Go-to-market systems for B2B companies. Built, then handed over.
I build the go-to-market systems B2B companies don't have — outbound, revenue operations, and internal tools — then hand them over. Every engagement is fixed in scope and ends with you owning what I built.
Buy the infrastructure. Own the intelligence. Most of this work gets sold to you as output you have to keep buying, or as software where the useful logic stays locked inside someone else's product. I write it into a repository with your name on it, then teach your team how to change it.
Most engagements combine two of the three, because outbound tends to expose whatever is wrong underneath it.
“We have capacity we can't fill.”
Infrastructure, the companies and contacts worth reaching, and automated outbound running against them.
“We can't trust our own numbers.”
Migration, a sales process redesigned around how you actually sell, and the reporting that tells you whether any of it worked.
“My experts spend their day reading paperwork.”
Software built on your own history that gets an expensive person to an answer in a minute.
Outbound
Built from scratch at two companies that had never done outbound. $2.7M in new revenue at a $30M commercial glazing contractor, $3.5M at an accounting and finance consulting firm.
Neither of them added a salesperson to get it.
CRM and revenue operations
Two rebuilds on two platforms — an Acumatica implementation at the glazing contractor, a HubSpot rebuild at an identity-security software company.
Migration and enrichment, pipeline stages, qualification gates, handoff logic, and a sales process redesigned around how the company actually sells. Then the automation on top — stage progressions, deal creation, note capture, signal detection — and the attribution and reporting that says whether any of it worked, with every field documented in place.
Then told them the campaign they had hired me to prove out was not sourcing anyone. They kept the build and changed the strategy.
Proprietary AI products
Reads incoming bid documents, scores them, and builds a quick quote from historical pricing and the likelihood of winning. The model extracts details, measurements and pricing to produce the takeoff; scoring is deterministic code against a published rate card, so every number is auditable.
36% → 24% median error, at $0.26 a bid. Over the same period the contractor's commercial win rate went 64% → 75% and commercial revenue $9.8M → $13.3M.
Where it cannot price something, it says so: six job types report a floor with no range at all.