DeCeroAI Solutions

Portfolio

From ambiguity to production.

Enterprise and startup systems, delivered. Data spines, whole-market pipelines, multi-tool agents, and the security and observability that keep them honest. Every card opens a case study with an architecture diagram. Client names protected, patterns and outcomes exposed.

15case studies
70+databases unified
12,400tickers scanned daily
84tools, one operator

Field Notes

From Laptop Prototype to Private Production Platform in Four Months

Two Python scripts on a laptop became a paid, private, audited platform between June and October. The code was rewritten; the discipline is what carried over. Here is the arc, with dates.

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Data Platforms 7

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

Chart-Pattern Engine, Validated by Backtest

Rebuilt a chart-pattern detector with strict, volatility-scaled gates, then ran 347,000 look-ahead-free evaluations over eight years to test whether the patterns predict price. They do not, so the product claims were narrowed to match.

TypeScriptNode.jsDaily OHLCV barsBacktest harness
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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

Ready to build?

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

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