AI Lead Conversion Platform

A privacy-safe reconstruction of a lead-conversion prototype developed during a 2024 AI hackathon in Ulm, Germany.

The platform will combine a reproducible machine-learning pipeline, a prediction API, a web dashboard, and LLM-assisted outreach message generation. The public implementation uses synthetic CRM data only.

Important

The original 2024 prototype was a team project and received a hackathon award. This repository is an independent 2026 reconstruction. The original corporate dataset, internal documents, credentials, and proprietary source files are not included. The reconstructed repository itself is not the award-winning artifact.

Why this project exists

Sales teams often have more leads than they can review manually. This project explores how a team could prioritize leads, understand the factors behind a prediction, and draft a reviewable outreach message without exposing personal or corporate data.

Planned user flow

1. Generate privacy-safe raw-like lead and lead-note data.
2. Reconstruct the historical ObjectID -> ParentObjectID relationship.
3. Reproduce the historical join/reduction workflow with leakage safeguards.
4. Compare baseline, Random Forest, and XGBoost classifiers.
5. Request a conversion prediction through a FastAPI endpoint.
6. Review model evidence in a React/TypeScript dashboard.
7. Generate an editable outreach draft through an optional LLM adapter.

Reconstruction goals

• Ground the public schema and aggregate distributions in evidence visible in public 2024 hackathon notebooks without publishing source records.
• Preserve the distinction between wide raw CRM data and the smaller modeling dataset rather than designing directly around today's feature subset.
• Prevent entity and target leakage by splitting at lead level and excluding post-outcome information.
• Separate preprocessing fitted on training data from evaluation data.
• Report macro-F1, recall, precision, ROC-AUC, and a confusion matrix in addition to accuracy.
• Treat generated messages as human-reviewed drafts, not autonomous decisions.
• Keep the project reproducible with tests, Docker, and GitHub Actions.

Current status

Phase 1 - calibrated synthetic data foundation

• Public/private data boundary documented
• Minimal API health endpoint and test added
• Historical lead/note raw shape and join relationship reconstructed
• Public aggregate CRM profile documented
• Privacy-safe synthetic generator calibrated to observed aggregate behavior
• Historical join/reduction and leakage-safe preprocessing
• Reproducible EDA summary and diagnostic charts
• Model baselines and experiment report
• Prediction and explanation endpoints
• React/TypeScript dashboard
• Optional LLM message adapter
• Docker Compose and CI workflow

See the roadmap for the planned milestones.

Quick start

python -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[dev]"
uvicorn lead_intelligence.api:app --reload

Then open http://127.0.0.1:8000/health.

Run the tests with:

pytest

Synthetic CRM data

The original public notebook workflow shows a wide leads.csv export (86,244 rows x 181 columns) and a one-to-many lead_notes.csv export (134,793 rows x 16 columns). The two were joined through ObjectID = ParentObjectID, then reduced to a smaller working dataset.

The reconstruction keeps that separation. It also calibrates several synthetic properties from public aggregate notebook outputs, including:

• the five observed workflow status proportions;
• all 39 observed Source_Text frequencies;
• the share of leads with note rows and average note count;
• field-specific missingness for the historical working cohort;
• observed cardinality/skew for owner, sales-unit, territory, and lead-name fields;
• mixed date formatting, language proportions, and dirty note placeholders.

All public records are still newly generated. Historical identifiers, customer or employee names, and free-text notes are not copied, masked, translated, or sampled.

The reconstruction is therefore aggregate-calibrated synthetic data, not an anonymized copy of the original corporate dataset. Properties whose full public distribution is unavailable remain explicit approximations rather than inferred historical facts.

Small matching examples are committed at:

• data/synthetic/leads_sample.csv
• data/synthetic/lead_notes_sample.csv

See Synthetic CRM specification and Historical CRM aggregate profile for the source evidence, calibration boundary, and remaining approximations.

Repository boundaries

• src/lead_intelligence/: Python application and ML code
• tests/: automated tests
• frontend/: planned React/TypeScript client
• data/synthetic/: generated, non-identifying samples only
• docs/: project history, data policy, and engineering decisions

For the historical boundary and attribution policy, see Original project context. For handling rules, see the Data and secrets policy.

License

No license has been selected yet. Copyright, licensing, and attribution boundaries will be reviewed before any external reuse or distribution.