Every team is being asked to do more with AI. Most stall in the same place: the assistant doesn’t know what counts as an account, which revenue number is real, or which customers belong in which segment. We fix that layer first, then put AI to work in marketing and go-to-market on top of it.
Someone exports Meta, Google and TikTok into a sheet every week. Meta says it drove 40% of revenue, GA says 15%, and nobody can say which twelve creatives are actually working.
The target account list is a spreadsheet from last year. Sales and marketing score accounts differently, research happens one tab at a time, and nobody knows which signals actually predict a deal.
Revenue has three definitions. "Active customer" means something different in every dashboard. Someone points an AI assistant at the warehouse and it confidently returns the wrong number.
Point a capable model at a warehouse and it will write fluent SQL against the wrong table. It doesn’t know that “customer” means something different to finance and to growth, that two accounts are the same company, or that last quarter’s revenue was restated.
So we start by writing your business down in a form a machine can use: a semantic layer with one definition per metric, an ontology of the things you sell to and how they relate, data models and segments both people and agents query. Everything after that, from reporting to account research to creative analysis, gets cheaper and more accurate.
There are more AI tools than anyone can evaluate, and every one of them demos well. Most companies we meet have trialed a few in different teams and still can’t say which problem they’re solving, or whether the quote is a fair price.
We’ve run SaaS buying for companies, and we know the market, the people building and using these tools, and what most SaaS actually costs. In two weeks you get a company-wide map of what to buy, build or skip, a shortlist with references, and what each tool should cost you. We work for you, not the vendors.
How it works →Fixed scope, fixed price, senior people who have done this inside growth and data teams. We work in your warehouse, your CRM, your BI tool and your ad accounts, and only build where nothing fits. If the baseline shows it isn’t worth fixing, we tell you in week one and stop.
Hours spent on reporting, time from question to answer, metrics nobody agrees on, accounts nobody is working. Written down, agreed with you.
Definitions, models, pipelines and agents built alongside the people who will use them. AI handles the reading, tagging and research; your rules and your team make the decisions.
Same metrics, measured again. Your team owns it. We stay on a light retainer only if you want us to.
Ran growth and data. Led growth and data teams at Opendoor and Alt. We know what it’s like to be asked for the number on Monday.
18 months embedded. Forward-deployed on marketing data, measurement and self-serve analytics for consumer brands, a national retailer and a payments company.
We build, not just advise. When nothing on the market fits, we write the software ourselves. Some of it became products.
Most AI projects fail on the data, not the model. Fix the definitions first. The clever part gets cheaper every month.
AI is good at reading, not deciding. Let it tag creative, research accounts and answer questions. Your team decides where the money goes.
If we can’t measure it, we won’t sell it. Every engagement starts with a number and ends with the same number, remeasured.
The report that takes a day, the account list nobody trusts, or the vendor shortlist you’re stuck on. Thirty minutes later you’ll know what we’d measure, roughly what it costs, and whether we think it’s worth fixing.
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