We build forecasting models using Prophet, ARIMA, LSTM, Transformer-based and hybrid ML pipelines. Solutions project sales, footfall, workload, staffing, inventory, and energy use with confidence intervals, seasonality, macro signals, and what-if tuning. Results integrate into BI tools, alerts, and automated procurement or pricing rules.

We predict which customers are likely to leave, downgrade, or expand based on behavior, transactions, and engagement signals. Scoring enables targeted retention, prioritization of high-risk accounts, and automated campaign triggers inside CRM or marketing systems. Lifetime value models optimize spend and resource allocation.

We develop fraud and credit models using anomaly detection, ensemble learning, and network analysis. Features include explainability, risk thresholds, escalation rules, API-based validation, and regulatory-ready logging. Works for payments, loans, claims, identity checks, and account activity across banking, fintech, and insurance.

We analyze sensor data, machine logs, and operational history to predict breakdowns, faults, and warranty risks before they occur. Models reduce downtime and optimize repair schedules, spare inventory, and SLA compliance. Deployable on edge devices, SCADA systems, and cloud observability stacks.

Instead of dashboards waiting for human review, models push decisions into action: auto-ordering stock, reallocating agents, adjusting pricing, or triggering outreach. Results are governed by thresholds, approvals, and monitoring so AI decisions remain safe, reversible, and auditable.

We deploy retraining pipelines, feature stores, data drift detection, versioned models, rollback safety, and cost monitoring. Business teams get dashboards showing accuracy, bias, latency, and ROI so ML systems remain reliable long-term instead of degrading silently in production.