I'm Brayden Hord — I run engineering like a P&L. I set AI strategy at the org level, build the systems that execute it, and put a defensible dollar figure on work most companies call unquantifiable. On the side I build embedded electronics, run statistical tests on PRNGs, and break things for fun.
Every system my team ships carries a live financial model behind it. Value is decomposed into nine channels — revenue protected, client retention, media efficiency, error prevention, billing accuracy, risk avoided, labour returned, cost avoided, and new revenue — 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 — and some have matured into intellectual property with standalone value: a metered service that turned an internal tool into a billable product line, and a growing proprietary dataset that compounds in value as it accrues. A few of the quieter examples: 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, 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 driving 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.
Natural-language ad-ops agent built on Claude via AWS Bedrock with 36 custom platform tools. Takes raw emails and project-management input and executes media buys across DSPs and social platforms with full PM-system traceability. Shipped before agentic media buying went mainstream.
Privacy-preserving data pipeline. HMAC pseudonymization, three modes of sum-preserving numeric perturbation, and a Paillier homomorphic encryption module. Lets proprietary datasets meet third-party AI without leaking the underlying records or aggregates.
Ad Object Naming System, grounded in information theory: Shannon entropy and Kraft's inequality applied to identifier design. Embedded foreign-key Key Rings, User Tags, Variant Slots. Deployed across StackAdapt, Meta, LinkedIn, TikTok, and Google. Data integrity baked into the encoding layer.
Bridge between physical and digital signal. USPS Informed Visibility scan ingestion, Flask-based PURL system for personalized landing pages, and portfolio research across BLE beacons, conductive ink, and Digimarc. Closing the loop between drop date, mailbox arrival, and downstream behavior.
Standardized client-services → media handoffs across Meta, Programmatic, YouTube, Search, Native, X, LinkedIn, and Reddit into a single normalized schema and versioned spec. Recovered 62 weekly team hours and ended a decade of tribal-knowledge handoff drift.
AI QA bot in Slack: Claude API piped through Make.com, tuned to enforce QA culture across media ops without being annoying. Catches the small mistakes humans miss and lets the team move faster as a result.
Three ESP32 nodes communicating via ESP-NOW peer-to-peer, HC-SR04 ultrasonics, and a Waveshare 64×64 HUB75E LED matrix with weather display and pixel-art animations. Multi-node embedded with timing-sensitive matrix driving and sensor fusion. The "stranger machines" side of the house.
Cryptographically-honest password generator, Fisher-Yates
array shuffler, side-by-side Math.random() vs
32-bit xorshift visual plots, and a Game-of-Life background
you can stage fullscreen.
I lead engineering, AI, and innovation at Arena, 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: a distinct vector for engineering excellence next to traditional agency work.
I'm most useful at the intersection of three things — production AI systems that have to actually run, hard data and information-theoretic problems, and the human side of getting an entire 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, vendor-assessment rubric, 24-hour incident protocol), chair the AI and Automation committees, deploy the agents themselves — secured, governed, and monitored — and run the training that gets 100+ people actually 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.