AI revenue signal engine — lead scoring, prioritisation, and next-best-action from inbound noise
No scoring layer — inbound lead triage relied entirely on manual gut-feel review.
See how an inbound lead is decomposed into features, scored, ranked, and handed off with reasoning attached.
Lead triage method: Manual gut-feel review
Lead triage method: Calibrated ML score + LLM signals
Inbound leads are scored, ranked, and routed automatically. High-intent signals surface to reps within minutes with a structured qualification card. The modelled pipeline opportunity uplift is €850k based on historical conversion rates applied to improved triage. Manual review overhead is estimated at 11h/week per rep — time redirected to active selling. Both figures are modelled estimates, not measured outcomes.
LinkedIn and form leads have systematically different signal distributions. Solved via source-specific feature scaling and a source-channel indicator feature that lets the model adapt to distribution differences without separate per-source models.
raw GPT-4o-mini scores clustered near 0.5 without explicit calibration. Resolved by prompting the model to use the full 0–1 scale with anchor examples at each decile, followed by a distribution normalisation step to enforce uniform spread across the scoring output.
the model initially over-weighted recently-contacted leads due to label timing effects in the CRM data. Fixed via time-normalised feature engineering and a temporal train/test split that evaluates on a future cohort, not a random split of the same period.
~18% of inbound have no Clearbit coverage. LLM web extraction fills 73% of gaps, but the remaining 27% degrade enrichment quality. A completeness score gate (minimum 5 core fields) flags low-coverage records before scoring and includes a data confidence label in the qualification card.
XGBoost binary classifier trained on CRM outcome data with a temporal train/test split. Features: 60% structured firmographic signals, 40% LLM-extracted semantic signals. SHAP TreeExplainer computes per-prediction feature contributions displayed on AE qualification cards. Model is designed for monthly retraining on a rolling 18-month window — retraining is triggered automatically when feature drift metrics exceed threshold.