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project · 04  ·  notion + zapier  ·  discovery tooling
Self-internship Notion + Zapier

A discovery system that runs itself.

The Discovery Hub is a Notion workspace I built to hold real product work — a PRD, notes from 20+ discovery interviews, and a RICE table scoring five decisions I actually made on the Distributor Management System. A live Zapier automation keeps its feedback database filling itself.

What it is
Discovery system + live automation
Built with
Notion, Zapier, Google Forms
Context
Self-internship · LeepX, week 2
Source material
Real DMS decisions & interviews
what it is ✿

Not a template exercise. Every entry is grounded in real product decisions from my work on the DMS at Salescode.ai — the same interviews, the same trade-offs, scored and structured after the fact. The Zapier automation is a working example, not a claim: submit the form and a Notion row appears.

4
connected pieces in one Notion workspace
20+
real discovery interviews, written up
5
real DMS decisions scored with RICE
0
manual steps between form and database

why I built it · 01

Discovery output is messy by default.

Discovery generates a particular kind of mess: interview notes scattered across documents, decisions made in meetings and never written down, priorities argued rather than scored. The work happens, and then it evaporates. Six months later nobody can reconstruct why a call went the way it did.

I wanted a working answer to that in a tool remote teams actually document in. So I built the Discovery Hub in Notion and filled it with my own real material rather than sample data — the interviews I ran and the decisions I made building the Distributor Management System.

That constraint is the point. Anyone can populate a Notion template. Structuring your own messy history into something referenceable is the part that's actually hard, and it's the part that proves the skill.

what's in it · 02

Four pieces, connected.

The Hub is one workspace with four linked parts, arranged in the order discovery actually moves — interviews, then prioritisation, then a spec, then a loop back for new input:

piece 01

User Interview Notes database

Written-up entries from 20+ real discovery interviews with distributors, sales heads and finance teams. Structured so a note can be filtered and cited later, not just read once.

piece 02

RICE prioritisation table

Five real DMS decisions, scored retrospectively on reach, impact, confidence and effort. Scoring calls I'd already made was the honest test — the framework had to agree with what shipped, or explain why it shouldn't have.

piece 03

PRD — invoice immutability & corrections log

A full spec for the finance decision I'd defend hardest on the DMS: invoices are immutable, with a corrections log, never free-form edits. Slightly less convenient, dramatically more auditable.

piece 04

Feedback database, wired to Zapier

A live intake table that fills itself from a Google Form. This is the piece that turns the Hub from a document into a system that keeps running.

prioritisation · 03

Scoring decisions I'd already made.

RICE is easy to perform and hard to use honestly. Running it forward on hypothetical features tells you very little. Running it backward on decisions whose outcomes you already know is a real test of whether your inputs were any good.

So the table scores five DMS calls I'd already lived with, including the density-versus-polish trade-off that a pilot client pushed back on:

DecisionWhy it scored where it didRank
Classic / density modeHigh reach across every power user, low effort once the data layer existed01
Invoice immutabilityNarrow reach, but impact and confidence both maximal — finance can't sign off without it02
Role-based accessStructural. Cheap on day one, expensive as retrofitted security debt03
Orders-processed as the adoption metricNo user-facing reach at all, but it governs every later prioritisation call04
Phone-first field, desktop-first adminHighest effort of the five; justified by two genuinely different jobs05

The interesting result was the density decision landing first. That was the call we got wrong initially — we shipped clean and got pushed back on. Scored properly against reach and effort, it should have been obvious from the start. That's the value of doing this retrospectively: it shows you which instinct to trust next time.

the automation · 04

A feedback loop with no human in the middle.

The Feedback database is connected to a Google Form through a live Zapier automation. A submission arrives, Zapier fires, and a structured Notion entry appears. There is no copy-paste step and no batching — the loop runs whether I'm looking at it or not.

trigger
Google Form
submission
zapier
Map fields,
fire action
action
New row in
Notion Feedback DB

submit it and the row is just… there ✿

The live automation — trigger and action, running without manual intervention.

The tooling here is deliberately unglamorous. What it demonstrates is trigger-and-action thinking: noticing that two tools don't talk to each other natively, and treating the manual step between them as a defect rather than as work.

Remote product roles increasingly assume that instinct. This is a working example of it rather than a line on a skills list.

the through-line · 05

The same instinct, at a smaller scale.

There's one idea underneath most of my product work, and it came out of watching distributors use the DMS:

Distributor operators process high volumes of orders, returns and accounting entries every day, so a minimal UI that hides information behind extra clicks and scrolling actually slows them down — density beats polish when speed is the job.

The Discovery Hub is that instinct at a much smaller scale. A prettier system that still needed me to copy form responses into Notion by hand would have been the wrong build. Structure that removes manual friction beats structure that looks tidy — the same trade-off, one tool down.

notion zapier google forms rice scoring prd writing discovery interviews
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