Dan Hill · Compliance Systems & Data Specialist

Understand the requirement.Understand reality.Build the improvement.

I work in food manufacturing compliance, turning complex requirements and manual processes into structured, reliable and auditable systems using data, workflow automation and better process design.

I approach technology as a practical tool for solving operational problems rather than as an objective in itself.

Outside work · self-directed projects, counted 29 Sep 2026

  • 38n8n workflows running on a schedule or trigger88 in the instance, counted 29 Sep 2026
  • 14services in Docker on one Raspberry Pi 58 GB of RAM, one box, reached through a Cloudflare Tunnel
  • 300places in a 1926 town that simulates itselfArkham: 128 investigators, 42 districts, running in real time
  • 178nodes in a café's social media workflowRTD Master V1: events, posters, captions, posting, metrics

01 · Professional

Compliance Systems & Data Specialist

Food Manufacturing · Compliance · Data · Automation · Process Improvement · Auditability

I am a food manufacturing compliance professional specialising in the intersection between compliance, data and operational systems.

My work focuses on turning complex requirements and manual processes into structured, reliable and auditable systems. I combine practical knowledge of food manufacturing compliance with strong capability in data management, workflow automation, reporting and digital process improvement.

Food Manufacturing Compliance

  • Compliance systems and processes
  • Non-conformance management
  • Complaints and investigations
  • Risk assessment
  • Specification management
  • Allergen controls
  • Audit preparation and evidence
  • Corrective and preventative actions
  • Multi-site compliance processes

Data & Systems

  • Data structure and governance
  • Microsoft 365
  • SharePoint
  • Microsoft Lists
  • Power BI
  • Power Automate
  • Excel and advanced reporting
  • Workflow design
  • Management information
  • Automated reporting

Process Improvement

  • Identification of inefficient manual processes
  • Workflow redesign
  • Digital transformation of manual processes
  • Exception handling
  • Process controls
  • Standardisation across sites
  • Continuous improvement
  • Designing systems around the people who use them

02 · Outside work

Five ways into the same person.

Everything from here down is self-directed project work, done in my own time. It isn't employment. Projects & Development explains why I do it.

Software that runs whether anyone is watching or not

A Node server that steps a whole town forward every few seconds and catches up after an outage. A Discord bot that opens a channel for each new investigation. Render services for voice, video and podcasts. This site, with its own analytics backend.

Evidence

03 · Projects

Things I've built that are actually running.

Self-directed projects. Every number here comes from the running system, counted in September 2026. Nothing is a mock-up.

LiveSoftware · AI · Media

In-house media services

Voice cloning, animated video rendering, vertical Shorts and podcast rendering, all as small HTTP services on the Pi.

Project details
LiveAutomation · Web · Data

ATechStudio: small-business systems

Websites, contact backends, email-triggered site updates with an audit trail, inbox triage and mail merges.

Project details
LiveSoftware · Data · Infrastructure

This site

A portfolio with its own privacy-conscious analytics backend, admin panel, n8n reporting and Google Sheets data feed.

Project details

04 · How I build

A loop, not a waterfall.

The same nine moves show up in every project. Pick a step to see where it happened.

01 / 09

Name the problem

Say what actually goes wrong, and for whom. 'Social media is hard' doesn't count. 'Past events keep getting promoted, and posters cost money every time' does.

For real: RTD: expired events were still being advertised. Roll The Dice Café: event and social automation

  • Build the smallest version that actually works, then improve that.
  • AI is one part of the system, and it doesn't get to make the final decision.
  • Nothing heavy starts just because a clock fired.
  • A failure should always make a noise, never pass silently as success.
  • If a workflow can do something twice, it will. Make it idempotent.

05 · AI

AI is a tool. The question is what you can build with it.

I don't use AI for the sake of saying I use it. It's a multiplier: it lets one person run what used to need a small team. The question I ask is what I can build with it, and what has to stay outside it.
ThinkingClaude · Gemini

Talking a design through, auditing a system, finding the one line that broke everything. Claude Code does a lot of the investigative legwork on the Pi.

Seen in: Credential audit, crash investigation, Pi Ops audit

BuildingClaude Code

AI-assisted engineering with me in charge: I set the constraints, review the output and insist on tests. Arkham's server, the Discord bot and this site were built that way.

Seen in: Arkham, Discord bot, this site

Automationn8n · Gemini nodes · webhooks · REST / GraphQL APIs

AI steps sit inside workflows with validation around them. The server checks a Gemini pack before anything goes live, and a failed image is skipped, not fatal.

Seen in: Arkham monthly update, RTD captions, content pipelines

CreationGemini image models · Chatterbox voice cloning · Piper · ffmpeg

Portraits, Polaroids, posters, scene art, narration in my own cloned voice, animated video, podcast episodes and clips.

Seen in: Arkham, RTD posters, Ninth Volume, podcast

AnalysisGoogle Sheets · SQLite · CSV exports · Microsoft Copilot

Post metrics fed back into prompts, chronicle exports, resource diaries. The TFCD channel teaches compliance teams to use Copilot for audit evidence.

Seen in: RTD metrics loop, Arkham chronicle, TFCD

DeploymentRaspberry Pi 5 · Docker · Cloudflare Tunnel · Tailscale

Self-hosted, including the AI services where that's practical: TTS and voice cloning run locally, and only generation calls go out to an API.

Seen in: Pi Ops, media services

What AI doesn't get to do

  • AI never decides whether something is published. Code or I do.
  • Anything AI generates is validated before it can reach a live system.
  • Spending is capped and idempotent: generate once, then reuse.
  • Where rules work, I use rules. The inbox sorter is deliberately not AI.

06 · Architecture

How the ecosystem fits together.

One Raspberry Pi, one orchestrator, and a set of services that each do one thing. Click any part to see what it does and where I use it.

People

Edge

Applications

Orchestration

AI & media

Data & reporting

07 · CV & contact

Got a problem that should be a system?

I'm most useful where there's a real operational problem and a willingness to try something. I'm happy to talk about roles, projects, or building something for your business.

Curriculum vitae

Compliance Systems & Data Specialist: food manufacturing compliance, data, systems and process improvement.

[email protected]

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