I'm building an independent verification layer for affiliate marketing and need a design partner

In display advertising, DoubleVerify built a $4 billion business by independently verifying that ads actually ran where they were supposed to. One simple question: did the thing that was supposed to happen actually happen?

Nobody is asking that question for affiliate marketing.

The problem

If you run affiliate programs, you've done this: pulled your internal conversion data, pulled your platform report, and stared at a 5–15% gap between them. Maybe you chalked it up to browser privacy. Maybe a misconfigured postback. Maybe you just shrugged and moved on, because the only data you have is the vendor telling you their own numbers are correct.

If you've ever looked at that gap and thought "I have no idea what's actually going on in there" that's exactly what I'm measuring.

What I've built

I have a working MVP at fidelitygrid.polsia.app. The methodology: fire synthetic events through the attribution pipeline, measure what comes back, and classify every discrepancy into four buckets:

Browser/privacy signal loss (unfixable — iOS, Privacy Sandbox)
Vendor infrastructure failures (dropped postbacks, server errors)
Processing latency (legitimate async delays)
Unexplained gaps (everything else — the interesting bucket)
That last category is where the money is. Not all discrepancies are created equal, and right now the industry treats them like they are.

The bottleneck

The tool works. Now it needs real-world data.

I need a design partner — someone running an affiliate program on Everflow (or Partnerize, Impact, Trackdesk, etc.) who's felt the pain of unexplained attribution drops and wants to know what's actually happening.

What you'd get

You'd be the first external validation of this methodology — which means you'd shape how it works. In return:

-A detailed breakdown of where your attribution gaps actually are and what's likely causing them
-Identification of potential "found money" — conversions that may be tracked but never credited, or credited but never tracked
-Full transparency on the methodology, its limitations, and what we learn together
-All findings are yours first, before anything is aggregated or anonymized

Two ways to do this

Option A — 15 minutes, zero risk: Export two CSVs (your internal conversion data and your platform report), upload them to the tool. I run the reconciliation and share findings within a day. No API access, no integration, no trust required beyond two spreadsheets.

Option B — continuous monitoring: Configure your platform's server-to-server stream to my endpoint for ongoing measurement. More depth, but I completely understand starting with Option A to see if the findings are worth going deeper.

Why I'm building this

The shift from client-side cookies to server-side tracking was supposed to make attribution more reliable. Instead, it moved the pipeline behind closed doors. Advertisers went from imperfect-but-visible tracking to opaque-but-trust-us tracking. That opacity benefits incumbents and costs advertisers real money — they just can't quantify how much.

I want to make it quantifiable.

If this resonates

DM me if you're running affiliate programs and curious about where your attribution gaps actually are. Even if you're on a platform I haven't mentioned, I'd love to talk — the methodology is vendor-agnostic and I'm genuinely trying to understand where the industry's pain points cluster.

Also very open to intros. If you know someone running $50K+/year in affiliate spend who's complained about tracking discrepancies, I'd appreciate the connection.