UaTuAI · GEO Technology Pioneer in the COSMO Intent Era
We break down Amazon's algorithmic evolution from A9 to COSMO into actionable strategies, helping brands upgrade to being 'intent-triggered', ensuring algorithms, listings, and real demand are perfectly aligned.
55%
Recommendation Weight Increase Based on COSMO Intent Graph3.2×
Long-tail Intent Keyword Conversion Efficiency Boost72h
Alexa for Shopping Visibility Surge Time After Listing Optimization6+
Global Core Station Coverage and Multi-Language SupportA9 → COSMO: The Algorithmic Leap of Intent Commerce
Over the past decade, Amazon has moved from behavior-based A9 (clicks/sales) to intent-based COSMO (why users buy). UaTuAI uses self-developed intent engines and multimodal LLMs to bridge this gap, letting brands be truly understood by algorithms.
Behavior-Driven Algorithm
- Keywords + CTR + CVR + Sales = Primary Ranking Factors
- Only answers "what users clicked," not "why they clicked"
- Common Issue: Irrelevant results for broad terms, long-tail failures
Intent-Driven System
- Combines LLM + Knowledge Graph + Behavior Sequences
- Understand "motivation behind action," e.g., "Pregnant -> Non-slip shoes"
- Unified experience across Recommendation / Search / Navigation
Traditional Logic
"Keyword Stuffing"
Cannot explain "why it was purchased"
Intent Era
Provide Motivation & Evidence
Let algorithms deeply understand value
How UaTuAI Bridges the Gap Between A9 and COSMO?
We use multimodal LLMs to analyze images, UGC, search logs, and structured data simultaneously, generating "intent evidence chains" readable by COSMO, then feeding back into listings and ad strategies.
How UaTuAI Decodes the COSMO Core Algorithm
COSMO generates "Product-Query-Intent" triplets through four main data sources. UaTuAI's GEO LLM transforms these signals into structured semantics, ensuring algorithms read your brand story correctly.
Search Queries
Dissect search semantics to identify "why users are searching."
Competitor Links
Identify competitor associations to predict algorithmic product categorization.
Product Scanning
Deeply identify selling points, functions, and intended uses.
Reviews & Q&A
Extract "why they buy" motivations and key evidence chains.
UaTuAI Multimodal LLM Workflow
Our specialized LLM for Amazon intent scenarios is orchestrated by multi-agent synergy:
Perception Agent Aggregates Images/UGC/Structured Data
Intent Reasoner Agent Outputs Hierarchical Intent Labels
Evidence Builder Agent Generates Traceable Evidence Snippets
Intent Recognition
Extract semantic vectors of "why users search/buy" from logs and UGC, aligning with COSMO's motivation labels.
Evidence Generation
Generate "evidence snippets" after multimodal alignment (images, tables, reviews) to populate listings and ad scripts.
Ads & Recommendation Alignment
Models precipitate intent vectors into persona profiles and recommendation assets, ensuring a unified semantic core.
Search System
Understand ambiguous queries -> Restore real demand scenes. E.g., "Winter camping" automatically links to tents/sleeping bags/heaters.
Rec System
Predict next demand based on history + intent graphs, significantly boosting recommendation accuracy.
Nav System
Upgrade category trees to "Demand Trees": Camping -> Winter -> Moisture-proof pads -> High-warmth bags.
UaTuAI Intent Graph Covers 18 Categories and 15 Relations
We train our LLMs on Amazon's 18 core categories and 15 relation types, ensuring every listing's semantics map to COSMO's knowledge graph for natural, human-like recommendations.
18 Core Categories
……and more niche sectors, continuously expanding.
15 Relation Types
These relations form the "E-commerce Common Sense Graph."
UaTuAI's intent graph daily learns Amazon's new products and cross-category purchase data, with LLMs automatically highlighting "semantic gaps" to ensure strict intent asset alignment across stations and languages.
UaTuAI LLM-Driven Seller Strategies
SEO is no longer about keyword stuffing; it's about managing semantics and motivations. Our GEO LLM workflow automates these five strategies, outputting ready-to-publish content, labels, and ad lists.
Content Semantic Optimization
UaTuAI's semantic LLM scans titles, bullets, and A+ content, flagging missing scenes/motivations and generating intent-rich copy to complete your evidence chains.
Keywords = Intent Anchors
Large models output "intent anchors" based on COSMO triplets (e.g., "Kids Study Desk -> Anti-myopia / Homework / Posture"), syncing to frontend and backend keywords.
Review & Q&A Mining
"Why they buy" in UGC is COSMO's most valued signal. UaTuAI LLM extracts motivation sentences and generates "evidence snippets" for listings and ad scripts.
Three-Layer Product Matrix
Differentiate narratives for entry-level, main, and premium SKUs. Provides specific advice on photography, A+ layout, and Q&A design for each layer.
Precise Audience Targeting
Infer priority personas (Identity/Motivation/Scene/Pain Points) from listings and feedback, generating Persona feature libraries for ad and content teams.
How UaTuAI Delivers
10-Minute Delivery: Multimodal collection -> LLM Diagnosis -> Intent Tagging -> Automated Listing/A+/Persona generation.
Cyclic Combat Process: ASIN Review Every 1-2 Weeks
AI Perspective Calibration
Check how Alexa for Shopping/COSMO currently describes your product and audience. Adjust titles and bullets if there's a deviation.
Semantic Detail Checkup
Verify if selling points, specs, and scenes are fully covered. Ensure keywords and Q&A align with high-frequency user intents.
Product Persona Differentiation
Ensure your product doesn't blend in with competitors. Generate differentiating evidence to prevent being buried by algorithms.
Fundamental Research Support
All diagnostic indicators are derived from public Amazon/Google research and UaTuAI's proprietary validated corpora.
ECOMSCRIPTBENCH · Amazon Research
Citing the Script Planning model, validating that search failure rates reach 68% when tasks are complex. UaTuAI transforms this into actionable metrics like intent thresholds and semantic gap indices.
Aligning LLMs with Implicit Preferences
Extracting 24 million intent tags from 3.7 million reviews to support UGC preference mining and scene-based reinforcement, ensuring content resonates with user micro-needs.