Real Problems. Real Results.

DataFlow's support team was drowning in 2,000+ daily tickets with an average 18-hour response time. Manual triage was inconsistent, and 40% of tickets were routed to the wrong department.

We built a multi-agent AI system using LangChain and FastAPI that auto-classifies, prioritizes, and drafts responses for incoming tickets. Integrated with their existing Zendesk workflow via webhooks. Deployed on GCP Cloud Run with auto-scaling.

Response time dropped from 18 hours to 12 minutes. Correct routing increased to 96%. The support team now handles 3x volume with the same headcount, saving $420K/year in staffing costs.

A penetration test revealed 14 critical vulnerabilities across SecureStack's microservice mesh. API keys were hardcoded, inter-service communication was unencrypted, and there was no rate limiting or anomaly detection.

Designed and implemented a zero-trust API gateway with mTLS between all services, JWT validation, dynamic rate limiting, and real-time threat detection using custom ML models. Migrated all secrets to HashiCorp Vault.

All 14 critical vulnerabilities eliminated. Attack surface reduced by 80%. Passed SOC 2 Type II audit on first attempt. Anomaly detection catches and blocks suspicious patterns within 200ms.

Industrial Dynamics was losing $2M annually to unplanned equipment downtime. Sensor data from 500+ IoT devices was collected but unused — sitting in raw CSV exports with no analysis pipeline.

Built an end-to-end ML pipeline: real-time data ingestion from IoT sensors, feature engineering with Pandas, predictive models in PyTorch, and a Django dashboard for operations teams. Deployed on Azure with automated retraining every 48 hours.

The system predicts equipment failures 72 hours in advance with 94% accuracy. Unplanned downtime reduced by 67%, saving $1.3M in the first year. Operations team now makes data-driven maintenance decisions.