Professional Systems
Enterprise AI systems from real delivery, translated into public-safe architecture.
A sanitized map of professional healthcare, insurance, claims automation, document intelligence, predictive modeling, FWA analytics, and MLOps patterns. The emphasis is system shape: intake, validation, review, deployment, and measurable operational impact.
Selected outcomes
Operational results, expressed without PHI, PII, employer-confidential data, or proprietary workflows.
Systems atlas
Six sanitized enterprise patterns, organized by problem, flow, stack, and outcome.
Each row shows the public-safe shape of a real delivery pattern: what entered the system, how it was validated, where human review stayed visible, and what operational signal it supported.
Enterprise systems are described as sanitized architecture patterns. Public examples contain no PHI, PII, employer-confidential data, proprietary claims data, private keys, production credentials, or sensitive datasets.
Claims automation
Claims Document Automation / Autonomous Adjudication Support
LangGraph-style orchestration for document understanding, structured extraction, validation gates, exception routing, and downstream claims-system handoffs.
90% review-time reduction patternClaims review acceleration and safer automation boundaries without exposing employer data.
document intakeOCR/extractionagent graphvalidation gatesexception routingclaims handoff
LangGraph patternsAzureADLSCosmos DBRedisAKSSynapseobservability
Healthcare quality
HEDIS Evidence Extraction Pipeline
Quality-measure evidence workflow over HL7 MDM/ORU-style documents, base64 PDFs, OCR, rule validation, human review, and closure tracking.
20% automated closure liftAutomated closure improvement while preserving reviewer accountability.
HL7 MDM/ORUbase64 PDFOCRmeasure rulesevidence extractionhuman review
PyMuPDFTesseractFHIR/HL7rules enginereview queuesPower BI
Private document AI
On-Prem RAG/OCR Compliance Review Microservice
Private inference workflow for regulated document review using local SLMs, retrieval, OCR, containerized services, and analyst-facing review surfaces.
7K backlog clearance patternReview-time reduction and backlog clearance without moving private documents into public demos.
local document storeOCRchunkingretrievalGemma/Ollama/vLLMreview UI
PodmanStreamlitOllamavLLMRAGSLMsprivate inference
Predictive healthcare
Readmission / LOS / ED Utilization Predictive Models
Healthcare predictive modeling across claims, ADT events, demographics, diagnoses, medications, SDoH, risk scores, and operational features.
Utilization prioritizationCare-management prioritization, utilization insight, and governed model delivery patterns.
claims + ADTfeature storeXGBoost/stat modelsrisk outputsPower BIoperational handoff
PythonSQLXGBoostscikit-learnMLflowPower BICI/CD
FWA analytics
FWA Transportation Anomaly Detection
Fraud, waste, and abuse analytics over multi-leg trips, utilization anomalies, geospatial validation, and proactive review workflows.
18% waste reductionWaste reduction through targeted, explainable review signals.
trip eventsmulti-leg featuresESRI validationanomaly scoringreview queuewaste-reduction reporting
PythonSQLESRIanomaly featuresPower BIworkflow analytics
Healthcare analytics product
Healthcare Analytics Platform / Health Index
0-to-1 healthcare analytics platform work using composite risk scoring, care gaps, SDoH features, quality analytics, and embedded delivery.
$500K new revenue / $3M P4P impactNew revenue and client impact through operational healthcare analytics products.
member datarisk scoringcare gapshealth indexembedded dashboardcare-manager workflow
PythonSQLPower BI EmbeddedAzurerisk modelsanalytics product