Michael DeCero

I take AI from prototype to production.

Governed semantic layers, whole-market data pipelines, multi-tool AI agents, and the security and observability that keep them accountable. Embedded inside your team, from architecture through deployment.

Enterprise embeddedAgents in productionFintech co-founder

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Proof of work

Numbers from systems running in production today.

70+

databases unified into one semantic layer

12,400

tickers scanned every trading day

120+

blocking checks on every pull request

tools shipped on a single agent platform

Selected work

Real systems, inside a Fortune-level enterprise and a fintech startup.

Fifteen case studies across data platforms, AI agents, and security, each with the architecture behind it. Client names stay under NDA.

Data Platforms70+databases unified

Enterprise Semantic Layer

Unified a sprawling operational data estate across a Fortune-level enterprise into governed semantic views. AI agents and analysts now query business data with traceable logic instead of guessing.

SnowflakeCortex AgentsSemantic ViewsDynamic Tables

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AI Agents & Runtimes84tools, one operator

Multi-Tool AI Agent Platform

Shipped an autonomous AI operator that lives in a company's Slack workspace, built on the open-source OpenClaw harness. 84 tools across 13 SaaS surfaces: it reads context, takes action, and reports back.

OpenClawTypeScriptNode.jsAzure Kubernetes Service

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Data Platforms186migrations, zero passwords

Market Data Ingestion and Warehouse

Built the data backbone for a fintech platform: eight external feeds cached and health-monitored, four identity-scoped batch jobs, and a PostgreSQL warehouse with roughly 180 tables that no human credential can open.

PostgreSQL 16TypeScriptNode.jsAzure Container Apps

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Security & Operations0public endpoints on data services

Private-Network Cloud Platform as Code

Took a startup's cloud estate from public endpoints to a fully private network defined in code: one audited door in through an edge firewall, one door out through a deny-by-default egress list that CI checks against the codebase, and about forty tripwires watching all of it.

BicepAzure Front Door and WAFPrivate LinkAzure Firewall

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How I work

Same pattern. Different domains. Always ships.

Map the reality

Understand the actual workflow, where decisions stall, where data breaks, what a human does today that an agent should handle tomorrow.

Build the data spine

Model facts, dimensions, and metrics so AI has stable, governed context it can query and trust.

Ship with guardrails

Production agents in real surfaces, Slack, email, calendar, the SaaS stack. Deploy with security, observability, evals, and audit, built to run in production from day one.

One person. Architecture to deployment.

If you need someone who can frame the opportunity, design the architecture, and ship real systems inside your enterprise, let's talk.

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