Working Mono · an AI-native firm

The join is the hard part. We build it in your warehouse.

Scoring, routing, expansion plays, churn risk: every GTM workflow downstream is only as good as the join underneath it. We join product usage, CRM, billing, and support into one source-traceable account model in your warehouse, where every field knows which system it came from and when.

We do the work. You own the machine. The join standing in your warehouse by day 28.

Proof

30+Commercial systems delivered
Month 1Foundation live, guaranteed
100%Owned by you
270+Workflow patches shipped to client systems
Attio Founding Expert Partner
Trusted by AI-native teams Granola Reducto Cache Vetrec
The definition

What is the GTM data join? One account model, four systems deep.

The GTM data join is the unified account model created when product usage, CRM, billing, and support data are merged in one warehouse, with every account, user, and dollar reconciled across systems and every field traceable to its source. It is the layer scoring, routing, expansion, and risk workflows read from, and the layer no tool ships prebuilt.

How it compounds

How the join gets built. And why it holds under change.

A join is more than an integration diagram: entity resolution, field lineage, and reconciliation logic written for how this company actually sells, built once in your warehouse and leaned on by every workflow that follows.

Step 01

Chart the four surfaces

Product events, CRM objects, billing records, and support tickets each describe your accounts differently, and each is partly wrong. We interview the operators who live in each system while reading the schemas directly, and chart where the four stories about one customer disagree.

Output: the source map, every conflict named

Step 02

Resolve the entities

The hard center of the work: matching users to accounts, accounts to invoices, tickets to both, across IDs that were never designed to meet. Rules are written for your naming mess, your parent-child structures, your migration scars, then tested against the records themselves.

Output: one entity graph all four systems agree on

Step 03

Wire the lineage

Each field in the joined model carries its origin: which system, which record, which sync, how fresh. When a number looks wrong, anyone can walk it back to the row that produced it in seconds. Trust in the model stops being a matter of opinion.

Output: a source-traceable model with no orphan fields

Step 04

Put workflows on top

With the join solid, the downstream work gets fast. Scoring reads real usage next to real revenue. Routing sees support load before assigning owners. Expansion and risk plays fire on joined signal instead of one system's partial view. Each ships as a patch to your repo.

Output: workflows on joined signal, shipped as code

Step 05

Reconcile the drift

Sources drift: plans change in billing, objects get renamed, product ships new events. A reconciliation pass runs on schedule, catches the drift, and reports what moved and what it touched downstream, before your scoring quietly goes stale.

Output: the weekly drift report, join intact

Step 06

Deepen the model

Every workflow the join feeds teaches it something: a segment that behaves differently, an edge case worth encoding, a new source worth adding. The model deepens around your business, in your warehouse, where it compounds instead of being rebuilt.

Output: a deeper join, ready for the next workflow

Stop scoring accounts your billing system can't see. Walk the join in 20 minutes.

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One revenue memory. Every surface.

GTM inputs
attio
stripe
metronome
clearbit
apollo
snowflake
segment
postgres
slack
hubspot
mixpanel
intercom
zendesk
linear
bigquery
salesforce
posthog
amplitude
+ and more
Month one

Month one builds the join. Every patch after leans on it.

The join is standing inside a month: warehouse up, entities resolved, lineage wired, and the first workflow reading joined signal before day 28.

Week 1

Chart the sources

We read all four systems and chart how each describes your accounts, naming every conflict the join has to settle.

Week 2

Resolve the entities

Matching rules link users, accounts, invoices, and tickets into one graph, tested against your live records.

Week 3

Lineage and queries

Field lineage goes live and the model opens to plain-language queries. Any answer walks back to its source row.

Week 4

The first joined workflow

Scoring or routing ships on joined signal: code in your repo, join in your warehouse, or your money back.

You own the machine.

The join would be dangerous to rent, so you don't: the resolution rules and models sit as code in your repo, the joined data lives in your warehouse, and the workflows reading it run on your infrastructure. Swap your CRM or billing tool and the join absorbs the change; the model stays. Cancel any month and the system keeps running. Every workflow we ship after just deepens it.

In production

Real systems, running today.

Joins like this sit under 30+ owned commercial systems today, feeding every scoring, routing, and risk workflow above them.

The next step

Let's build the join everything else stands on.

Bring your four systems. In 20 minutes we'll find where they disagree about your accounts and scope the joined model, live in your warehouse inside a month.

prefer email? contact@workingmono.com