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Challenge

Sensor failures were only caught after customer complaints — causing costly on-site service calls and brand damage. No early warning system existed.

Solution

Built a real-time time-series forecasting pipeline on device telemetry. An LSTM model combined with a statistical ensemble (Z-score + rolling baseline) detects anomalous sensor behavior 4–6 hours before failure threshold is breached. Alerts route to the field team's dashboard automatically.

Challenge

Sales team spending 30+ hours/week on manual prospecting. ICP targeting was inconsistent and outreach was generic — low reply rates, wasted pipeline.

Solution

Built a multi-stage NLP pipeline that scores LinkedIn profiles and company signals against ICP criteria. A rules-based + ML classifier assigns intent scores. Outreach sequences auto-trigger based on score threshold — personalized using a fine-tuned LLM summarizer trained on their best-performing emails.

Challenge

Marketing campaigns were one-size-fits-all against 2M+ customer records. No way to identify high-value segments or predict churn — wasted ad spend, reactive retention.

Solution

Applied K-Means and DBSCAN clustering on RFM features plus behavioral signals to discover 5 distinct customer segments. A gradient boosting churn model (XGBoost) trained on 18 months of transaction history integrates directly into their CRM for campaign activation and real-time churn scoring.

Challenge

Manual quality auditing of customer communications consumed 40 hours/week. FCC compliance required proof of evaluation consistency — impossible with human-only review at this scale.

Solution

Deployed an Epistema-based evaluation agent that scores customer communications automatically. Each evaluation generates a full evidence packet: criteria applied, decision, source data, and reasoning trace — all audit-ready for FCC review.