Case Studies
Real results from real clients. Explore our case studies to see how EFS Networks has helped businesses transform their technology and operations.
Custom WordPress Development for Axis Construction
EFS Networks built a custom WordPress theme and plugins for Axis Construction Management, delivering a full marketing website on AWS with client-managed content for projects, …
Cyber Security Audit Checklist for SMBs
A practical cyber security audit checklist for small and mid-size businesses covering access controls, endpoint security, network, data, and incident response.
Dual-Zone AI Architecture: Structurally Eliminating PHI Exposure
How EFS Networks designed an AI architecture where protected health information can never reach the foundation model — using deterministic tokenization, dual-zone isolation, and defense-in-depth …
EFS Networks Achieves Dual AWS AI Competency
EFS Networks achieves both the AWS Generative AI and Agentic AI competencies — among fewer than 65 partners worldwide with the Agentic AI designation.
GenAI Data Assistant for HR Analytics
How EFS DevOps built a GenAI-powered HR data assistant that enables natural language queries against workforce analytics.
HAM With Real Device Visibility
How a manufacturing company gained complete hardware asset visibility across 12 facilities using ServiceNow HAM.
HIPAA AI Clinical Decision Support System
How EFS DevOps built a HIPAA-compliant AI clinical decision support system on AWS using Bedrock, with end-to-end encryption and audit logging.
How Confidence Gating Makes AI Safe for Enterprise Decisions
How confidence gating prevents autonomous AI from making bad decisions in production — with EDI automation and HIPAA workflow examples from EFS.
How to Choose a Custom Software Development Partner
How to evaluate custom software development partners. Covers technical vetting, communication, pricing models, and red flags to watch for.
How We Built HIPAA-Compliant AI with Zero PHI Exposure
How EFS Networks built a clinical AI assistant that makes it structurally impossible for patient data to reach foundation models — and saved $5.7M annually.