Opal University graduation stories: Class of April '26

Here's what the Opal U | AI Marketing University April cohorts proved: five days is enough.

Enough to build an ABM team from scratch. Enough to retire the Slack thread where everyone asks "what's happening with our tests?" Enough for a solo web marketer at a medical device company to absorb a colleague's entire workload during maternity leave and not drop a single blog post.

The April cohorts ran across four weeks and pulled in 51 builders from 40+ companies. Salesforce, KPMG, FedEx, Vodafone, NatWest, HMRC, Birkenstock, Canon Europe, ODEON Cinemas, ClassPass, Republic Services, Sandvik. Companies you'd expect.

...and a few you really wouldn't — a UK government department, a retail bank, a medical device company, an indirect procurement manager who had no business being this good at this.

Cohort 7 ran as an experimentation specialty track, and delivered 264 agents in five days. That's the highest volume of any cohort to date. 

~600 agents total, but (just) five stories below.

The one that exposes your competitor's entire campaign strategy

Michael Richter, Manager CRO & UX @ Robinson Club

Michael came into Cohort 5 with a specific frustration. Robinson Club runs campaigns across multiple global markets. His job involves a lot of the work that happens before a campaign goes anywhere near a channel — competitive analysis, hypothesis generation, brand QA. All of it important, all of it slow, all of it competing for the same limited hours in a week.

He built 14 agents (!). That's the most of anyone across all four cohorts so far.

The one worth pausing on: the Blue Ocean Campaign Strategist. You give it a competitor's campaign. It extracts what they're doing, maps where the positioning space is crowded, and finds the uncontested territory Robinson Club could own instead. The kind of brief a good strategist takes three days to write. This one runs in minutes.

Then there's the Hypothesis Generator. This one turns email content into experimentation hypotheses automatically. A Brand & Tone Compliance Reviewer that catches copy before it ships. A Global Homepage Campaign Analyzer. Together, they're not automating a task here or a task there. They're replacing the entire strategic and QA layer of a campaign team.

Time saved: A campaign-strategy and brand-QA function that used to need a team, now one operating system.

The one that did 200 hours of work a year on the side

Dominic Mac-Ennin, PPC Manager @ Telia Mobil Danmark

Dominic manages paid search across a 10-client portfolio, and he is the kind of person who runs the math. So when he built a PPC Executive Narrative Generator; an agent that takes monthly campaign performance and translates it into C-level summaries — he didn't say "this probably saves a lot of time." He sat on Demo Day and calculated it out loud.

Monthly reporting, 10 clients: previously manual, repetitive, eating hours that should have been going to actual strategy. The agent does the translation. He checks it. It ships.

He also built a Daily Monitor that surfaces performance anomalies in Slack before they become expensive problems, an RSA Ad Copy Generator with strict character-limit enforcement, and a Daily Portfolio Monitor. The full PPC operating cadence — research, copy, daily monitoring, exec narrative — now runs on agents.

Time saved: 200 hours per year, on the narrative reporting agent alone. Across the full portfolio. YES.

The one where one person ran a small agency's output

Allison Beckman, Senior Web Marketing Associate @ Graphic Controls / Nissha Medical

A content specialist went on maternity leave. But, of course, the blog volume didn't. Allison absorbed that task — no agency, no contractor, no extra hours handed to her. So, what did she do? She built her way out of it.

Ten agents. A full medical-device SEO and content stack: an SEO FAQ Generator, a Keyword Topic Extractor, an SEO & Geo-Content Optimizer, a Social Media Drafter. The one that really moves the needle: a QoQ SEO Slide Generator that takes Looker Studio data and turns it into a stakeholder-ready PowerPoint automatically. The deck that used to take a chunk of her week now renders itself.

What's easy to miss in the headline number is the kind of work this represents — it's a regulated industry, a solo marketer, and a significant coverage gap from a team absence. Allison didn't just save herself time; she built the capacity to keep going without dropping anything, and made the whole workflow handoff-ready in the process.

Time saved: Hours back per blog. An entire team gap covered without an agency.

The one where a retail bank put guardrails on every experiment

Steve Quinlan, Digital Experience Manager @ NatWest

Running experiments at a retail bank is not like running experiments at a DTC brand. The stakes are different. Measurement plans have to be right. Hypothesis quality matters. Results need to be translated into language that survives a stakeholder meeting without anyone misreading what the data actually says.

Steve built eight agents, all aimed at that same problem: catching what goes wrong with experiments before it goes wrong at scale.

A Hypothesis Stress Tester pressure-tests ideas before a test gets resourced. A Measurement Plan Critic reviews plans for blind spots. An Experiment Story Synthesizer turns results packs into plain-language narratives that stakeholders can actually use. A RICE Prioritization Expert scores the backlog. A GEO Readiness Auditor. A Monthly Performance Report Builder that covers traffic, site speed, accessibility, and product copy in one pass.

The through-line: a consistent quality layer before and after every test, running without a programme manager reviewing every brief by hand.

One of the other graduates in Steve's cohort who watched his demo put it best:

The one that caught the broken experiment in real time

Jen Gazvoda, Senior Manager Analytics @ American Home Shield (Frontdoor)

Every experimentation team has lived this one. A test runs for two weeks, results come back flat, then someone notices the success event wasn't firing. The test was measuring nothing.

Two weeks, gone. [insert giant, crying sad face here]

Jen built a Live Experiment Zero Metric Watcher. It detects experiments running with broken instrumentation in production, in real time, before the two weeks are up. That's not a small thing. That's potentially dozens of wasted test cycles a year, caught before they happen.

She also built a Feature Experiment Plan Builder (raw feature ideas to compliant Optimizely rollout plans), a Conversion Gap Test Finder that mines GA4 engagement data for A/B test candidates, and a Reddit Pulse that monitors American Home Shield's brand sentiment on Reddit and clusters it by theme — so customer signal feeds directly into product without a research vendor in the loop.

Time saved: Broken test cycles, caught before two weeks pass. Voice-of-customer research, running continuously without anyone commissioning it.

The other 46 builders

Five stories can't cover 51 graduates and the incredible agents they've created.

A sneak peek into the full picture? Fine, you persuaded us.

A procurement manager at Republic Services who built a 10-agent vendor-risk-and-RFP workflow that would normally take a category management team (Darren Davis, who was not our typical audience but he built like a power user anyway). A consultant at Intermedia who productized her entire CMP rollout playbook into 11 repeatable agents and called it "5 to 10 hours saved for every piece" (Claire Barlow). A demand gen manager at Restaurant365 who essentially rebuilt her ABM team in 10 agents covering account selection, daily prioritization, partner research, and reporting (Lacey Grim). A government experimentation programme at HMRC that now writes production-ready JavaScript instrumentation on demand (Paul Knights). A ClassPass UX designer who built a Competitor Experiment Radar that reads competitor source code for testing platform signatures in real time (Niko Landolfo).

Ready to build? Apply to Opal U | AI Marketing University now.