Skip to main content

Overview

This guide provides technical overview and core concepts for integrating AdMesh AI-powered product recommendation capabilities into enterprise applications.

What is AdMesh?​

AdMesh is an enterprise recommendation engine designed for AI applications, conversational interfaces, and modern web platforms. The system uses machine learning algorithms to analyze user intent and deliver contextually relevant product recommendations.

Core Concepts​

Intent Detection​

AdMesh analyzes user queries to categorize intent types:

  • compare_products - Comparative analysis requests
  • best_for_use_case - Scenario-specific recommendations
  • trial_demo - Product evaluation inquiries
  • budget_conscious - Cost-optimized suggestions

Semantic Matching​

The recommendation engine implements:

  • Text embeddings using OpenAI's text-embedding-3-small model
  • Cosine similarity algorithms for semantic matching
  • Trust scores for quality assurance
  • Keyword matching for precision targeting

Recommendation Scoring​

Each recommendation provides:

  • Intent match score (0-1) - Query relevance measurement
  • Trust score - Quality and reliability metrics
  • Reason - AI-generated recommendation rationale

Architecture Overview​

SDK Ecosystem​

Backend SDKs​

  • Python SDK - For AI applications, data processing, and backend services
  • TypeScript SDK - For Node.js applications and serverless functions

Frontend SDK​

  • UI SDK - React components for displaying recommendations with built-in tracking

Integration Patterns​

AI Assistant Integration​

Implementation for conversational interfaces and AI assistants:

# Intent detection and recommendation retrieval
response = client.recommend.get_recommendations(
    query="Enterprise CRM solution requirements",
    format="auto"
)

# Process recommendations for chat interface
for rec in response.response.recommendations:
    print(f"Recommendation: {rec.title} - {rec.reason}")

E-commerce Integration​

Product discovery enhancement for e-commerce platforms:

// User behavior-based recommendations
const recommendations = await client.recommend.getRecommendations({
  query: userQuery,
  format: 'auto'
});

// UI component integration
<AdMeshLayout recommendations={recommendations} />

Content-Based Integration​

Contextual product recommendations for content platforms:

// Citation-based recommendation display
<AdMeshCitationUnit
  recommendations={recommendations}
  conversationText="For project management solutions..."
  citationStyle="numbered"
/>

Key Features​

AI-First Architecture​

  • Purpose-built for AI applications
  • Advanced intent detection algorithms
  • Contextual analysis capabilities
  • Natural language processing integration

UI Component Library​

  • Production-ready React components
  • Citation-based conversational interfaces
  • Floating chat widget implementations
  • Sidebar component options
  • Automated recommendation widgets

Analytics and Tracking​

  • Automated view tracking
  • Click-through rate monitoring
  • Conversion attribution
  • Revenue analytics

Customization Options​

  • Light and dark theme support
  • Custom accent color configuration
  • Responsive design implementation
  • Accessibility compliance

Revenue & Monetization​

Calculate Your Potential Earnings

Estimate your revenue potential with AdMesh's AI-powered recommendations. Our earnings calculator helps you understand the monetization opportunities based on your platform's traffic and user engagement.

AdMesh provides revenue sharing for AI platforms through:

  • Performance-based payouts - Earn from successful recommendations
  • Tiered revenue sharing - Higher percentages for Pro and Enterprise plans
  • Real-time analytics - Track clicks, conversions, and earnings
  • Transparent reporting - Detailed revenue breakdowns and insights

Implementation Checklist​

  • Register account at useadmesh.com/agent
  • Obtain API credentials from dashboard
  • Select appropriate SDK (Python or UI)
  • Install SDK in development environment
  • Execute initial API integration
  • Implement recommendation display
  • Configure tracking and analytics
  • Calculate earnings potential - Use the earnings calculator to estimate revenue

Next Steps​

  1. Configure API Authentication - Set up credentials
  2. Quick Start Implementation - Execute first API call
  3. SDK Selection:

Use Cases​

AI Conversational Interfaces​

Product recommendation integration for conversational systems:

  • Customer support automation
  • Shopping assistance platforms
  • Business advisory systems

E-commerce Platforms​

Product discovery and conversion optimization:

  • Recommendation engine implementation
  • Search result enhancement
  • Personalized user experiences

Content Platforms​

Contextual product suggestion integration:

  • Editorial content recommendations
  • Tutorial tool suggestions
  • Review platform integrations

SaaS Applications​

Tool discovery and optimization:

  • Workflow optimization recommendations
  • Integration suggestions
  • Feature discovery systems

Begin implementation by configuring API authentication and executing your first recommendation request.