Case Studies

Real systems, real results. No stock photos, just architecture.

Oil & Gas Legacy Migration

.NET Framework → .NET 8 Azure 6 months

The Challenge

A Dallas-based oil & gas company was running a 15-year-old monolithic .NET Framework application that managed pipeline operations across Texas. The system was critical. Downtime cost $50,000 per hour in lost production.

Key Problems:

  • Deployment required 4-hour maintenance windows
  • Scaling meant buying more expensive servers
  • No automated testing. Every release was risky
  • Compliance audits were nightmares due to poor logging
  • Recruiting developers who knew .NET Framework 4.5 was impossible

Our Solution

We implemented the Strangler Fig Pattern to gradually extract functionality into microservices. The legacy system continued running while we built the new architecture around it.

Migration Strategy:

We implemented a phased approach:

  • Phase 1: Legacy Monolith - All logic in one application with SQL Server database and manual deployments
  • Phase 2: API Gateway + Gradual Extraction - Introduced API Gateway routing to new microservices while legacy monolith shrinks
  • Phase 3: Full Microservices Architecture - Complete separation into Pipeline Monitoring, Alert & Notify, and Data Analytics services with Event Bus (Azure Service Bus)

Key Technical Decisions:

  • .NET 8: Modern, performant, cross-platform
  • Azure Kubernetes Service: Auto-scaling, high availability
  • Azure Service Bus: Reliable event-driven communication
  • Feature Flags: Gradual rollout with instant rollback
  • Terraform: Infrastructure as Code for repeatability

Implementation Timeline

Week 1-2
Discovery & Audit

Mapped dependencies, identified bounded contexts, created migration plan

Week 3-6
POC & Architecture

Built proof of concept for pipeline monitoring service, validated approach

Month 2-4
Phase 1 Migration

Extracted monitoring, alerting, and reporting services

Month 5-6
Phase 2 & Cutover

Migrated remaining services, decommissioned legacy system

Results

99.97%
Uptime Achieved

Up from 99.2% with legacy system

$2.4M
Annual Savings

Infrastructure costs reduced by 65%

0
Downtime Incidents

Zero unplanned outages during migration

15 min
Deployment Time

Down from 4-hour maintenance windows

Healthcare AI Document Processing

Python / FastAPI TensorFlow HIPAA Compliant

The Challenge

A Dallas healthcare network was manually processing 10,000+ medical records daily. Each record required human review to extract patient information, diagnoses, and treatment plans. The 3-day turnaround was causing patient care delays.

Our Solution

We built an AI-powered document processing pipeline with custom NLP models trained on medical terminology. The system had to be HIPAA-compliant with end-to-end encryption.

Document Processing Pipeline:
  • Document Ingestion: S3 + KMS Encryption for secure storage
  • OCR Pipeline: Tesseract + CV2 for text extraction with bounding boxes
  • NLP Processing: BioBERT fine-tuned model for Named Entity Recognition, Medical Code Extraction, and Relationship Mapping
  • Validation Layer: Human-in-loop review for low confidence cases and edge cases
  • Encrypted Database: PostgreSQL + RDS for secure data storage

Model Architecture:

Input: Scanned medical record (PDF/Image) ? OCR extraction ? Preprocessing (clean, normalize, tokenize) ? NER Model (BioBERT) ? Entity Extraction (patient demographics, diagnoses, medications, procedures, lab results) ? Validation with confidence scoring ? Output: Structured JSON for EHR integration

Results

4 hours
Processing Time

Down from 3 days

94%
Accuracy Rate

Validated against human review

18
FTEs Saved

Redeployed to patient care

100%
HIPAA Compliant

Passed security audit

Manufacturing IoT Platform

Node.js TimescaleDB Real-time Analytics

The Challenge

A Texas manufacturing company had 200+ factory sensors generating 50GB of data daily, but no way to analyze it in real-time. Production bottlenecks went undetected for hours, costing thousands in lost productivity.

Our Solution

We built a real-time IoT data pipeline with predictive maintenance algorithms and a custom dashboard for production managers.

IoT Data Pipeline Architecture:
  • Factory Sensors: MQTT Protocol for Temperature, Pressure, Vibration, Production counters, and Equipment status
  • Message Broker: Apache Kafka with 3 partitions and Replication factor of 3
  • Stream Processing: Apache Flink for real-time aggregation, anomaly detection, and alert triggering
  • TimescaleDB: Time-series data storage with automatic chunking and compression
  • Analytics Engine: Predictive ML models, maintenance alerts, and production forecasts

Results

32%
Downtime Reduction

Predictive maintenance prevented failures

<100ms
Alert Latency

Real-time problem detection

$1.8M
Annual Savings

Reduced unplanned downtime

200+
Sensors Monitored

Across 3 facilities

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