EonAI Lead Optimizer MVP

Product: EonAI Lead Optimizer MVP
Version: 2.0 - Focused Scope
Owner: Michael Pouliot
Date: July 2025
Timeline: 4-6 weeks
Scope: Core data pipeline + optimization + Monday.com integration

MVP Focus

Core Value: Automated data ingestion → optimization analysis → actionable recommendations → Monday.com workflow

Scale Target: 2-5M records/month

Delivery: Production-ready foundation for v2 automation

1. MVP Product Vision
2. Core Requirements & Success Criteria
3. System Architecture
4. Data Pipeline Components
5. Optimization Engine
6. Monday.com Integration
7. Production Requirements
8. Timeline & Weekly Milestones

1. MVP Product Vision

A lightweight, production-ready lead optimization system that automatically ingests lead data, runs optimization analysis, and generates actionable recommendations delivered to Monday.com for manual implementation.

Key Principle: Minimal viable product that delivers immediate value while laying the foundation for full automation in v2.

2. Core Requirements & Success Criteria

Functional Requirements

Automated Data Ingestion: Pull lead data from sources into new database
Scheduled Optimization: Daily/hourly analysis runs to identify improvements
Smart Recommendations: Generate suggestions for lead type, source, motivation, communication channel, message timing
Alert System: Notify team of critical issues and opportunities
Monday.com Integration: Create tasks with purchase instructions and implementation details
Audit Trail: Log all data flows and recommendations for tracking

Success Metrics

Handle 2-5M records/month reliably
Generate optimization recommendations within 30 minutes of data ingestion
99% data ingestion success rate
Zero manual data transfer processes
Ready for v2 automation enhancement

3. System Architecture

High-Level Components

Data Ingestion Service: Pulls data from lead sources
Lead Database: PostgreSQL database for all lead data
Optimization Engine: Analyzes data and generates recommendations
Alert System: Monitors and notifies on thresholds
Monday Integration Service: Creates tasks and purchase instructions
Scheduler: Manages automated runs and data pulls

Technology Stack

Backend: Python with FastAPI
Database: PostgreSQL 14+ with connection pooling
Scheduler: Celery with Redis
Integrations: REST APIs for data sources and Monday.com
Monitoring: Basic logging and health checks
Deployment: Docker containers on cloud infrastructure

4. Data Pipeline Components

4.1 Data Ingestion Service

Input Sources

Lead provider APIs (configurable endpoints)
CSV/Excel file monitoring (as fallback)
Webhook receivers for real-time data

Data Processing

Schema validation and normalization
Duplicate detection using phone/email combinations
Data quality scoring and flagging
Basic lead enrichment (timezone, market classification)

Core Database Schema

-- Core tables for MVP
leads (
    id, external_id, source, lead_type, contact_info,
    market, motivation_level, quality_score, created_at
)

optimization_runs (
    id, run_date, leads_processed, recommendations_generated, 
    status, duration, created_at
)

recommendations (
    id, run_id, recommendation_type, current_value, 
    suggested_value, confidence_score, impact_estimate, status
)

monday_tasks (
    id, recommendation_id, monday_item_id, task_type,
    purchase_instructions, status, created_at, completed_at
)

ingestion_log (
    id, source, batch_size, success_count, error_count,
    processed_at, errors
)
    

4.2 Data Quality & Monitoring

Real-time data validation with error logging
Duplicate rate monitoring and alerts
Source performance tracking
Data freshness monitoring

5. Optimization Engine

5.1 Analysis Components

Performance Analysis

Source Performance: Conversion rates by lead source
Lead Type Effectiveness: Which lead types perform best
Timing Analysis: Optimal contact times by market/segment
Communication Channel: Best performing channels (call, text, email)
Message Performance: Which messages drive highest engagement

Optimization Algorithms

Statistical analysis of conversion patterns
Trend detection for performance changes
Cost-benefit analysis for source allocation
Simple machine learning for pattern recognition

5.2 Recommendation Types

Recommendation Type	Analysis	Output
Lead Source Optimization	ROI and conversion rate analysis	Increase/decrease spend on specific sources
Lead Type Targeting	Performance by demographics/motivation	Target different lead types
Communication Timing	Contact time vs response rates	Optimal contact windows by market
Channel Optimization	Call/text/email performance	Preferred communication method
Message Optimization	Message variant A/B testing	Best performing message templates

5.3 Alert Triggers

Performance Drops: Conversion rate drops >20% week-over-week
Source Issues: Lead source delivers <50% expected volume
Quality Alerts: Data quality score drops below threshold
Cost Alerts: Cost per lead increases >30% without conversion improvement
Opportunity Alerts: New high-performing patterns detected

6. Monday.com Integration

6.1 Task Creation Workflow

Optimization engine generates recommendations
System creates Monday.com tasks with detailed instructions
Tasks include purchase instructions and implementation steps
Team implements changes manually
System tracks completion status

6.2 Monday Board Structure

Lead Optimization Board

Column	Type	Purpose
Task Title	Text	Recommendation summary
Recommendation Type	Dropdown	Source, Timing, Channel, Message, etc.
Priority	Dropdown	High/Medium/Low based on impact estimate
Impact Estimate	Numbers	Expected improvement percentage
Purchase Instructions	Long Text	Detailed steps for implementation
Status	Status	Not Started/In Progress/Complete
Assigned	Person	Team member responsible
Due Date	Date	Implementation deadline

6.3 Purchase Instructions Template

Example Purchase Instruction:

RECOMMENDATION: Increase Facebook Lead Ads spend in Atlanta market

CURRENT STATE:
- Source: Facebook Lead Ads - Atlanta
- Current Spend: $500/week
- Current Volume: 150 leads/week
- Conversion Rate: 12%

RECOMMENDED CHANGE:
- Increase spend to: $750/week (+50%)
- Expected volume: 225 leads/week
- Projected conversion: 13.5% (based on pattern analysis)

IMPLEMENTATION STEPS:
1. Log into Facebook Ads Manager
2. Navigate to Atlanta Lead Campaign
3. Increase daily budget from $71 to $107
4. Monitor for 48 hours for delivery changes
5. Report results back to optimization system

TRACKING:
- Track lead volume increase
- Monitor conversion rate changes
- Report cost per conversion after 1 week
    

7. Production Requirements

7.1 Performance & Scalability

Data Processing: Handle 2-5M records/month (170K records/day peak)
Response Time: API endpoints respond within 200ms
Optimization Runtime: Complete analysis within 30 minutes
Uptime: 99.5% availability (acceptable for MVP)

7.2 Error Handling & Reliability

Retry logic for failed API calls (3 attempts with exponential backoff)
Dead letter queues for unprocessable records
Circuit breaker pattern for external API calls
Graceful degradation when services are unavailable

7.3 Security & Compliance

API key authentication for all endpoints
Data encryption at rest and in transit
Basic PII handling compliance
Access logging and audit trail

7.4 Monitoring & Alerting

System Health: Basic health checks and uptime monitoring
Data Pipeline: Monitor ingestion rates and processing times
Business Alerts: Email/Slack notifications for optimization opportunities
Error Tracking: Log and alert on system errors

8. Timeline & Weekly Milestones

Week	Milestone	Deliverables	Success Criteria
Week 1	Foundation & Data Pipeline	• Database schema creation • Basic data ingestion service • Docker setup & deployment pipeline	• Can ingest 1K test records • Database properly structured • CI/CD pipeline functional
Week 2	Data Processing & Quality	• Data validation and normalization • Duplicate detection system • Basic error handling and logging	• Process 10K records without errors • Duplicate detection working • Error logs properly captured
Week 3	Optimization Engine	• Statistical analysis components • Recommendation generation logic • Performance benchmarking	• Generate basic recommendations • Analysis completes in <30 min • Recommendations are actionable
Week 4	Monday.com Integration	• Monday API integration • Task creation workflow • Purchase instruction templates	• Tasks automatically created • Instructions are clear/actionable • Status tracking functional
Week 5	Alerts & Monitoring	• Alert system implementation • Basic monitoring dashboard • Performance optimization	• Alerts trigger correctly • System handles peak load • All monitoring functional
Week 6	Production & Launch	• Production deployment • Load testing & optimization • Documentation & handover	• Handles 2-5M records/month • System stable in production • Team trained on system

Risk Mitigation

Week 1-2: Start with simple data sources to validate pipeline
Week 3: Begin with basic statistical analysis, can enhance later
Week 4: Have Monday.com sandbox ready for testing
Week 5-6: Plan for load testing with realistic data volumes

Out of Scope for MVP

Advanced machine learning models
Real-time dashboard/UI
Multi-client support
AI chat interface
Complex role-based permissions
Advanced reporting features

Note: These features are planned for v2 with full automation.

V2 Preparation

This MVP lays the foundation for v2 automation by:

Establishing reliable data pipeline
Validating optimization algorithms
Creating structured recommendation format
Building Monday.com integration patterns
Proving scalability at target volumes

Document Version: 2.0 MVP Focused | Timeline: 4-6 weeks

Owner: Michael Pouliot | Status: Ready for Development

Product Requirements Document