DocMetrics

Every sales tool I've seen does a good job of instrumenting what happens before a proposal goes out. Lead scoring, intent data, CRM activity, opportunity stages — that side of the funnel is reasonably well covered.

The moment a rep hits send, visibility drops to almost zero.

They don't know if anyone read it. They don't know which pages held attention and which ones lost it. They don't know if it was forwarded internally to someone they've never spoken to. They don't know if the person they originally sent it to is still the one driving the evaluation, or if a new stakeholder has quietly become the real decision-maker. They find out when the deal closes or when it dies — and by then it's too late to do anything differently.

That blind spot is where I built DocMetrics.

What it does

DocMetrics turns a tracked document link into a live signal. When a prospect opens a proposal, it captures how they engage with it — which pages they spent time on, whether they came back, whether they returned specifically to the pricing section before going quiet, whether a second person from the same company started reading it. Then instead of showing a dashboard of numbers, it interprets those signals in plain language and tells the rep what the pattern typically means and what's worth paying attention to.

The interpretation layer is the part I've spent the most time on. Most document analytics tools show you what happened. DocMetrics tries to tell you what it means — and importantly, it's honest when the signal is too thin to draw a reliable conclusion. Every output is framed as "document engagement suggests X" rather than "this deal is X," because the gap between those two things is real and I think pretending otherwise erodes trust faster than anything else.

What I've learned from building it

I've spent the last several months talking to RevOps leaders, sales practitioners, and revenue researchers — not to pitch them, but to understand where my thinking was wrong. A few things stand out.

The signal most people actually trust is a new person from the same company opening the document. One practitioner put it directly: "One excited champion means nothing if they can't get budget. But the second a colleague starts poking around, that's a real deal." I've also found that the inverse — the original contact going quiet while a new stakeholder becomes the active reader — is equally meaningful and almost never surfaced by existing tools.

Engagement is not intent. Someone opening a proposal three times tells you almost nothing on its own. It could mean serious evaluation or it could mean they're quietly building the case for why it's a no. The interpretation has to be honest about that ambiguity rather than forcing a confident verdict from a thin signal.

The structural limitation is real. DocMetrics observes a subset of what's happening in a deal — the document layer specifically. The most consequential things in a B2B sale often happen outside that layer entirely. A champion leaving, a budget decision made in a meeting, a competitor offering a discount — none of that is visible to DocMetrics. I've tried to build that honesty into every output rather than overclaiming what the tool knows.

The signals already exist in most stacks. One of the sharpest observations I received from a practitioner was this: "The signals already exist. They're just not being read together. That's the problem worth solving. Not replacing the PDF. Connecting the dots that are already there." That framing shaped how I think about where DocMetrics fits — not as a replacement for anything a rep already uses, but as the interpretation layer that reads the document signals that currently go unread and surfaces them where the rep already lives, whether that's HubSpot, Slack, Teams, or email.

Where it is now

DocMetrics is live. The core interpretation layer is built — committee detection that distinguishes confirmed company domain matches from link-only forwarding, re-read analysis that identifies whether a viewer returned to specific pages or re-read the full document, disappearing-viewer detection that surfaces when the original contact goes quiet while a new stakeholder becomes active, and signal agreement checking that flags when individual engagement and group engagement point in different directions.

Integrations with HubSpot, Slack, and Teams are live so the signals flow into wherever a rep already works rather than requiring them to check another dashboard.

The honest gap: the signals and thresholds are currently based on practitioner judgment and behavioral patterns, not validated correlations against actual deal outcomes. I've built the outcome-capture infrastructure to eventually run that validation. But I don't have the data yet and I'm not pretending otherwise.

What I'm looking for

I'm looking for sales practitioners, founders who send proposals, and RevOps people who've felt this blind spot directly — especially anyone willing to run a real proposal through DocMetrics and tell me honestly where the interpretation breaks down.

Not looking for positive feedback. Looking for where it gets it wrong.

If that's you, you can try it at docmetrics.io — or just reply here with your perspective on the problem. I've learned more from honest conversations than from anything else in this process, and I'd rather keep that going than turn this into a launch announcement.