Designing operational platforms where architecture, product, and automation intersect.

Solutions Architect @ McGraw Hill
Canada • Platform Architecture • SaaS Systems • Automation Infrastructure

Overview • Featured Projects • Proficiencies • Architecture • Principles • Collaboration

I build systems at the intersection of:

product direction → platform architecture → operational automation

My focus is on systems that are:

• observable
• reliable
• traceable
• operationally clear

I optimize for real operations, not demo environments.

Current focus: deterministic AI execution, reconciliation infrastructure, and governance-first automation.

• building platform systems that prioritize explainability and operational safety
• designing automation that remains deterministic under production stress
• integrating product delivery with governance, policy, and auditability from day one

• when systems are growing fast but operational risk is rising
• when automation exists but reliability, traceability, or governance is weak
• when product, architecture, and execution need to align under one operating model

flowchart TB

Users["Operators<br/>Product Systems"]
AIAS["AIAS<br/>AI Workflow Architecture"]
Requiem["Requiem<br/>Execution Kernel"]
Settler["Settler<br/>Reconciliation Control Plane"]
External["External Systems<br/>Payments • APIs • SaaS"]

Users --> AIAS
AIAS --> Requiem
Requiem --> Settler
Settler --> External

Selected projects that best represent my architecture and operating model.

_{Tip: Start with Requiem + Settler for the clearest view of my governance-first systems approach.}

What this portfolio emphasizes: systems that can be operated, audited, and evolved safely under real production constraints.

• faster delivery without sacrificing governance
• deterministic execution over brittle automation
• auditable operations with clear failure paths
• practical architecture that supports product velocity

• explicit policy + guardrail layers before autonomy
• observability designed in (not retrofitted)
• reproducible deployment and verification workflows
• human escalation paths for high-risk decisions

flowchart LR

Sources["Data Sources<br/>Bank • ERP • Payment APIs"]
Ingestion["Ingestion<br/>ETL • Webhooks"]
Matcher["Matching Engine"]
Review["Human Review"]
Ledger["Verified Ledger"]
Audit["Audit Reporting"]

Sources --> Ingestion
Ingestion --> Matcher
Matcher --> Review
Review --> Ledger
Ledger --> Audit

Goals:

• deterministic matching logic
• auditability
• traceable financial workflows
• human review checkpoints

flowchart TB

Inputs["Inputs<br/>Events • Agents • Tasks"]
Kernel["Execution Kernel"]
Trace["Trace Engine"]
Policy["Policy Layer"]
State["System State"]

Inputs --> Kernel
Kernel --> Trace
Kernel --> Policy
Trace --> State
Policy --> State

Focus areas:

• deterministic workflows
• execution traceability
• governance layers
• reproducible automation

flowchart LR

Docs["Documents<br/>Web Data"]
Agents["AI Agents"]
Review["Human Oversight"]
Output["Operational Systems"]

Docs --> Agents
Agents --> Review
Review --> Output

Goal:

AI systems that remain observable, governable, and operationally safe.

flowchart LR

UI["UI<br/>React • Next.js • Tailwind"]
API["API<br/>Node.js • REST • Webhooks"]
Middleware["Middleware<br/>Auth • SDKs"]
Data["Data<br/>Postgres • Supabase • RLS"]
Infra["Infrastructure<br/>CI/CD"]
Obs["Observability<br/>Logs • Verification"]

UI --> API
API --> Middleware
Middleware --> Data
API --> Infra
Infra --> Obs

Primary: TypeScript/JavaScript, Python, SQL, Go, HTML/CSS, Bash
Systems familiarity: Rust, C++
Execution environments: WebAssembly (WASM), Node.js, Deno, Bun

• reduce complexity before automating it
• prefer observable systems over opaque abstractions
• design for degraded states
• keep humans in the loop where judgment matters
• build systems that survive real-world conditions

If a system cannot be debugged, explained, or recovered, it probably is not ready to ship.

If you're building platform-heavy products or AI-enabled operational systems, I’m always open to exchanging architecture notes and practical implementation patterns.

• GitHub discussions/issues on relevant repos
• Connect here: github.com/Hardonian

• v1: clarity and structure upgrade
• v2: narrative + credibility polish
• v3: production-grade framing, scanability, and strategic positioning
• v4: conversion optimization, decision-context framing, and collaboration pathing