02 · The work
Enterprise AI Systems
I build AI systems that do real work inside regulated enterprises—where cost, latency, quality, auditability, integrations, and user trust all have to survive contact with production.
Production evidence
From workflow to dependency
Document processing
5,000+ documents annually
Multi-model orchestration using Claude, Gemini, MLflow, and Databricks achieved 95%+ accuracy and reduced turnaround from days to minutes.
Enterprise integration
60+ business units
An AWS serverless platform connected Calendly, Microsoft Graph, and Salesforce with 99.9% uptime and sub-second latency.
Model routing
40% lower cost
Providers are routed by document class, field risk, latency budget, confidence threshold, and measured evaluation performance.
Data platform
3,000+ clients
Databricks, PySpark, and Snowflake pipelines operate across gigabyte-to-terabyte scale data.
Deployment pattern
Reliability scaffolding
01 · DiscoverRisk, latency, data boundaries, owner, and failure modes.
02 · PrepareNormalize inputs and retrieve controlled context deterministically.
03 · ExecuteRoute models by measured fitness with bounded retries and persisted state.
04 · EvaluateLog decisions, test regressions, and add human review for high-risk actions.
Production is an operating condition, not a demo milestone. A durable system needs golden test fixtures, correlation IDs, latency/error/cost telemetry, explicit failure states, and rollback paths.