Featured · case study
PrivateStack — 0 → production in eleven weeks.
Secure multi-tenant LLM SaaS for regulated industries. 66 endpoints, 99.9% uptime, full IP transfer. Architecture, timeline, and outcomes from an eleven-week build.
Case studies and applied frameworks from production engagements — trust architecture, fraud detection, predictive maintenance, healthcare AI. Written by the people who built them. Outcomes, not adjectives.
Secure multi-tenant LLM SaaS for regulated industries. 66 endpoints, 99.9% uptime, full IP transfer. Architecture, timeline, and outcomes from an eleven-week build.
A structured methodology for stress-testing LLM and agent systems against prompt injection, tool abuse, and data exfiltration before they ship—mapped to OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF.
A reference architecture for standing up a production AI system from zero — requirements through handoff — drawn from real engagements: multi-tenant isolation, JWT/JWKS auth at the gateway, managed in
The reference architecture behind a system that turns a firehose of federal solicitations into a scored, searchable opportunity store and a daily Go/No-Go digest for business development.
Five anonymized failure modes from real engagements — the data problem wearing an AI costume, the missing eval harness, the governance gap, the pilot with no path to production, and security as an aft
A practitioner framework for gating enterprise RAG releases on retrieval and answer quality—golden datasets, the right metrics, calibrated LLM judges, and CI regression gates that block bad chunking b
The reference architecture behind a production multi-tenant LLM SaaS platform delivered in roughly eleven weeks, with hard tenant isolation, JWT-at-the-edge authentication, and a clean IP handoff.
How we designed a production-ready clinical documentation system that reduces physician documentation time by 50% while maintaining full HIPAA compliance.
A production-ready fraud detection architecture designed for sub-second response times, regulatory compliance, and continuous model improvement.
A production-ready predictive maintenance architecture that reduces unplanned downtime by 40% and maintenance costs by 25% through edge AI and IoT integration.
This paper provides practitioners with evidence-based frameworks for building, maintaining, and rebuilding trust in human-AI relationships, with specific focus on the unique challenges faced by organi
This guide will help you navigate the complex world of human-AI relationships with realistic expectations, practical strategies, and healthy skepticism.
A look at the hidden obstacles that cause the majority of AI projects to stall — and a roadmap for breaking through.
An examination of the critical factors behind AI implementation failures, with actionable insights for successful adoption.