Four recoveries and one responsible-AI build. Different industries, different ambitions, same discipline.
Merger of hundreds of locations. 100+ redundant modules. Fragmented data. No unified patient record. Costs escalating faster than the integration roadmap.
Diagnostic across merged workflows and architecture. Start–Stop–Continue rationalization. API-led integration. Governance framework established with named executive owners.
Multi-year enterprise transformation stalled. Siloed ownership. Unclear metrics. Heavy contractor dependency. Value realization lagging every quarterly review.
Transformation council established. End-to-end architecture audit. Data foundation rebuilt. Shifted the delivery model to POC-first testing and validation.
Five consecutive holiday collapses. Executive credibility eroding. Each year the post-mortem blamed a different layer; each year the pattern repeated.
Architecture triage. Performance re-engineering. Governance reset with load-test milestones owned by a single accountable executive through peak season.
Two weeks to launch. Multi-million legal exposure if the date slipped. Toxic program environment: no single owner, no credible plan, no confidence in the vendor.
Migration bottleneck identified and automated. Performance tested and tuned. Around-the-clock sprint to launch. War-room execution under embedded senior leadership.
Compliance-first fintech platform using live financial data. Needed AI that could guide consumer credit behavior while protecting privacy, auditability, and regulatory readiness.
Stratium signal: Responsible AI was embedded at the architecture level. Governance, controls, validation, auditability, and secure data handling were designed before scale, not retrofitted later.
Designed and built a governed AI architecture: policy-gated workflows, prompt controls, response validation, audit logging, and backend-mediated LLM execution.
Production-grade AI foundation established. SOC2-aligned controls, secure data pipelines, DevOps automation, and AI inference integrated into core decision flows.
AI accelerated architecture, documentation, implementation planning, and governance design with a lean founder-led team.
The result: enterprise-grade AI discipline delivered at startup speed.