Healthcare.gov Stabilization
Led large-scale recovery initiative improving uptime, scaling infrastructure, and establishing operational playbooks.
Lee Consultants partners with leaders to turn complex technology challenges into scalable systems, architectures, and measurable business outcomes. From large-scale migrations and PCI-compliant architectures to applied GenAI — we design, build, and operationalize.
Focused, high-leverage engagements. Built to compound.
Design and remediation of enterprise-scale systems aligned to PCI DSS, cloud governance, and audit-readiness.
On-prem to cloud or hybrid migrations. Full-stack inventory, dependency mapping, and zero-downtime cutovers.
Turnaround programs for large-scale systems - resilience engineering, observability, and performance tuning.
PMO setup, governance models, and oversight for multi-stream initiatives. Proven methodologies for scope, schedule, and stakeholder alignment.
Practical roadmaps for AI adoption: use-case selection, data readiness, model lifecycle management, and guardrails.
Retrieval-augmented generation pipelines, knowledge bases, and copilots integrated with enterprise data.
Hands-on guidance for CTO/CIO/VP teams. Partner ecosystem design and capability growth acceleration.
Transparent communication and active co-creation. We bring clarity to complexity and momentum to execution.
We focus on real, operational deliverables - dashboards, scripts, playbooks, and reference architectures - not just PowerPoint.
Experienced practitioners who ship. Minimal overhead, rapid iteration, measurable value.
Every engagement ties back to clear metrics - stability, efficiency, compliance, or growth.
From siloed data to an adaptive intelligence fabric. Each stage compounds learning, efficiency, and impact.
AI Discovery & Experimentation
Identify and unify siloed data, establish visibility, and generate pilot opportunities.
AI Operationalization
Deploy pilots into production with data pipelines, governance, and feedback mechanisms.
AI Optimization & Scaling
Centralize data into hubs or fabrics, unify APIs, and scale AI capabilities enterprise-wide.
AI Autonomy & Intelligence Fabric
Evolve toward a self-learning enterprise where data, models, and decisions continuously adapt.
Improvement - Demonstration - Optimization - Transformation
Before building AI, build awareness. Lee Consultants helps enterprises see their full data, cost, and capability landscape - unifying it under a framework of autonomous data profiling, ethical intelligence, and continuous learning. This is how raw data becomes a self-improving AI Factory.
Identify every data-producing system - on-prem, cloud, SaaS, data lakes, APIs, IoT, collaboration, telemetry, and shadow IT. Map ownership, sensitivity, and connectivity.
Tie each data system to its financial footprint: hardware, software, licensing, vendor contracts, cloud utilization, and labor - exposing hidden redundancies and under-used assets.
Profile schemas, lineage, update cadence, retention rules, and inter-dependencies to understand how information flows.
Use AI-driven profilers to autonomously interpret and score datasets for semantics, quality, anomalies, and business context - building a living metadata layer.
Evaluate governance, access controls, sensitivity (PII/PCI/PHI), and lineage auditability to ensure responsible AI readiness.
Quantify value vs usage for each dataset and identify dark data (collected but unused).
Apply exploratory analytics and ML to surface correlations, trendlines, and operational signals.
Group insights into actionable AI/automation use cases aligned with strategic objectives - efficiency, CX, risk, growth.
Identify redundant or complementary datasets and design unified ‘fusion’ views for cross-domain intelligence.
Locate waste from reactive operations, duplicate storage, fragmented compute, or license inefficiencies.
Translate top waste items or insights into measurable AI pilots with clear ROI hypotheses.
Evaluate ethical impact, carbon footprint, and workforce effects before scaling AI.
Implement automated feedback on accuracy, adoption, and ROI; feed learned signals back into governance.
Harden validated pilots with MLOps and versioned deployment playbooks.
Institutionalize continuous learning so each cycle improves data quality, efficiency, and strategic foresight.
This is Stage 1 of our AI Factory model - where data, dollars, and decisions align to build a self-improving enterprise intelligence system.
Led large-scale recovery initiative improving uptime, scaling infrastructure, and establishing operational playbooks.
Delivered multi-year migration programs across industries - on-prem to cloud and hybrid modernization.
Built secure, auditable enterprise architectures for Fortune 500 payment systems, aligned to PCI DSS v4.0.
Developed local AI environments and retrieval pipelines to operationalize GenAI capabilities safely and efficiently.
Reach out with a challenge, an idea, or a target outcome. We’ll reply within one business day.