Solutions Architect · AI Systems Operator · Platform Builder
I turn complex AI, SaaS, integration, and revenue workflows into systems people can operate.

Toronto, Canada · SaaS architecture · local-first AI · observability · automation · commercial systems

• Local-first AI control planes, inference routing, and GPU workflows
• Reliable SaaS backends with auth, tenancy, billing, webhooks, and data integrity
• Integration and identity architectures for complex business systems
• Operator tooling that turns infrastructure into a repeatable service
• Proof-first productization: documentation, packaging, checkout, fulfillment, and support

Hardonia operates a local-first AI systems lab and product platform. Runtime status is intentionally read from current operator evidence rather than hard-coded into a public README.

The public story is:

Observe → Control → Operate → Prove → Monetize

Current local truth is published through the authenticated Command Center endpoint /api/truth and the read-only operator artifact state/truth-latest.json. The envelope separates technical health, evidence freshness, commercial readiness, and provider-correlated payment evidence.

The commercial rule is strict: catalog rows, local purchase rows, synthetic tests, and delivery-looking records are not reported as realized revenue without independent provider correlation.

I operate a private, local-first AI lab and product platform. Its internal control plane, checkout API, audit API, compute lanes, and revenue database are intentionally private; the public profile links only to repositories and surfaces that visitors can actually open.

The operating loop is:

The design priorities are boring reliability, tenant and payment integrity, local privacy, observable operations, and small systems that reduce manual work.

The platform is an active local operating environment with public-facing components and private operator services. Readiness is reported per layer: technical runtime, catalog/artifact integrity, provider-backed payment evidence, fulfillment, and realized revenue are separate verdicts.

Inference fleet — Hardware-aware Ollama lanes and a router support local chat, code, vision, and embedding workloads. Current lane and model state is measured by the operator truth envelope rather than hard-coded here.

Image workflows — ComfyUI, custom nodes, documented workflows, and packaged workflow assets support repeatable local generation.

Checkout and fulfillment — Checkout, webhook verification, revenue classification, and fulfillment are implemented as separate components. Provider-correlated payment evidence is required before realized revenue is claimed.

Revenue operations — Purchase rows, leads, truth classifications, catalog state, and provider evidence are reconciled separately.

Compute API — Prepaid credit access supports bounded GPU jobs with API-key isolation and signed result delivery. Metered billing is not claimed in this release.

Proof layer — Evidence envelopes and deterministic audit artifacts make operational claims inspectable and freshness-aware.

Self-heal — Watchdogs and operator timers exist, but their aggregate state is reported honestly when failed units or contradictory probes are present.

The following repositories are active again because they form a coherent foundation for The Platform. They are reference implementations and capability surfaces, not claims that every route or deployment topology is production-ready.

• Requiem — native execution engine, operator console lineage, and deterministic control-plane experiments
• Reach — deterministic run, transcript, replay, and cryptographic evidence contracts
• ReadyLayer — AI-assisted software delivery governance, policy checks, provenance, and evidence export
• Zeo — local-first composable agent pipelines, signed module artifacts, and deterministic exports
• truthcore — Python verification, content-addressed caching, anomaly detection, and offline evidence reports
• JobForge — Postgres-native idempotent jobs, retries, backoff, and RLS-aware execution contracts
• MissionLedger — governed agent missions, policy boundaries, and proof-grade execution records

The enterprise fold is intentionally modular:

• verification and evidence → truthcore + Reach
• governed execution and durable jobs → Requiem + JobForge + MissionLedger
• AI-assisted delivery assurance → ReadyLayer + Zeo
• local-first runtime and hardware-aware routing → existing The Platform control plane, Ollama lanes, and ComfyUI services

Additional archived prototypes remain preserved while their useful contracts are extracted into the private platform rather than presented as live products.

• llm-inference-api — OpenAI-compatible local inference gateway patterns
• ollama-router — multi-lane local model routing
• Nautilus — deterministic operational AI infrastructure concepts
• comfyui-api — Cloudflare-facing ComfyUI integration work
• ControlPlane — control-plane exploration and operator architecture

• Settler — reconciliation intelligence for finance and operations
• TokenGoblin — AI spend and token-efficiency observability
• finops-autopilot — FinOps automation concepts
• reliability-platform — reliability-oriented platform work
• webhook-witness — webhook capture and inspection patterns

• identity-entitlement-broker — identity, entitlements, and policy boundaries
• enterprise-integration-fabric — governed integration architecture
• golden-path-platform — developer-platform and delivery guardrails
• commercial-architecture-simulator — experimental commercial modeling
• architecture-playbook — reusable architecture delivery notes

These public pages describe real artifacts in this repository. Availability, pricing, and fulfillment state are kept on the product page rather than overstated in the profile.

1. Discover the actual system and constraints.
2. Fix the smallest root cause.
3. Keep private infrastructure private.
4. Verify with real tests, endpoints, assets, and logs.
5. Separate technical readiness from commercial proof.
6. Document rollback and the next highest-leverage action.

• Connect with Scott Hardie on LinkedIn
• Visit the AI Automated Systems website
• View Scott Hardie's GitHub profile
• Email Scott Hardie

If you are building a serious AI, SaaS, integration, or operations system, start with a specific problem, a measurable outcome, and a verifiable path to production.