SmartGuy
How AZK.AI designed an automated B2B recommendation engine to vector-match corporate network members based on real-time activity and service constraints.
Client Challenge
Large B2B business networks fail to match compatible members efficiently. Standard directory interfaces rely on manual grid searches, checkmarks, and message threads, which suffer from poor conversion rates. Member profiles, service capabilities, locations, and historical transaction activities are rarely analyzed as unified vectors to suggest high-value networking pairings.
Objectives
- Develop an automated B2B profile scoring and similarity matching platform.
- Map click behaviors, search queries, and listings to build accurate user embeddings.
- Trigger real-time notifications to connect matching network partners.
Solution Design
We built a real-time recommendation core using Next.js, NestJS, and Pinecone vector similarity databases.
The platform monitors member actions, profile settings, and service postings. When updates occur, a NestJS microservice calls OpenAI embeddings to construct profile vectors and indexes them into Pinecone. A similarity scoring loop checks matches across active members and trigger email alerts to connect partners.
Implementation Process
Phase 1: Ingestion Setup
Monitored user transaction history and listing postings to log behavioral metrics.
Phase 2: Embedding Generation
Configured OpenAI model scripts to vectorize user profile data on update triggers.
Phase 3: Connection Alerts
Built alert microservices in Redis to notify partners upon matching similarity scores.
Results & Business Outcomes
5x Faster Member Connections
Rather than sorting through directory pages manually, members receive tailored compatible recommendations daily.
30% Messaging Increase
By providing clear business compatibility scores, click-through rates and member messages grew by 30%.
Technology Stack
System Architecture Flow
Monitors and logs real-time user click behaviors and listings.
Constructs business and service profile embeddings using OpenAI.
Compares profile vectors using cosine similarity scoring rules.
Automatically triggers alert flows to connect matching partners.
SmartGuy Connection Dashboard
Simulated user interface showing recommended B2B matching profiles, vector scores, and contact actions.
Matched Partner: Apex Development
94% MatchSoftware consulting firm seeking Next.js engineers. Compatibility score matches your service profile.
Matched Partner: Summit Legal
89% MatchCompliance consultancy looking for automated document processing tools.
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