DeCeroAI Solutions
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Portfolio

From ambiguity to production.

Enterprise and startup systems, delivered: data spines, whole-market pipelines, multi-tool agents, and the security and observability around them. Each case study carries its architecture diagram. Client names stay under NDA; the patterns and outcomes are the point.

14case studies
70+databases unified
12,400tickers scanned daily
84tools, one operator

Field Notes

Making an AI-Assisted Platform Commercial Grade

Two founders write most of the code with AI coding agents, and the platform takes payments and holds member data. These notes catalog the controls that make it safe.

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Data Platforms 6

Data Platforms

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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Data Platforms

Document Intelligence and Ingestion APIs

Built ingestion pipelines and REST APIs that turn semi-structured documents, forms, technical exports, and meeting transcripts into queryable data the AI stack consumes.

Azure FunctionsAzure Document IntelligenceAzure Blob StoragePython
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Data Platforms

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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Data Platforms

Whole-Market Scanner and Technicals Sweep

A live screener over the whole US equity market with 49 preset scans and 56 filters. A daily batch rebuilds a year of technical indicators for every ticker in about 260 API calls.

TypeScriptPostgreSQLMarket data REST APIAzure Container Apps
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Data Platforms

Tamper-Evident Performance Archive

Every published call is priced from the market tape rather than the post's claimed price, outcomes are computed from daily bars, and an append-only audit log enforced by the database means losing calls can never be quietly removed.

Python 3.11PostgreSQLpsycopg with Entra tokensSQLite test backend
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Data Platforms

Data Observability: Feed Health at Scale

Every external feed, sweep, and scheduled job leaves evidence. A sampler judges each one every five minutes against its own freshness rule, draws 90 days of history, and emails once when a feed goes red and once when it recovers.

TypeScriptPostgreSQL advisory locksApplication InsightsAzure Monitor
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AI Agents & Runtimes 4

AI Agents & Runtimes

Self-Service Analytics Agent

Packaged a governed semantic layer into a natural-language analytics tool. Non-technical users ask questions in plain English and get answers routed to the right domain expert, with no SQL required.

Claude Code pluginsSnowflakeCortex AnalystSemantic Views
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AI Agents & Runtimes

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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AI Agents & Runtimes

MCP Server with OAuth 2.1

A production Model Context Protocol server that lets subscribers plug a fintech data platform into Claude, ChatGPT, or their own agents: 21 curated research tools behind a standards-compliant OAuth 2.1 authorization server.

Model Context ProtocolOAuth 2.1TypeScriptNext.js
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AI Agents & Runtimes

AI Content Engine with a Human Approval Gate

Turns a stream of public market chatter into original, compliance-checked stories with branded charts. Nothing reaches an audience until a human approves it in Microsoft Teams, and every approval is priced into the performance record automatically.

Python 3.11Azure Bot ServiceAdaptive CardsAnthropic Claude
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Security & Operations 4

Security & Operations

Enterprise AI Connectors

Rolled out AI connectors across ChatGPT Enterprise and Claude Enterprise with encryption boundaries, auditability, and least-privilege access for a large enterprise.

ChatGPT EnterpriseClaude EnterpriseEnterprise Key ManagementSnowflake OAuth
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Security & Operations

Agent Security Architecture

Designed and documented the security model for an AI agent with private data access, external egress, and untrusted input, the worst-case surface for prompt injection. Controls that hold up.

Threat modelingAzure FirewallAzure Key VaultAzure Monitor
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Security & Operations

AI Agent Observability

Built operational observability for an LLM agent in production: heartbeat, users served, top tools, spend, errors, and deploys. Most teams ship the agent and then discover they have no idea what it is doing.

Azure Kubernetes ServiceLog AnalyticsKQLPython
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Security & Operations

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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Delivery pattern

How every engagement ships.

The same architecture spine shows up across analytics, market data, document intelligence, and multi-agent workflows.

Source Reality

APIs, spreadsheets, PDFs, market feeds, identity systems, tribal knowledge

Semantic Spine

Facts, dimensions, metrics, quality rules, queryable interfaces

Agent Runtime

Purpose-built tools, shared context, bounded autonomy

Operational Loop

Logging, evals, alerting, health checks, human review

Architecture principles

Production systems that hold up in the real world.

Parity

If a human can inspect and operate it, the system should too, under guardrails.

Granularity

Small, explicit tools beat giant black-box automations.

Composability

New workflows by recombining stable blocks.

Observability

Production AI needs logs, health checks, and eval loops.

Technical breadth

The stack behind the work.

I build across the full stack, from data platforms to AI runtimes to frontend applications to the security and observability layers that make agents safe to run.

Data & Analytics Engineering

SnowflakePostgreSQLPythonSQLDynamic TablesSemantic Modeling

AI & Agents

ClaudeOpenClawAnthropic APIOpenAICortex AgentsModel Context Protocol

Cloud & Infra

Kubernetes (AKS)Container AppsBicepAzure FirewallKey VaultDocker

Integration

REST APIsWebhooksMCP ServersOAuth 2.1Slack Socket ModeGoogle Workspace API

Frontend & Apps

ReactNext.jsNode.jsTypeScriptMapboxVite

Security & Observability

Threat ModelingAction FirewallsEgress AllowlistingAzure MonitorKQLDefender

Bring the problem. I bring the build.

Best fit for teams that need one person who can bridge product sense, data architecture, and implementation speed.

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