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

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

Overview • Featured Projects • Active Systems • 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 — telemetry, traces, and audit trails built in
• Reliable — designed for degraded states and partial failures
• Traceable — every decision reconstructable from logs
• Operationally clear — runbooks, not runaway automation

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.

Additional production systems in active development — each addressing a specific operational domain.

• 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 • Normalization"]
    Matcher["Matching Engine<br/>Deterministic Rules + ML Assist"]
    Review["Human Review<br/>Exception Queue • Policy Gates"]
    Ledger["Verified Ledger<br/>Immutable • Auditable"]
    Audit["Audit Reporting<br/>Traces • Exports • Compliance"]

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

Goals:

• Deterministic matching logic with configurable rules
• Full auditability — every match traceable to source
• Traceable financial workflows with human checkpoints
• Extensible for new payment rails and data formats

flowchart TB

    Inputs["Inputs<br/>Events • Agents • Tasks • Schedules"]
    Kernel["Execution Kernel<br/>DAG Runtime • Idempotency • Retries"]
    Trace["Trace Engine<br/>Spans • Decisions • Artifacts"]
    Policy["Policy Layer<br/>Guardrails • Approvals • Budgets"]
    State["System State<br/>PostgreSQL • Event Store"]

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

Focus areas:

• Deterministic workflows with replay capability
• Execution traceability — every decision logged
• Governance layers: policies, budgets, approval gates
• Reproducible automation via event sourcing

flowchart LR

    Docs["Documents<br/>Web Data • APIs • Feeds"]
    Agents["AI Agents<br/>Specialized • Composable"]
    Review["Human Oversight<br/>Review • Approve • Redirect"]
    Output["Operational Systems<br/>APIs • DBs • Queues"]

    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 • shadcn/ui"]
    API["API<br/>Node.js • REST • Webhooks • tRPC"]
    Middleware["Middleware<br/>Auth • SDKs • Rate Limiting"]
    Data["Data<br/>PostgreSQL • Supabase • RLS • ClickHouse"]
    Infra["Infrastructure<br/>GitHub Actions • ArgoCD • Terraform"]
    Obs["Observability<br/>OpenTelemetry • 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++, Zig
Execution environments: WebAssembly (WASM), Node.js, Deno, Bun
Infrastructure: Kubernetes, PostgreSQL, Redis, ClickHouse, Supabase
AI/ML: LangGraph, OpenTelemetry, custom agent runtimes

• Reduce complexity before automating it — automation codifies; don't codify chaos
• Prefer observable systems over opaque abstractions — if you can't trace it, you can't trust it
• Design for degraded states — partial failure is the normal case
• Keep humans in the loop where judgment matters — approval gates, not just notifications
• Build systems that survive real-world conditions — network partitions, clock skew, bad inputs

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
• v5: extended active systems, deeper repo details, architecture diagrams, technical surface expansion