Featured · case study
PrivateStack: 0 → production in eleven weeks.
Secure multi-tenant LLM SaaS for regulated industries. 66 endpoints, tenant isolation, and full IP transfer. Architecture and delivery lessons from an eleven-week build.
Case studies and applied frameworks from production engagements, secure multi-tenant LLM platforms, federal opportunity intelligence, healthcare AI, and control architectures for regulated programs. Written by the people who built them. Outcomes, not adjectives.
The proof model is deliberate: anonymized client context where permission is limited, shipped-system detail where artifacts can be shown, and public frameworks where the lesson matters more than the logo. This is where we surface the regulated proof we can show publicly: healthcare workflows, federal pipeline design, and regulated-SaaS control architecture. If a reference is public, we say so. If it is not, we do not pretend otherwise.
How to read the evidence: Anonymized engagement evidence describes delivered artifacts without identifying the client. DSE-owned reference implementations show systems we can inspect directly. The practitioner qualifications listed here are earned credentials such as CISSP and Databricks Certified Data Engineer Professional, not company certification or vendor endorsement.
Secure multi-tenant LLM SaaS for regulated industries. 66 endpoints, tenant isolation, and full IP transfer. Architecture and delivery lessons from an eleven-week build.
A DSE-owned reference implementation showing how public-source data moves through immutable capture, fail-closed quality gates, Databricks Bronze and Silver, and traceable analytical delivery.
An anonymized case pattern showing how a buyer moved from AI policy approval to a working retrieval-and-review workflow with controls, evaluation, and handoff.
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 infe
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 afte
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
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.
60% of fraud-detection AI never ships. This MLOps framework hits sub-100ms decisions, explains every call for regulators, and handles model drift.
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.