"What are we missing?"
I sent my first commercial email in 1998. Back then, email was simple. You wrote a message, you clicked send, it arrived. There were no spam filters, no authentication protocols, no reputation systems. If you had an SMTP server, you could reach anyone.
That changed fast.
By 2007, email deliverability had become a discipline. ISPs were fighting spam with increasingly sophisticated filters. Legitimate senders were getting caught in the crossfire. I founded Teneo to help them navigate this new reality.
For nearly two decades, I've been in the trenches with some of Europe's largest senders—retailers processing millions of transactional emails daily, media companies managing dozens of newsletter brands, e-commerce platforms where a 1% drop in inbox placement means six figures in lost revenue.
No matter how thorough the analysis, no matter how many dashboards we built, no matter how sophisticated our monitoring—there was always the same nagging question:
"What are we missing?"
Because we always were. Missing something, I mean. The pattern that only becomes obvious in hindsight. The correlation across ESPs that nobody thought to check. The slow decay that looked like noise until it became a crisis.
Dashboards can only answer the questions you think to ask. And in complex email ecosystems, the most dangerous problems are the ones you don't know to look for.
Late one night, working on a particularly complex deliverability issue for a multi-brand retail client, I found myself explaining the investigation process to an AI assistant. Not asking it for answers—just thinking out loud, the way you do when you're stuck.
"First I'd check the bounce rates by ISP. Then I'd look at whether the pattern holds across all sender identities or just some. Then I'd correlate with engagement metrics to see if this is a reputation issue or an infrastructure issue..."
And then a thought: What if the AI didn't just listen to this process—what if it could execute it?
We built a prototype. Crude at first—just a way to let AI query our deliverability data and follow chains of investigation the way a human expert would.
Then we pointed it at a real customer's data. A customer we thought we knew well. A customer whose email program we'd been monitoring for months.
Within hours, it found a pattern we'd completely missed: a specific sender identity was performing differently at one ISP—but only on mobile devices, and only in certain geographies. The signal had been there for weeks, buried in the noise, invisible to our dashboards because nobody had thought to slice the data that way.
That was the moment. Not "this is interesting technology." But: "Why didn't we see this? And what else are we missing—right now—for every other customer?"
Email analytics tools give you data. Lots of data. Dashboards, charts, exports, APIs. But data isn't insight. Insight comes from investigation—from forming hypotheses, probing anomalies, correlating across dimensions, following threads until you find root causes.
That's what deliverability consultants do. It's what I've done for 27 years. But humans can only investigate so much. We get tired. We have blind spots. We can't monitor everything, all the time, across every dimension.
What if software could?
We didn't set out to create a new category. We set out to solve a problem that had bothered us for two decades: the unknown unknowns in email deliverability.
But as we built, we realized that what we were creating wasn't just another analytics tool. It wasn't a dashboard. It wasn't an alerting system. It was something fundamentally different: software that investigates.
We call it an Agentic Email Intelligence Platform. "Agentic" because it has agency—it doesn't wait to be asked, it actively observes, hypothesizes, investigates, and escalates. "Intelligence" because it doesn't just report what happened, it figures out why and what to do about it.
27 years in email. 18 years in deliverability consulting.
Bram founded Teneo in 2007 and has since advised some of Europe's largest email senders on deliverability strategy, infrastructure architecture, and reputation management. His client work spans retail, media, financial services, and e-commerce—organizations sending tens of millions of emails daily across complex multi-brand, multi-ESP environments.
Engagor is the product he wished existed for the past two decades: an intelligence layer that watches everything, all the time, and finds what humans miss.
Engagor isn't AI pretending to understand email. It's 27 years of deliverability expertise translated into investigative logic. The AI has the same instincts a senior consultant would—because that's what we taught it.
We've spent two decades working with organizations where email is mission-critical. We build for that reality: tenant isolation, audit logging, compliance-ready architecture. Not retrofitted—foundational.
Dashboards show you data. Engagor investigates it. The difference isn't cosmetic—it's the difference between waiting for humans to ask questions and proactively finding answers they didn't know they needed.
Headquartered in Belgium. GDPR-native. But built for organizations operating across regions, ESPs, and regulatory environments. Local roots, global reach.
Let's talk about your email ecosystem and how Engagor can help you find the patterns hiding in your data.