I'm Brayden Hord. I run engineering, IT, and AI at Arena, and I run them like a P&L: every system my team ships carries a value model, the model gets audited, and nothing counts until real people are using it. After hours I build embedded electronics, Chrome extensions, and a game that plays itself.
A small group of engineers who own their work end to end, from spec to on-call. Twenty-five systems in production. The rule is simple: it isn't done until it runs for real users, is valued honestly, and the people it was built for actually use it.
25 systems live · 4 AI models in productionI wrote the enterprise AI policy, chair the AI and Automation committees, chose and rolled out Claude Enterprise and OpenAI Enterprise for 100+ staff, run the training, and ship the agents myself: Claude on AWS Bedrock in production, and local models (CLIP, Ollama) wherever the data can't leave the building.
Claude · OpenAI · Bedrock · local modelsIAM across AWS, GCP and Google Workspace, hardware tokens, quarterly security training, a vendor-assessment rubric (SOC 2, ISO 27001, breach notification, training-data opt-out), and a 24-hour incident protocol. Boring on purpose.
AWS · GCP · Workspace · hardware keysA nine-channel value model sits behind every system: revenue protected, retention, media efficiency, errors prevented, billing accuracy, risk avoided, labour returned, cost avoided, new revenue. COGS, margin and EBITDA per build. When an audit says a headline number is too high, it comes down.
$1.2M realized · every dollar a formula176 staff hours a week returned, about 4.4 FTE. A daily compliance sweep across roughly 1,400 ad accounts that catches committed media running dark. A four-hour reconciliation loop across eight platforms that flags money moving with no sold line behind it. A build-vs-buy that replaced a $50K/yr vendor feature with a script.
176 hrs/week · 1,400 accounts swept dailyTwo productized offerings shipped, an internal tool turned into a metered, billable service, $250K+ in technical consulting sold from a sales-engineer seat, and a proprietary dataset that gets more valuable every month it accrues. Client-driven and revenue-bound, or it doesn't get built.
2 products shipped · $250K+ consulting soldEvery system my team ships carries a live financial model. Value is split into nine channels, and every dollar is a formula pointed at auditable inputs, each rated for confidence. No asserted totals, anywhere. The most recent audit of that model revised my own headline number downward, on purpose. A figure you can defend in front of a CFO beats a bigger one you can't.
That discipline changes what gets built. Builds are weighed the way finance weighs them: what they do to COGS, what margin they protect, what they contribute to EBITDA. Some have grown into intellectual property with standalone value, like a metered service that turned an internal tool into a billable product line, and a growing proprietary dataset that compounds as it accrues. A few of the quieter examples: the daily compliance sweep, the reconciliation loop, and a privacy toolkit that lets client data meet third-party AI with cryptographic guarantees instead of good intentions.
None of it is a solo act. I lead a tight-knit team of engineers who own their systems end to end, and the operating rule is that nothing counts until it's running in production for real users, valued honestly, and adopted by the people it serves.
Shows what every ad pixel on a page sends, proves whether tracking stops when a visitor says no, follows an ad click through to the purchase, and tests new pixel code in a Safe mode that never sends a real event. Made for anyone who ships tracking and wants proof it behaves.
Watches the header-bidding auction happen in your browser: every Prebid bidder and price, including the bids that lost, the creatives behind them, the IDs you've been assigned, and the targeting that was sent. Then it lets you change that targeting and prove a line item serves. First of its kind.
Keeps a MacBook awake with the lid closed and no monitor attached, then turns itself off the moment you unplug, so it never cooks in a bag. One obscure system flag plus the guardrails that flag badly needs. Swift, no dependencies, free.
Hand it the email thread and the project board and it places the media buy across DSPs and social platforms, writing every action back to the PM system so nothing happens off the record. Built on Claude through AWS Bedrock with 36 custom platform tools, and shipped before agentic media buying was a category. What it changed: a buy that took a planner an afternoon of copy and paste now takes a review and a click, and the audit trail is better than the manual one was.
A search engine for a library of thousands of mail pieces and digital ads. Describe what you need in plain English and it returns the closest matches, ranked by a CLIP model that runs on the box itself, so no creative ever leaves the network. Facet filters, PDF rendering, ZIP and slide-deck export, and a usage monitor so adoption is a number, not a feeling. Runs on a spare Mac on the LAN or a $22-a-month cloud box.
A privacy layer so proprietary data can meet third-party AI without handing it over. HMAC pseudonymization, three modes of sum-preserving numeric perturbation, and a Paillier homomorphic encryption module, with documentation that explains the guarantees to leadership in plain terms. The point: use the vendors without leaking the records or the aggregates.
An Ad Object Naming System with information theory behind it (Shannon entropy, Kraft's inequality), so identifiers carry their own integrity. Embedded foreign-key Key Rings, User Tags, Variant Slots. Live across StackAdapt, Meta, LinkedIn, TikTok, and Google, which means reports join cleanly instead of by hand.
Closes the loop between a mail drop and what happens online. USPS Informed Visibility scan ingestion, a Flask PURL system for personalized landing pages, and a research portfolio across BLE beacons, conductive ink, and Digimarc. Physical spend finally shows up in the same dashboard as digital.
One normalized schema and versioned spec for client-services to media handoffs across Meta, Programmatic, YouTube, Search, Native, X, LinkedIn, and Reddit. Recovered 62 team hours a week and ended a decade of tribal-knowledge drift.
A QA bot in Slack: the Claude API behind Make.com, tuned to hold a QA standard across media ops without being annoying about it. It catches the small mistakes people miss, and the team moves faster because someone is always checking.
Three ESP32 nodes talking over ESP-NOW, HC-SR04 ultrasonics, and a Waveshare 64×64 HUB75E LED matrix with a weather display and pixel-art animations. Timing-sensitive matrix driving and sensor fusion across nodes. The "stranger machines" side of the house.
A password generator that is honest about its randomness, a
Fisher-Yates shuffler, Math.random() plotted next to
32-bit xorshift, a NIST-style RNG testing kit, an F1 reaction
timer, and Elemental Arena, a bouncing-orb battle game where
every match replays exactly from a seed.
I lead engineering, IT, AI, and innovation at Arena (arenawins.com), a digital advertising firm with deep political-cycle exposure and an unusually broad surface area: DSPs, social, CTV, direct mail. The role was created on purpose, as a distinct vector for engineering next to traditional agency work. That is my day job, and the ArenaWins link in the header goes there. Elemental Arena, elsewhere on this site, is a game about orbs and has nothing to do with it.
I'm most useful where three things meet: production AI that has to actually run, hard data and information-theory problems, and the human work of getting a whole organization to adopt new machinery. I own the AI strategy end to end. I authored our enterprise AI Policy v2.0 (7 core principles, 16-category Universal Data Restrictions, a vendor-assessment rubric, a 24-hour incident protocol), chair the AI and Automation committees, deploy the agents myself, secured, governed, and monitored, and run the training that gets 100+ people using them. Strategy on paper is cheap. I'm accountable for the execution.
Outside work I build embedded electronics, run statistical tests on PRNGs because it's fun, contribute to local political campaigns, and keep a homelab in northern Utah running on more YAML than is strictly defensible.
