LinkedIn Beta Product

Sales Assistant AI

Autonomous Sales Prospecting Assistant at LinkedIn

Timeline 2024 - Present
Role Staff Software Engineer
Sales Assistant AI

Project Overview

Built an intelligent sales assistant that automates the entire prospecting workflow, from lead identification to personalized outreach generation, using advanced ML models and behavioral analytics.

Key Metrics

Architecture
Multi-Agent System
Specialized AI agents
Communication
MCP Protocol
Standardized AI interfaces
Processing
Real-Time Analytics
Behavioral intent detection

Technology Stack

PythonLangChainScikit-learnXGBoostspaCyTransformersBERTApache KafkaRedis StreamsLangSmith

Key Features

MCP (Model Context Protocol) for standardized AI model communication

Multi-Agent Architecture with specialized sales functions

Research Agent for deep prospect analysis using social media APIs

Scoring Agent implementing ML-based lead scoring with gradient boosting

Outreach Agent generating personalized messages using fine-tuned LLMs

Behavioral Intent Detection with real-time digital footprint analysis

A2A (Agent-to-Agent) Communication for coordinated decision-making

Dynamic Persona Matching using clustering algorithms (K-means, DBSCAN)

Automated Follow-up Sequences with stateful workflow management

API integrations with LinkedIn Sales Navigator, Salesforce CRM, HubSpot

Challenges Solved

  • Implementing MCP protocol for seamless AI model integration
  • Building multi-agent architecture with coordinated decision-making
  • Developing real-time behavioral intent detection systems
  • Creating dynamic persona matching using advanced clustering
  • Ensuring stateful workflow management for automated sequences

Key Outcomes

  • Successfully deployed multi-agent sales automation system
  • Implemented advanced MCP protocol for model communication
  • Built real-time behavioral analytics for prospect insights
  • Achieved automated prospecting workflow from lead identification to outreach