AI RevenueEngineering
AI RevenueYour team already has AI tools. What it does not have is AI built into the actual work: how a lead gets qualified, how follow up happens, how campaigns get operated, how the CRM stays true. An engineer embeds with your team and ships that into production.
Almost every revenue team now has AI somewhere in the stack. Very few have it inside the process, where the work happens. The tools sit alongside the workflow, so someone has to remember to open them, and most of the time nobody does.
One named engineer works in your stack and ships to production. The work spans your sales process and your marketing operations, because the handoff between them is usually where the money is.
Qualification, scoring, routing, follow up, and reply handling built into the workflow your reps already use, so the system does the work whether or not anyone remembers to.
The operational layer under marketing: list building, enrichment, segmentation, personalization at volume, and the reporting that tells you which of it worked.
CRM, marketing automation, product telemetry, billing, warehouse. Built two way, built in production, documented, and yours to keep when the engagement ends.
Every engagement is scoped and quoted on a call, with a milestone rubric agreed before kickoff and a published change order process. Nothing is billed against a vague retainer.
One integration, shipped to production. Fixed scope, fixed fee.
4 to 6 weeksStanding capacity across your sales process and marketing operations.
3 month minimum, then monthlyMore than one engineer, for organizations with continuous build demand across the business.
Scoped separatelyEverything built is yours. Code, pipelines, documentation, and access transfer to you. The engagement can end without the system ending with it.
Most of what gets sold as AI transformation is one of the four things below. If that is what you are shopping for, this will disappoint you.
There is no assessment phase that ends in a roadmap for your team to go build. The engineer writes the code. If the deliverable is a document, you paid for the wrong thing.
Nothing new to log into. The work happens inside the systems you already own and already pay for, which is the only reason anyone will keep using it after launch.
Pilots are how AI projects die. Work ships to production or it does not count, and scope is set small enough to make that possible inside a defined window.
AI applied to an undefined process makes the mess run faster. If nobody can describe how a lead moves from first touch to closed, fix that first and come back. It will cost you less.