Site Sentry
Turning high-volume data into a clear “what matters now” experience at decision speed.
| 0→1 Launch in 7 Months | $145M Pipeline Impact | Google Adopter |
Context
Product Type
AI platform for decision making
Team
63 across product, applied science, and data operations
Timeline
7 months (concept → market)
Role
Product & Execution Lead (Applications)
The Bridge:
While built for intelligence, this challenge is common across creative and consumer-driven industries: transforming data into timely, relevant insights that drive action. The decisions below mirror the same tradeoffs common in sports, media, travel, and advertising platforms where attention, clarity, and momentum matter more than feature density.
The Real Problem
;
In fast-moving environments, tools don’t create value; clarity does.
What we initially believed:
The product needed a more comprehensive toolbox, one with additional features to help users explore data, export views, and run their own analyses.
What we realized:
In fast-moving environments, tools don’t create value; clarity does.
A product only earns attention if it can answer
What just changed?
Is it important?
What should I look at next?
BEFORE: HIGH-VOLUME DATA, NO SIGNAL
AFTER: CLEAR SIGNAL AT DECISION SPEED
The risk wasn’t under-building features. The risk was shipping something technically capable that users didn’t return to.
The Moment: We shifted the organization from building analytical tools to shipping decision infrastructure, and adoption followed.
The Strategic Tradeoff
We faced a high-exposure 0→1 decision:
The product would be evaluated by external partners and executives before it was structurally “complete,” making early missteps visible and difficult to hide. We had two options:
Option A:
Optimize for completeness and long-term structure.
Option B:
Optimize for early signal, usability, and momentum, accepting short-term imperfections
We chose Option B, prioritizing momentum over structural completeness.
At the time, we had no proof this would work, only conviction that shipping clarity mattered more than shipping completeness.
To keep teams aligned, I set a Value-First rule:
If a feature didn’t directly improve a user’s ability to understand change, it didn’t ship.
What that meant in practice:
We prioritized:
Lightweight sharing so insights could move between people, not just systems
Early AI-driven context layers that explained why something mattered, not just that it happened
We deferred:
Deep administrative controls
Perfect backend abstractions
Edge-case completeness
In the short term, this created friction for power users and required manual workarounds from the team, a cost I accepted to preserve speed and adoption momentum.
The goal was to create a product that felt useful immediately, not one that was theoretically finished
Outcome
The launch validated and tested a core insight
This launch reframed success internally from feature delivery to decision velocity, shifting how teams evaluated progress, adoption, and roadmap tradeoffs.
Impact:
Launched from 0 → 1 in 7 months
Directly contributed to a $145M pipeline
Adopted by global partners, including Google
Had adoption stalled, the responsibility would have been squarely on this prioritization call.
What I’d Do Differently
With the same strategic decision, I would instrument sharing and engagement telemetry from Day 1. That signal would have accelerated experience-based decisions and reduced reliance on qualitative feedback.