E-Commerce Intelligence
Semantic search intelligence across the entire query pipeline, from keystroke to conversion, at the latency shoppers expect and a fraction of the cost.
Seven specialist models bring semantic understanding to every stage of e-commerce operations: search expansion, rescue, re-ranking, intent classification, catalog enrichment, visual tag validation, and fulfillment QC. Text models fine-tuned via LEAP Workbench, vision models via LFM2.5-VL. At 10M queries per day, the entire pipeline costs what a single cloud API endpoint would.
7 specialist models
How It Works
One specialist model per e-commerce task,
from search to catalog to fulfillment
Semantic Expansion at the Speed Shoppers Expect
'Cozy blanket' should match 'fleece throw,' but keyword search cannot make that connection. Cloud language models expand queries well but at 200-500ms, visible in autocomplete. A specialist LFM expands vague queries into ten or more rich semantic terms in 45ms. Keyword-engine speed with deep semantic understanding. Fine-tune for your catalog vocabulary via LEAP: seasonal terms, brand synonyms, category-specific expansions, deployed same-day. Orders of magnitude cheaper than cloud LLMs at scale.
Zero Results Is Zero Revenue. Rescued Before the Page Renders
Ten to fifteen percent of searches return empty pages, each one a lost conversion that no keyword engine has a built-in mechanism to fix. A specialist LFM detects zero-result conditions and generates alternative queries via broadening, synonyms, and constraint relaxation before the empty page renders. The shopper sees products, not apologies. New rescue strategies for seasonal inventory or catalog changes deploy via LEAP in minutes. Every rescued search is recovered revenue.
Your Search Returns the Right Products in the Wrong Order
Position one has 20%+ higher click-through than position three. Keyword scoring cannot understand that 'gift for a coffee lover' means pour-over sets outrank mugs. A specialist LFM re-ranks fifty results by semantic relevance in 45ms with no separate vector database or embedding infrastructure. Fine-tune ranking signals for your merchandising strategy. The model learns your catalog's semantic relationships, not generic web patterns. One call, one model, fractions of a cent.
Every Query Understood and Routed to the Right Strategy
Does 'apple' mean a laptop or a fruit? Without intent classification, every query hits the same retrieval path. Heuristic rules do not scale. Cloud NLU takes 200-500ms. A specialist LFM classifies intent and resolves ambiguity in 45ms, routing each query to the optimal retrieval strategy before it reaches the index. New intent categories (seasonal, promotional, emerging product lines) deploy via LEAP in minutes. The search engine finally understands what the shopper means.
Invisible Products Cannot Sell. Structure the Unstructured
Marketplace sellers submit sparse listings: 'Blue shirt, comfortable fit' with no material, size, or style tags. These products vanish from filtered search. Manual enrichment is expensive. Cloud language models cost tens of thousands monthly at catalog scale. A specialist LFM extracts structured attributes from unstructured descriptions and enriches ten million listings overnight at a fraction of the cost. New attribute taxonomies deploy via LEAP as your catalog evolves.
Every Supplier Tag Validated Against the Product Image
Suppliers submit millions of product listings with incorrect tags: wrong colors, materials, categories. Manual QA catches a fraction. Cloud vision APIs like GPT-4o cost $60K to scan 20M products. LFM2.5-VL validates every supplier tag against the actual product image in under 80ms at $0.0003 per image — 10x cheaper than GPT-4o Vision. Correct colors, materials, and categories across your entire catalog overnight. Data never leaves your infrastructure.
Package Quality Inspected at Conveyor Speed
Fulfillment centers process millions of packages daily. Manual inspection at conveyor speed is error-prone and expensive. LFM2.5-VL inspects every package in under 80ms — fast enough for high-speed conveyor belts. Torn corners, crushed boxes, damaged labels, and loose tape are flagged with severity levels before the package reaches the loading dock. Real-time defect rates and throughput metrics give warehouse managers instant visibility.
Try each model
All Demos
Query Expansion
Transform vague queries into rich semantic search terms in under 50ms
'cozy blanket' → 10+ semantic terms in 45ms. Search autocomplete that cloud LLMs can't serve
Zero-Result Rescue
Recover failed searches with intelligent rewrites before the user sees an empty page
10-15% of searches return zero results, each one a lost conversion rescued in under 50ms
Semantic Re-Ranking
Re-order search results by semantic relevance. Put the right product in position 1
Re-rank 50 results in 45ms. Your search returns relevant products, just in the wrong order
Search Intent Classification
Classify query intent and disambiguate ambiguous searches in real-time
Does your search know what the user means? Classify intent + disambiguate in under 45ms
Product Attribute Extraction
Extract structured attributes from unstructured product descriptions at catalog scale
2M SKUs with missing attributes = 2M invisible products. Enrich your entire catalog at a fraction of cloud cost
Catalog Tag Validator
Validate supplier product tags against images using vision-language intelligence
GPT-4o Vision costs $0.003/image. LFM2.5-VL self-hosted costs $0.0003/image — 10x cheaper at catalog scale
Fulfillment QC
Real-time package quality inspection for fulfillment centers using vision AI
At <80ms per package, LFM2.5-VL inspects faster than conveyor belt speed with camera-based QC
Ready to deploy in your environment?