We Solved for Discovery, but not Deployment

The Detective Dilemma

Mid-2022, we'd cracked the code on giving data a voice. Our algorithms could spot the vampires, identify the canaries, and reveal what your existing customers were really worth. We'd moved beyond dashboards to deliver actual insights that companies could understand.

But we'd overlooked one critical fact: not everyone's a back-of-the-baseball-card nerd.

We were handing over magnifying glasses and expecting businesses to become Sherlock Holmes. Our platform would say: "This customer's buying patterns changed 47% in Q3." And leaders would stare back, waiting for the "so what?"

The Missing Translation

Picture this scene across dozens of our early client meetings:

"Amazing work! Now, what do I tell my team to do with this information?"

We'd find:

• The customer who'd stopped ordering 3 product categories (but which ones did they miss most?)
• The pricing inconsistencies across regions (but where should we standardize first?)
• The product margins shifting downward (but which levers actually reverse it?)

Data was talking. Insights were clear. But the bridge from "what we discovered" to "what you do Tuesday morning" was still our clients' burden to build.

The First Breakthrough: Named Agents

Our answer started with two focused algorithms:

• Customer CPR Agent: Identifiable at-risk customers ranked by impact, with specific retention actions
• Canary Agent: Early-warning customers showing behavior changes that predict future problems

Instead of generic "high-risk customer" reports, these agents delivered action-ready lists:

• Call Sarah at Acme Corp TODAY regarding Product X
• Email James at Widget Inc. about his 38% order reduction
• Visit Nina at Parts Co. about unexplored Product Y opportunities

We'd transformed "here's what's happening" into "here's whom to call." Progress—but we were still assembling a to-do list instead of solving the real problem.

The Lurking Revolution

By late 2022, something was stirring in the broader tech world. We could sense it in our client conversations—the way they started asking about AI, the questions about ChatGPT, the raised expectations.

The revolution was coming, and it would fundamentally change how businesses expected to interact with their data...

What happened next forced us to completely rethink not just how we deliver insights, but the very nature of what an insight should be.