About
A seasoned operator building AI into your GTM motion.
How the practice works
The work runs in two layers: the foundation that makes AI reliable (clean data, definitions written down, every workflow tested before your team depends on it), and the workflows on top, from first touch to renewal.
Two kinds of agents live on that base: automations that run like a product and deliver into the tools people already use, and co-pilots each person works with, on access that keeps your data safe. Workflows ship one at a time, with time blocked for your team to adopt each.
I work solo and fractionally, start to finish. Because I've run the function, I can act independently and fast: I diagnose with qualitative and quantitative data, align with your teams on what's slowing growth, then build the systems that fix it.
The outcome is improved conversion and speed at the stages where your growth stalls most, and customers who keep growing after they buy. You own everything: the systems, the documentation, and the working knowledge to run them without me.
Where the name comes from
There's a lot you don't control. A new competitor shows up, your best rep quits, a buyer's budget gets cut the week you were going to close. That's the first arrow, and every business takes it.
The second arrow is the damage that didn't have to happen: leads nobody followed up, targeting that was a guess, churn nobody saw coming, a team buried in manual work. Better systems and a clearer strategy can take most of that one off the table. It isn't easy, but it's the arrow you get to choose.
Who's behind it
Aaron Altamura.
- Head of revenue operations, twiceAt venture- and private-equity-backed B2B SaaS companies.
- Former head of financeRan finance as well as revenue operations, so the work fits the whole company, not just sales.
- Builds with Claude Code, hands-onAgents, the context they run on, and the review gates and tests that earn them autonomy.
- Certified in Revenue ArchitectureUses Winning by Design’s bowtie to find where growth stalls.
Over a decade in those seats: the systems, the data, and the process the revenue engine runs on. I'm CPA-trained and a former fractional CFO, so I've been the one approving spend like this. I led AI adoption as a full-time operator: AI analysis of 5,068 sales calls that surfaced the customer pains converting 4-5x higher, deal qualification filled straight from call recordings, and a stack rebuild that cut GTM SaaS spend 75%.
A public example of that layer is Torrance Watch (torrancewatch.org): four messy government data systems joined into one dataset trustworthy enough to publish, which brought election transparency to a city of 96,000 voters, read by an estimated 26,000 people in three months. Usable data, written context, review gates, and evals, the same things I build for clients.
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