AI-Powered B2B Revenue Operations Command Center
Full-cycle autonomous lead generation · Predictive scenario modeling · Post-sales retention analytics
Most B2B sales teams burn 60–70% of SDR time on manual prospecting, data entry, and qualification calls that go nowhere. Meanwhile, high-quality leads decay — response time kills conversion.
"Why does it take 3 days to reach a lead that filled out a form, and why are 40% of our outbound leads outside our ICP?"
RevOps Lead Engine solves this by building a fully autonomous B2B sales pipeline — from programmatic lead generation to AI-powered qualification, predictive revenue modeling, and post-sales retention tracking — eliminating manual SDR grunt work and giving leadership real-time pipeline visibility.
A GenAI conversational interface embedded directly inside the dashboard. Ask natural-language questions about pipeline risk, quota pacing, rep performance, and revenue forecasts. Includes quick-action executive prompts for CEO, VP Sales, and VP Revenue personas.
An interactive predictive engine with four adjustable levers — Lead Volume, Win Rate, ACV, and Cycle Time. Projects a 90-day S-curve revenue trajectory with real-time AI insights that tell you whether you'll hit quota and why.
Tracks the "BowTie Funnel" beyond closed-won. Features Net Dollar Retention (NDR) metrics, Gross Retention Rate, Account Health scoring across 3 risk tiers, and a dynamic ARR Composition Waterfall chart (Starting ARR → New Logos → Expansion → Contraction → Churn → Ending ARR).
Every lead score (0–100) includes transparent textual explanations: (+25 ICP) Strong revenue fit, (-30 Needs) Enterprise lock-in risk. Hover over any lead to see the full scoring breakdown — no black-box models.
VP-level executive overview tracking Quota Attainment, Pipeline Coverage Ratio, Average Deal Size, Win Rate trends, CAC/LTV unit economics, and real-time SDR pacing metrics — all filterable by date range and individual sales rep.
Filter the entire database by firmographic fit (industry, geography, employee count, revenue) and BANT qualification signals. Queue leads into targeted outreach campaigns with one click.
Full-text search across the active lead database with XAI tooltips explaining every score component. Instantly surface high-intent prospects with technology stack gaps and recent buying signals.
Deep-dive into individual leads with AI-generated Deal Briefs, SPIN discovery questions, BANT qualification summaries, and one-click actions for LinkedIn outreach and company research.
Conversion funnel visualization, Stage Velocity tracking (days per pipeline stage), Campaign ROI attribution table, and an SDR Leaderboard ranking reps by pipeline generated, meetings booked, and quota attainment.
Tracks 3-touch email cadence performance — opens, replies, bounces — with weekly trend charts and response classification (Interested / Not Now / Not Interested / Auto-reply).
Simulated Salesforce-style opportunity management with deal stages, amounts, close dates, and pipeline progression tracking.
┌──────────────────────────────────────────────────────────────────────────────┐
│ B2B AUTONOMOUS LEAD ENGINE │
│ Full-Cycle SDR Automation │
├──────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ STAGE 1 │──▶│ STAGE 2 │──▶│ STAGE 3 │──▶│ STAGE 4 │ │
│ │ PROSPECTING │ │ ENRICHMENT │ │ SCORING & │ │ AUTOMATED │ │
│ │ & DISCOVERY │ │ & PROFILING │ │ QUALIFICATION│ │ SDR OUTREACH │ │
│ └─────────────┘ └──────────────┘ └──────────────┘ └──────┬───────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ STAGE 5: CRM SYNC & HANDOFF │ │
│ │ HubSpot / Salesforce — Opportunity Management │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ 🖥️ STREAMLIT REVOPS COMMAND CENTER │ │
│ │ Revenue Dashboard · Scenario Modeler · AI Copilot · NDR Tracking │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└──────────────────────────────────────────────────────────────────────────────┘
┌────────────────────────────────────────────────────────────────────────────────────────────────┐
│ DATA LINEAGE & PERSISTENCE │
├────────────────────────────────────────────────────────────────────────────────────────────────┤
│ │
│ ICP CONFIG ──▶ DISCOVERY ──▶ ENRICHMENT ──▶ SCORING ──▶ OUTREACH ──▶ CRM │
│ (YAML) (Apollo/CNPJ) (Hunter/BuiltWith) (Rules/ML) (Email) (HubSpot) │
│ │ │ │ │ │ │ │
│ │ dim_companies fct_enriched_leads fct_scored fct_outreach Deals │ │ │ dim_contacts │ + deal_brief _events + Opps │
│ │ │ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼ ▼ │
│┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │
││ TARGETS │ │ RAW LEADS │ │ PROFILES │ │ SCORES │ │ SEQUENCES │ │ DEALS/OPPS│ │
│└─────┬─────┘ └─────┬─────┘ └─────┬─────┘ └─────┬─────┘ └─────┬─────┘ └─────┬─────┘ │
│ └───────────────┴──────────┬─────┴───────────────┴─┬──────────────┴───────────────┘ │
│ ▼ ▼ │
│ LAKEHOUSE / WAREHOUSE (SQLite ⮕ BigQuery) │
└────────────────────────────────────────────────────────────────────────────────────────────────┘
| Layer | Technology |
|---|---|
| Frontend | Streamlit, Custom CSS (Cybermorphic dark theme), Plotly Graph Objects |
| Backend | Python 3.10+ · SQLite3 |
| Data Models | Pydantic v2 (strict validation, JSON serialization) |
| AI / ML | LLM prompt templates · Rule-based scoring engine · XAI transparency layer |
| Data Simulation | Faker · Random weighted distributions · Monte Carlo projections |
| API | FastAPI · Uvicorn (scoring endpoint) |
| CRM | HubSpot / Salesforce interface (mock for MVP) |
| Testing | pytest |
| Config | YAML (ICP profiles) · .streamlit/config.toml (theme) |
- Python 3.10+ (Conda recommended)
- Git
# Clone the repository
git clone https://github.com/eduardocornelsen/revops_lead_engine.git
cd revops_lead_engine
# Create and activate conda environment
conda create -n lead_engine python=3.10 -y
conda activate lead_engine
# Install dependencies
pip install -r requirements.txt# Runs end-to-end: discovery → enrichment → scoring → outreach → CRM sync
python -m src.pipelinestreamlit run src/dashboard/app.py
# Open http://localhost:8501uvicorn src.api.main:app --host 0.0.0.0 --port 8000 --reload
# Docs at http://localhost:8000/docsrevops_lead_engine/
├── .streamlit/
│ └── config.toml # Streamlit theme configuration
├── config/
│ └── icp_config.yaml # Ideal Customer Profile definition
├── docs/
│ ├── architecture.md # System architecture diagrams
│ ├── data_dictionary.md # Schema documentation for all tables
│ └── Real_Data_Integration_Guide.md # Production API integration guide
├── src/
│ ├── pipeline.py # End-to-end pipeline orchestrator
│ ├── config/
│ │ ├── settings.py # Environment settings (Pydantic)
│ │ └── icp_loader.py # ICP YAML config loader
│ ├── models/
│ │ └── models.py # Pydantic data models (Company, Lead, Deal)
│ ├── database/
│ │ ├── database.py # SQLite database engine & queries
│ │ └── seed_data.py # Synthetic data generator
│ ├── discovery/
│ │ └── discovery.py # Lead discovery engine (Stage 1)
│ ├── enrichment/
│ │ └── enrichment.py # Multi-source enrichment pipeline (Stage 2)
│ ├── scoring/
│ │ ├── scoring.py # Lead scoring engine with XAI (Stage 3)
│ │ └── deal_brief.py # AI deal brief & SPIN question generator
│ ├── api/
│ │ └── main.py # FastAPI scoring endpoint
│ ├── outreach/
│ │ └── outreach.py # 3-touch email outreach automation (Stage 4)
│ ├── crm/
│ │ └── crm_sync.py # CRM sync & handoff logic (Stage 5)
│ └── dashboard/
│ ├── app.py # Main Streamlit application (1300+ lines)
│ └── sim_metrics.py # Revenue simulation & metric generators
├── tests/
│ ├── test_icp_loader.py # ICP config validation tests
│ ├── test_scoring.py # Scoring engine unit tests
│ └── test_pipeline.py # Pipeline integration tests
├── deal_briefs/ # Sample AI-generated deal briefs
├── requirements.txt # Python dependencies
├── pyproject.toml # Project metadata
└── README.md # This file
| # | Module | Purpose |
|---|---|---|
| 1 | 💬 AI RevOps Copilot | Natural-language AI chat for pipeline risk analysis, quota pacing, and rep performance |
| 2 | 📊 Revenue Dashboard | VP-level KPIs: quota attainment, pipeline coverage, CAC/LTV, revenue forecast |
| 3 | ⚡ Generate Leads | Firmographic & BANT filtering engine to queue targeted outreach campaigns |
| 4 | 🔍 Lead Intelligence | Full-text search with XAI tooltips explaining score components |
| 5 | 🧭 Sales Navigator | Individual lead deep-dive with AI Deal Briefs, SPIN questions, LinkedIn actions |
| 6 | 💼 CRM / Salesforce | Opportunity management with deal stages and pipeline progression |
| 7 | 📈 Pipeline Analytics | Funnel drop-off, Stage Velocity, Campaign ROI attribution, SDR Leaderboard |
| 8 | 📧 Outreach Performance | Email cadence tracking: opens, replies, bounces, response classification |
| 9 | 🏦 Post-Sales (NDR) | Net Dollar Retention, Account Health tiers, ARR Waterfall chart |
| 10 | 🔮 Scenario Modeler | 90-day revenue trajectory projection with adjustable revenue levers and AI insights |
The dashboard follows a Cybermorphic design language — a dark-mode aesthetic combining:
- Deep gradient backgrounds (
#020617→#1e3a8a) for a premium, immersive feel - Glassmorphism metric cards with
backdrop-filter: blur(16px)and subtle border highlights - Staggered
@keyframesload animations for a polished entry experience - Neon accent colors (indigo, emerald, cyan) against the dark canvas for data emphasis
- High-contrast Plotly charts with dark text on colored surfaces for maximum readability
- 10-module Streamlit RevOps Command Center
- ICP-driven lead discovery with simulated multi-source data
- Multi-source enrichment pipeline (tech stack, buying signals)
- Rule-based lead scoring (0–100) with XAI transparency
- BANT pre-qualification automation
- AI deal brief generation (SPIN questions, objection handling)
- 3-touch email sequence engine with response classification
- Revenue Scenario Modeler with 90-day S-curve projections
- Post-Sales NDR module (Account Health, ARR Waterfall)
- GenAI RevOps Copilot with executive prompt templates
- FastAPI real-time scoring endpoint
- Dynamic per-rep and per-period filtering across all modules
- Cybermorphic dark-mode UI with CSS animations
- Apollo.io & Hunter.io live API integration
- HubSpot / Salesforce bi-directional CRM sync
- ML-based scoring model (XGBoost / Scikit-learn)
- MLFlow experiment tracking
- SendGrid email dispatch integration
- n8n / Airflow workflow orchestration
- BigQuery / Snowflake data warehouse migration
This project is powered by src/pipeline.py to generate realistic synthetic data for demonstration purposes without requiring expensive API keys.
To adapt this for a live production B2B SaaS environment, refer to: 👉 Real Data Integration Guide
| Document | Description |
|---|---|
| Architecture Guide | System design & data flow diagrams |
| Data Dictionary | Schema documentation for all tables |
| Real Data Integration Guide | Replacing synthetic data with live APIs |
Contributions are welcome! This project is part of a portfolio demonstrating end-to-end Revenue Operations engineering capabilities.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
MIT License — see LICENSE for details.
Built with ☕ by Eduardo Cornelsen — © 2026 All Rights Reserved
Revenue Operations · Data Engineering · AI/ML · Full-Stack Development