitorsuse site search. They convert at 2-3x the rate of browsers. Yet most stores run keyword matchers that haven't fundamentally changed since Solr was deployed in 2008.
Keyword dependency
"Athletic footwear" returns nothing because your catalog says "running shoes." Customers shouldn't need to guess your taxonomy.
Zero-result dead ends
12% of searches return nothing. Each one is a customer walking out of your store. Most retailers don't even monitor this metric.
Static merchandising
Category pages display the same grid for every visitor. A power buyer and a first-time browser see identical layouts.
Discovery friction
Customers know what they want but can't describe it in your system's language. Visual intent and contextual signals go completely unused.
How It Works
Four layers of intelligent discovery
Semantic Search
Your customer types "breathable summer dress for a beach wedding" and actually finds one. Our search engine parses intent, occasion, material preference, and context — not just keywords. It understands synonyms, colloquialisms, and even misspellings, mapping natural language to your product catalog with vector-based retrieval.
Built on transformer embeddings fine-tuned for commerce. Handles long-tail queries that keyword search ignores entirely.
Visual Search
A customer screenshots a pair of shoes from Instagram. They upload it. Within 200ms, they see every matching and visually similar product in your store — ranked by similarity, availability, and price. No tags needed. No manual curation.
Uses multi-modal vision models to extract color, pattern, shape, and style. Works across categories — furniture, fashion, electronics, beauty.
Contextual Recommendations
Not "people who bought X also bought Y." That approach peaked in 2012. Our recommendations factor in the customer's current browsing session, time of day, device, cart contents, and past returns — not just purchase history. The result: suggestions that feel uncanny, not spammy.
Session-aware inference runs in real time. Recommendations update with every click, scroll, and hover signal.
AI-Curated Storefronts
Your "New Arrivals" page looks different for every visitor. A returning customer who buys menswear sees menswear first. A first-time visitor from a Pinterest ad for candles sees home goods. Category pages, collection pages, and landing pages all reorganize dynamically based on individual signals.
Merchandising rules still apply — you set the guardrails, the AI handles the arrangement within them.
Results
The impact of getting discovery right
3x
Higher conversion rate
from search-to-purchase
40%
Fewer zero-result searches
queries that used to dead-end
60%
More product engagement
clicks, saves, and add-to-carts
2x
Average order value
through contextual cross-sell
Ready to transform product discovery?
Stop losing customers to broken search. See how Octavium's intelligent discovery engine can turn your catalog into a competitive advantage.
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