Artemis

The AI-powered nervous system for modern sales—integrate any tool, bridge any data, orchestrate any interaction. Change prospecting into an engine that delivers genuine, timely connections at scale.

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Inspiration

The B2B sales landscape is evolving rapidly, yet sales teams still spend 70% of their time on non-selling activities - researching prospects, qualifying leads, and managing data across disparate systems. We were inspired to build Artemis after witnessing firsthand how AI could transform this process.

Our vision was to create an intelligent sales assistant that doesn't just store data, but actively discovers, enriches, and qualifies prospects using AI agents. We imagined a world where sales teams could focus on what they do best - building relationships and closing deals - while AI handles the heavy lifting of prospect research and qualification.

What it does

Artemis is an AI-powered sales intelligence platform that revolutionizes how B2B companies discover and engage with prospects. Key features include:

AI Agent Ecosystem

  • Scout Agent: Autonomously discovers potential prospects across the web using advanced scraping and AI analysis
  • Analyst Agent: Evaluates prospect fit using custom scoring algorithms and intent signals
  • Strategist Agent: Crafts personalized outreach strategies based on prospect data
  • Executor Agent: Manages multi-channel campaign execution

Intelligent Data Pipeline

  • Automated ETL pipeline processing prospects every minute
  • Smart validation and deduplication to maintain data quality
  • Quarantine system for manual review of edge cases
  • Real-time enrichment from multiple data sources

Smart Features

  • Lead scoring based on 50+ signals including intent data
  • Automated prospect enrichment with company and contact information
  • Multi-workspace support for team collaboration
  • Real-time activity feeds and engagement tracking
  • Comprehensive analytics dashboard with conversion insights

How we built it

Architecture Overview

Frontend (React + Vite) → Backend (Node.js + Express) → Database (Supabase/PostgreSQL)
                                    ↓
                            AI Services (Arcade, LlamaIndex)
                                    ↓
                            External APIs (BrightData, Enrichment)

Technology Stack

  • Frontend: React 18, Vite, Tailwind CSS, Recharts for visualizations
  • Backend: Node.js with Express, ES Modules, Socket.io for real-time updates
  • Database: Supabase (PostgreSQL) with Row Level Security
  • AI Integration: Arcade AI for agent orchestration, LlamaIndex for LLM operations
    • Mastra Framework: AI workflow orchestration and agent management
    • Arcade AI for agent orchestration
    • LlamaIndex for LLM operations
  • Data Collection:
    • BrightData: Web scraping infrastructure and proxy networks for prospect discovery
    • Custom scrapers for LinkedIn and company websites
  • Data Pipeline: Custom ETL system with staging tables and automated processing
  • Infrastructure: Docker containers, ready for Vercel (frontend) and Render (backend) deployment

Key Implementation Details

  1. Built a robust data pipeline with staging and quarantine tables
  2. Implemented service-oriented architecture for modular functionality
  3. Created real-time WebSocket connections for live updates
  4. Developed comprehensive validation and normalization services
  5. Integrated multiple AI services for intelligent prospect analysis

Challenges we ran into

  1. Database Schema Evolution The most significant challenge was discovering that critical pipeline tables were missing from the production database. We had to carefully create migration scripts that wouldn't affect existing data while adding the necessary infrastructure.

  2. AI Agent Coordination Orchestrating multiple AI agents to work together seamlessly required careful state management and error handling. We implemented a workflow system to ensure agents could pass context and results between each other.

  3. Data Quality at Scale Processing thousands of prospects from various sources meant dealing with inconsistent data formats, duplicates, and invalid entries. We built a comprehensive validation system with over 15 validation rules and a quarantine mechanism for edge cases.

  4. Authentication & Multi-tenancy Building a secure multi-workspace system with proper data isolation required implementing Row Level Security policies and careful permission management.

Accomplishments that we're proud of

  1. Fully Automated Data Pipeline We built a production-ready ETL pipeline that processes prospects every minute, automatically validating, normalizing, and enriching data without human intervention.
  2. Intelligent Deduplication Our fuzzy matching algorithm can identify duplicate prospects even with variations in names, emails, and company information, maintaining data integrity at scale.

What we learned

  1. AI Orchestration Complexity Coordinating multiple AI agents requires careful consideration of context passing, error handling, and fallback strategies. We learned to design systems that gracefully degrade when AI services are unavailable.
  2. Data Pipeline Design Building a robust data pipeline taught us the importance of staging areas, validation layers, and quarantine systems. The ETL pattern proved invaluable for maintaining data quality.

What's next for Artemis

  • Advanced AI Features: Implement GPT-4 powered email generation and response analysis
  • Integration Ecosystem: Add native integrations with Salesforce, HubSpot, and other CRMs
  • Mobile Applications: Develop iOS and Android apps for on-the-go prospect management
  • Enhanced Analytics: Add predictive analytics and conversion probability scoring

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