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At Opticon 2026 in New York, Steve Farr (Chief Digital Officer) and I took the stage to share a project we're genuinely proud of: how OneMagnify built an AI-powered early-warning system on Optimizely Opal for a wholesale B2B client — turning a mountain of disconnected data into something leadership could actually act on.
The Question That Started It All
"How many customer problems are we discovering because the customer called us first?"
That's the question our client brought to us. Like a lot of B2B distributors, their business depends on easy ordering and reliable, on-time delivery. When those expectations slip, cancellations and returns follow. And too often, the team was hearing about a problem from the customer instead of catching it first.
It's a familiar story: a small, focused team with real investments in ERP, PIM, CMS, and CRM platforms, sitting on a mountain of data — but with no way to connect it. The systems were disconnected, hard to trust, and impossible to analyze quickly. A prior attempt at a BI and data visualization initiative hadn't earned leadership's confidence, and margin pressure meant there wasn't headroom to hire a team of analysts to bridge the gap.
The Solution: An Intelligence Layer, Not a Rip-and-Replace
We built a secure, scalable AI-enabled intelligence layer that integrates Optimizely Opal with the client's ERP, CMS, and data — without replacing any of their existing systems. It has four parts:
What We Learned Along the Way
Not everything went in a straight line, and that was some of the most useful material in the session. Our initial commerce use cases tried to process large volumes of ERP data spanning months to a full year. In practice, Opal has real limits on how much data it can ingest and process effectively for AI-generated responses — we found ourselves progressively narrowing the window down to just a few days, which wasn't enough for meaningful commerce analytics.
Working with Optimizely's product team, we landed on a better design: pull fewer, high-value data points instead of full ERP records, and extend the timeframe to months instead of days. That shift unlocked business-relevant insights without hitting platform constraints — and freed us to expand into CMS-based use cases in parallel, leveraging Optimizely's existing agent framework.
Live, on Production Data
Rather than just talk about it, we demoed it — live, with real production data — across five commerce use cases:
The Results
Beyond the numbers, this set the client up with a scalable, future-ready AI architecture — one that can expand into new dashboards, new agents, and new data sources as the business grows.
The Bigger Point
The title wasn't just a hook. Cuts to content, staffing, or process rarely show up in revenue right away — the impact can take six to eighteen months to surface, by which point the decision that caused it is long forgotten. The same is true in reverse: an intelligence layer like this doesn't pay off in a single quarter. It pays off by catching the problem before the customer has to call and tell you about it.
Thanks to everyone who joined us in the room at Opticon — great crowd, great questions, and a fun one to demo live.