Case Studies
Real AI transformation outcomes with measurable results. Every engagement includes security-first engineering, zero-trust architecture, and knowledge transfer to your team.
AI-Powered Diagnostic System Cuts Diagnosis Time by 85%
Multi-specialty hospital network deploys computer vision and NLP to accelerate diagnosis while maintaining 99.2% accuracy and HIPAA compliance.
Client Profile
- Industry: Healthcare
- Size: 8 hospitals, 45 clinics
- Annual Patients: 2.5M+
- Challenge: Radiologist shortage, diagnosis delays
Timeline
- Discovery: 3 weeks
- Pilot: 8 weeks (1 hospital)
- Full Rollout: 16 weeks
- Total: 6 months
Technology Stack
- Computer vision (PyTorch)
- NLP for clinical notes
- HL7/FHIR integration
- Azure HIPAA-compliant hosting
The Challenge
A regional hospital network faced critical radiologist shortages, resulting in 3-7 day delays for diagnostic imaging reports. This created bottlenecks in treatment plans, increased patient anxiety, and risked adverse outcomes for time-sensitive conditions.
Existing PACS systems had no AI capabilities, and radiologists spent 60% of their time on routine cases that could be accelerated with AI assistance. The organization needed a solution that maintained diagnostic accuracy while meeting strict HIPAA and FDA guidelines.
Our Solution
Phase 1: Proof of Concept (8 weeks)
- Deployed computer vision models for X-ray and CT scan analysis at one pilot hospital
- Integrated with existing PACS using HL7/FHIR standards
- Built radiologist review interface with AI confidence scores
- Validated against 10,000 historical cases with ground truth diagnoses
Phase 2: Production Deployment (16 weeks)
- Rolled out to all 8 hospitals with phased approach
- Implemented human-in-the-loop workflows for edge cases
- Built real-time monitoring dashboard for model performance
- Created training program for 120 radiologists across the network
Security & Compliance
HIPAA Compliance
- End-to-end PHI encryption
- BAA agreements in place
- Audit logging for all data access
- Regular compliance assessments
Zero-Trust Architecture
- Role-based access control (RBAC)
- Network segmentation
- MFA for all clinical staff
- Continuous monitoring & alerting
FDA Guidance
- Followed FDA AI/ML guidance
- Clinical validation protocols
- Model monitoring & retraining
- Documentation for 510(k) pathway
Results
85%
Faster Diagnosis
Average turnaround: 6 hours (was 3-7 days)
99.2%
Diagnostic Accuracy
Validated against radiologist consensus
40%
Radiologist Productivity
More time for complex cases
"The AI system has transformed our radiology department. We're catching critical findings faster, our radiologists are less burned out, and patient satisfaction scores are up 23%. Somedge's team understood healthcare compliance from day one." — Dr. Sarah Chen, Chief Medical Officer
Zero Security Incidents in $500M+ Payment Platform
Digital payment platform achieves 100% uptime and zero breaches with zero-trust architecture and AI-powered fraud detection.
Client Profile
- Industry: FinTech / Payments
- Transaction Volume: $500M+/year
- Users: 250K+ merchants
- Challenge: Legacy security, fraud losses
Timeline
- Security Audit: 2 weeks
- Architecture Redesign: 6 weeks
- Implementation: 12 weeks
- Total: 5 months
Technology Stack
- Zero-trust network (ZTN)
- ML fraud detection (real-time)
- Kubernetes on AWS
- Vault for secrets management
The Challenge
A fast-growing payment processor faced escalating fraud losses ($2.1M in one year) and failed a PCI-DSS audit due to security gaps in their legacy architecture. Their monolithic application had no network segmentation, shared database credentials, and lacked real-time fraud detection.
A single breach could cost them their merchant relationships and regulatory approval. They needed enterprise-grade security without rewriting their entire application.
Our Solution
Security Architecture Overhaul
- Zero-Trust Network: Implemented identity-based access controls with micro-segmentation
- Least-Privilege Access: Dynamic credentials with 1-hour TTL, no long-lived secrets
- Encrypted Everything: TLS 1.3 in transit, AES-256 at rest, including database backups
- Secrets Management: Migrated from env files to HashiCorp Vault with automated rotation
Fraud Detection System
- Real-Time Scoring: ML models analyze every transaction in <50ms
- Behavioral Analysis: Device fingerprinting, location anomalies, velocity checks
- Graph Networks: Detect fraud rings through entity relationships
- Adaptive Rules: Models retrain nightly on latest fraud patterns
Implementation Approach
Strangler Fig Pattern: We wrapped the legacy monolith with security layers while gradually extracting services into microservices. This allowed zero-downtime migration without a risky "big bang" rewrite.
Phase 1: Secure the Perimeter
- WAF & DDoS protection
- TLS termination & cert management
- Rate limiting & IP reputation
Phase 2: Internal Hardening
- Service mesh (Istio) for mTLS
- Secrets rotation automation
- Database encryption & audit logs
Phase 3: Observability
- SIEM integration (Splunk)
- Real-time anomaly detection
- Incident response playbooks
Results
0
Security Incidents
18 months post-deployment (was 12/year)
94%
Fraud Reduction
From $2.1M to $120K annual losses
100%
Uptime
Zero unplanned downtime in 18 months
"Somedge's team secured our platform without disrupting our merchants. The fraud detection system paid for itself in 3 months, and we passed PCI-DSS audit with zero findings. They understand payment security at a level most consultants don't." — James Park, CTO
60% Cost Reduction Through Multi-Cloud Migration
Global logistics company migrates 200+ legacy applications to multi-cloud architecture, cutting costs by 60% while improving performance.
Client Profile
- Industry: Logistics & Supply Chain
- Infrastructure: 800+ servers on-premise
- Applications: 200+ (mix of legacy & modern)
- Challenge: $12M/year infrastructure costs
Timeline
- Assessment: 3 weeks
- Pilot Migration: 6 weeks
- Full Migration: 9 months (phased)
- Total: 11 months
Technology Stack
- AWS (compute, storage)
- GCP (data analytics)
- Kubernetes (container orchestration)
- Terraform (IaC)
The Challenge
A global logistics company ran 200+ applications on aging on-premise infrastructure costing $12M annually. Hardware refresh cycles were approaching, requiring $8M capex investment. Performance was degrading (80%+ CPU utilization during peak), and disaster recovery was manual with 48-hour RTO.
Previous cloud migration attempts failed due to application dependencies, data residency requirements (EU/US/APAC), and fear of downtime impacting real-time shipment tracking.
Our Solution
Discovery & Planning (3 weeks)
- Dependency Mapping: Automated discovery of 200+ apps and 1,200+ database connections
- 6R Strategy: Categorized workloads (rehost, replatform, refactor, retire, retain)
- TCO Analysis: Built 3-year cost model comparing on-prem vs. cloud
- Risk Assessment: Identified high-risk dependencies and mitigation plans
Migration Waves (9 months)
- Wave 1: Dev/test environments (validate approach, zero business impact)
- Wave 2: Non-critical batch jobs (prove cost savings)
- Wave 3: Stateless web services (containerize & auto-scale)
- Wave 4: Databases & stateful apps (migrate with replication)
- Wave 5: Mission-critical tracking systems (blue/green deployment)
Multi-Cloud Strategy
We architected for cloud-agnostic workloads while optimizing for each provider's strengths:
AWS (Primary)
- Web applications (ECS, Lambda)
- Primary databases (RDS, DynamoDB)
- Object storage (S3)
- CDN (CloudFront)
GCP (Analytics)
- Data warehouse (BigQuery)
- ML pipelines (Vertex AI)
- Real-time streaming (Dataflow)
- Cost optimization (Spot VMs)
Hybrid (Compliance)
- EU data residency (on-prem)
- Legacy mainframe integration
- VPN mesh between clouds
- Unified monitoring (Datadog)
Zero-Downtime Migration
For mission-critical shipment tracking (handles 50K requests/sec peak), we used blue/green deployment with database replication:
- Replicate: Set up real-time replication from on-prem to cloud databases
- Parallel Run: Deploy cloud version, route 10% traffic for 2 weeks
- Cutover: Gradually increase to 100% over 3 days with instant rollback capability
- Decommission: Keep on-prem as warm standby for 30 days, then retire
Results
60%
Cost Reduction
$12M → $4.8M annual infrastructure spend
10x
Faster Deployments
Monthly releases → 50+ per day
0
Downtime
Zero customer-impacting incidents during migration
"Somedge executed a flawless migration of our most complex systems. The cost savings exceeded projections, and our engineering team now deploys features 10x faster. They didn't just lift-and-shift — they modernized our entire stack." — Maria Rodriguez, VP of Engineering
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