Bjorn David Hansen·Forefront of AI marketing enablement
Hi, I'm Bjorn.

I hit the numbers. I build the tools that hit them with me.

Senior digital marketing manager at a nine-figure ecommerce business — driving a major share of paid-media revenue at goal-exceeding efficiencies. The way I do that, efficiently and repeatably, is by building the tools, agents, and validated AI workflows that hit the numbers with me. The wins get shared back across the digital marketing department, so every operator's tide rises together. I work at the forefront of AI marketing enablement via a probabilistic-to-deterministic methodology — chat experiments become versioned, guardrail-protected production tools.

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Bjorn David Hansen
Seven-figure
Monthly paid media budgets across Google Ads, Microsoft Ads, and YouTube
Hundreds of thousands
SKUs in the ecommerce catalog the program serves
Strong YoY
Revenue and order growth in 2026, with improving marketing efficiency
Production AI tooling
Governed AI workflows and internal tools running in real marketing operations

The pattern

Three moves, same operator. The throughline behind every quarter that hits and every tool that gets shipped.

01 · Result

Hit the numbers

Senior paid-media manager running enterprise-scale Google Ads, Microsoft Ads, and YouTube. Seven-figure monthly budgets, hundreds of thousands of SKUs, eighteen months at the current account. Goals on revenue, MER, CAC, and new-customer acquisition — not in-platform ROAS.

  • ▪A major share of paid-media revenue at a nine-figure ecommerce business runs through the program I own.
  • ▪2026 YTD: strong double-digit revenue growth, improving marketing efficiency, disciplined CAC.
  • ▪Budget pacing, bid strategy, promo architecture, feed management, daily PUSH/PULL/HOLD — owned end-to-end.
02 · Method

Build the tools that hit them with me

The way I hit those numbers, efficiently and repeatably, is by building the operational tooling around the campaigns — governed AI workflows that turn manual, error-prone marketing work into reproducible systems with a human at the gate. Forecasting and pacing, promo automation, feed quality, task capture: each one lifts accuracy, speed, and efficiency without giving up control.

  • ▪A suite of internal production tools — control surfaces, planning and promo workflows, feed-quality toolkits, and task pipelines — all built with Claude Code as the coding agent.
  • ▪Daily stack: Next.js, TypeScript, Python, Tailwind, Vercel, Cloudflare, Anthropic SDK, Google Ads / GTM / GA4 APIs.
  • ▪Probabilistic-to-deterministic methodology — chat experiments become versioned, guardrail-protected production code.
03 · Multiplier

Lift the team

The wins don't stay on my account. Playbooks, tool registries, methodology essays, and working sessions go back across the digital marketing department, so every operator's tide rises together.

  • ▪The pipeline of "workflows worth lifting into tools" goes from single-source intake to multi-source spotting — because the rest of the team learns to see them too.
  • ▪Shared tool registries and playbook registries built so coding agents (Claude Code, Codex) can reuse the verbs across the org.
  • ▪Cross-functional training so the next operator can run the playbook — the building doesn't stop when I leave the room.
Three moves, one operator. Each feeds the next — and that's the actual job description.

Selected builds

Six representative builds across campaign pacing, budget planning, promo automation, feed operations, campaign infrastructure, and task extraction. Real workflows, not weekend demos.

Due to the sensitive nature of these businesses, all of this coding work is private.

Paid-media control surface

Private · Production

Performance control surface for Google Ads — hourly EOD forecasting that monitors spend, revenue, MER, and weekly goals, flags pacing drift early, and orchestrates bid/budget moves from a few clicks.

Before
Juggle six spreadsheets (daily spend, 52-week pacing, BSP calculator, revenue feed, Google Ads UI) to decide PUSH/PULL/HOLD. Weekend coverage was effectively zero — nobody opens six tabs on a phone.
After
One tap refreshes the hourly EOD forecast, classifies severity, and overlays MER status. A second tap surfaces a recommendation; a confirm sends the bid batch with safety bounds, preview verification, and audit logging. The operator owns the decision; the manual spreadsheet work is gone.
Next.jsTypeScriptVercelAnthropic SDKGoogle Ads APIGoogle Sheets/Drive
  • ▪Drives the daily operating cadence (morning, hourly, weekly).
  • ▪Hard guardrails enforced at the API layer — `validateTroasRange` throws on every mutation.
  • ▪Responsive — runs from a phone on nights and weekends.
  • ▪Also the entry point for broader optimization work (feed-management roadmap, title-tag experiments, post-test attribute analysis).

Budget planning workflow

Private · Live deploy

Reproducible heuristics-led monthly budget planning workflow — ROAS-weighted daily distribution under a binding monthly cap, with a human review step.

Before
Roughly an hour per account per planning cycle. Pull last year's daily cost + conversion-value, find weekday-aligned matches manually, hand-balance daily totals to the monthly cap, review for math/header/monotonicity errors.
After
Enter the monthly cap; the system distributes it across days using 52-week weekday alignment, ROAS performance weighting, and day-of-week + promo multipliers. Manual Adjustments table (the explicit Human Intelligence Layer) lets you override any row — system re-normalizes to the exact monthly total. Claude validates against seven sanity rules before export.
Next.jsTypeScriptVercelAnthropic SDKGoogle Ads APIGoogle Sheets/Drive
  • ▪Live on Vercel; used recurring monthly.
  • ▪364-day weekday alignment — "Mondays to Mondays, not calendar dates."
  • ▪Dual-auth Sheets pipeline sidesteps Drive quota limits.
  • ▪Cost-optimized Claude — invoked only when deterministic checks flag a critical failure.

Promo automation workflow

Private · Live deploy

End-to-end promo automation across thousands of product pages — AI-assisted offer extraction, RSA copy drafted from brand guidelines and promo rules, SERP-render simulation, and validated GAE CSV export, with human approval.

Before
Hand-roll RSA copy across thousands of product pages every promo cycle. High-stakes, time-pressured, error-prone.
After
Drop the promo brief in. The workflow extracts offer facts with per-field confidence and source tracking, then drafts RSA copy bound by the brand copy guidelines, RSA and promo rules, promo verbiage docs, and cross-channel examples. Every ad stays editable and gated on human approval. SERP preview emulates how Google will render the headline combos. Output is a validated Google Ads Editor CSV — or push directly via the API for human review.
Next.jsTypeScriptAnthropic SDKGoogle Ads APIPlaywright
  • ▪~2,300 ad-group rows per run.
  • ▪Real SERP combo generator (3-of-15 headline selection logic).
  • ▪28-column GAE CSV export.
  • ▪Multi-agent collab files (AGENTS.md / CLAUDE.md / RALPH-REFLECTION) coaching multiple coding agents through the codebase.

Catalog/feed operations toolkit

Private · Methodology publishable

Deterministic Google Merchant Center feed operations across hundreds of thousands of SKUs — feed quality and parity, attribute QA, title/metadata experiments, regression diffs, dual-agent cross-verification, and an autonomous loop with pass/fail launch gates.

Before
Weekly catalog audits done by eyeball or one-off LLM prompts. Each run reinvents column choices and methodology. Vendor-flagged issues had no independent verification path.
After
One CLI command runs the full feed-operations battery in seconds — quality, parity, and attribute QA across coverage, GTIN, MPN, image link, URL liveness, availability, brand (with compound-brand normalization), pricing, titles, and more, plus title/metadata experiments and post-test analysis. Dual-agent (Claude Code + Codex) implements the same spec in parallel for cross-verification. Week-over-week regression diffs and pass/fail launch gates that block ad-disapproving regressions.
PythonpandasClaude Code CLICodex CLIGMC Content API
  • ▪Audits a 265K+ SKU catalog in seconds.
  • ▪Dual-agent cross-verification — disagreement is the investigation signal.
  • ▪PRD-driven autonomous loop with a pass/fail "Judge" gate.
  • ▪Methodology essay (~1,540 words) generalizing the pattern for ops teams.

Campaign operations toolkit

Private · Architecture is the story

Python tooling that builds full Google Ads campaigns end-to-end and analyzes millions of search terms in seconds — with safe preview/pilot/execute mutation contracts on every operation.

Before
Custom Google Ads scripts proliferate across the team. Each duplicates client setup, filter logic, and safety patterns. AI agents had no consistent way to discover and call the existing tooling.
After
Centralized library: singleton client + 35+ UI-mirroring filter presets. Every mutation accepts three modes — preview (dry run), pilot (small subset), execute (full). Tool registry designed so AI agents can discover and reuse the verbs. Drafts complete campaign builds from a meeting transcript or a brief.
Pythongoogle-ads SDK v23pandaspytest
  • ▪32 CLI tools · 14 mutation modules · 22 tests.
  • ▪Three-mode mutation contract on every operation.
  • ▪Tool registry + playbook registry built for AI-agent reuse.
  • ▪Streaming search_term_view extraction for multi-million-row L90 analysis.

Passive task pipeline

Demo-ready

Passive task pipeline that solves the central failure of every project management system — manual input that eventually stops.

Before
Tasks scattered across four sources. Manual triage of meetings, email, calendar, chat. "What did we agree to in that meeting?" is a 20-minute archaeology hunt. Tasks get dropped or duplicated.
After
Pipeline reads my business channels passively. Claude extracts managers' and stakeholders' directives verbatim and creates canonical tasks from those quotes. Cross-source semantic dedup. Tasks surface in seven explainable priority lanes with a transparent score breakdown. As I communicate completion in those same channels, the registry updates passively. The only manual input is a yes/no feedback loop on each extracted task.
Python 3.13FastAPINext.js 16React 19SQLite + TursoClaude API
  • ▪40-test pytest suite.
  • ▪16 FastAPI REST endpoints.
  • ▪7-lane priority engine with explainable lane-assignment decision tree.
  • ▪Fixture-backed demo seed — runnable end-to-end demo in 60 seconds.

How I work

The pattern across every tool. Same six moves, every time.

  1. 01

    Map the messy workflow

    Sit next to the operator. Watch the actual moves they make — every tab, every spreadsheet, every dropdown. The tool is built against the real workflow, not the imagined one.

  2. 02

    Encode the expert judgment

    The operator's heuristics — "compare Mondays to Mondays," "weekdays earn more than weekends," "never push CSV without a preview" — become executable code, named explicitly, with documented thresholds.

  3. 03

    Add guardrails before features

    Hard bounds, dry-run/pilot/execute contracts, preview verification, audit logging, rollback capture. The expensive mistakes that already happened can't happen again.

  4. 04

    Keep humans at the gate

    AI handles extraction, normalization, and validation. Humans approve mutations. Severity levels carry prescribed actions, but the operator owns the final PUSH/PULL/HOLD call.

  5. 05

    Train the team alongside the tool

    Working sessions on how to spot a workflow that's a candidate for tool-lifting, how to scope the build, how to evaluate the outcome. The pipeline of "workflows to lift" goes from single-source intake to multi-source spotting.

  6. 06

    Make it repeatable

    Document the playbook so the next operator can re-run it. Codify patterns in essays and shared tool registries. The expert judgment becomes institutional, not tribal.

Outside the laptop

Most of the operator/builder energy comes from somewhere quiet outside the work.

Bjorn hiking — out from behind the laptop
Family — the actual reason I optimize for nights and weekends being workable from a phone

I work remote, from the outskirts of Nashville. My wife and daughter are the actual reason I designed my work to be runnable from a phone in the first place — the operator-controlled, off-pace-detector, one-tap-from-anywhere version of the job is downstream of wanting to be at the park on a Saturday afternoon and still confident the account is on pace.

Outside of work I hike, read, and tinker with side builds (some of which become real builds — that's how the passive task pipeline started). I'd rather have one strong demo to walk through than ten polished slides about what I'd theoretically do.

If you're a hiring manager scanning this page in 30 seconds: I drive real business growth as a senior digital marketing manager, I build the tools and AI workflows that make that growth efficient and repeatable, and I lift the rest of the team by sharing every win back as a playbook. Same operator. The three feed each other.

AI enablement framework

The methodology behind the tools above: moving marketing AI from one-off chat experiments to reproducible, guardrail-protected operating systems — captured across three short decks.

Download · 12-slide deck (.pptx + .pdf)

Engineering AI Reliability

The Probabilistic → Deterministic methodology as a 12-slide deck — the four-stage maturity model, the four common marketer objections refuted, and the discipline of not skipping stages. Click through the slides below, or grab the .pptx or .pdf directly. No email required.

Engineering AI Reliability — slide deck — slide 1 of 12
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Download · 13-slide deck (.pptx + .pdf)

The Build Staircase

How a throwaway prompt hardens, step by earned step, into a product anyone can use — Prompt → Skill → CLI → Agent → App. The probabilistic-to-deterministic lifecycle, with one rule: climb only when each step is proven in real use. Click through the slides below, or grab the .pptx or .pdf. No email required.

The Build Staircase — slide deck — slide 1 of 13
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Download · 10-slide deck (.pptx + .pdf)

Scout & Workhorse — MCP vs CLI

When to reach for MCP vs CLI: MCP to explore what's possible, CLI to do it reliably. Why neither replaces the other — and how a proven MCP run graduates into a hardened CLI command. Click through the slides below, or grab the .pptx or .pdf. No email required.

Scout & Workhorse — slide deck — slide 1 of 10
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Let's talk

I'm looking for an organization where AI-assisted operating is treated as strategic infrastructure for the marketing team — not a chat tool. If your team treats AI as operating leverage — governed workflows tied to revenue, efficiency, adoption, and measurement in day-to-day marketing operations — we should talk.

bjorndavidhansen@gmail.comlinkedin.com/in/bjorndavidhansenResume on request