Applied AI and GTM,

engineered forward.

UNKAPPED is a GTM and engineering partner for companies from pre-seed to enterprise. We find what is capping revenue, then build and run the system that removes it. The work spans three disciplines: growth systems, covering GTM engineering, revenue operations, ABM and demand; applied AI, covering workflow automation, revenue agents and forward-deployed engineers; and leadership presence, covering founder and executive storytelling and product marketing.

Both, and the building is the larger half. A consultancy hands over a recommendation. We name the constraint, engineer the system inside your own environment, run it to an agreed standard, and train your team to own it. What is built sits in your accounts and outlives the engagement.

An agency sells activity by the month, and little of what it produces is built inside your environment, so little of it survives the contract. We sell a named result, measured against the number on a baseline agreed up front.

It is how most engagements start: a short, fixed-scope read of how ready your go-to-market and AI foundations are to scale, and what is most likely capping growth. The findings are yours whether or not we work together. We walk through the detail on a first call.

Revenue agents are AI agents that run parts of the commercial process inside your existing systems, such as enrichment, routing, forecast hygiene, research and drafting, with evaluations and human review gates. Forward-deployed engineers are named UnKapped engineers who build and run those systems inside your environment, then hand them to an owner.

Both. From pre-seed to Series C the problem is usually the number: how to reach it, and what has to be true. In mid-market and enterprise it is usually the process between teams. The method is the same; what we build changes with the altitude.

They are pointed at the product, and they should be. Commercial systems are a different surface, and borrowing product engineers costs roadmap. Our forward-deployed engineers work in customer stacks every week and are scored against a skills matrix before they meet a customer.

A hire lands about two quarters after the need, with one discipline, where most constraints need three or four. Hire once the constraint is named and the system exists, so the person you hire inherits something that works rather than a blank page.

Founder and executive storytelling for technical companies. We write and run content across LinkedIn, Substack and X, engage every day with the accounts and investors that matter, and handle positioning and product marketing, so the people buying from you have heard of you first.

A senior engineer who sits inside your team and your systems, maps the real workflow, builds on what you already own and stays until it runs in production. Then hands it over with a runbook and a named internal owner. Not a consultant with a deck, not an agency with a dashboard.

AI attached to one named business process, running on your own data and systems, with an owner, a human review gate and a number it is measured on. A chat licence is not Applied AI. An agent that triages tickets inside your helpdesk, scored against last quarter's tickets, is.

Treating pipeline as a system rather than a campaign: data resolved once, signals scored with a reason, routing with SLAs, plays by tier, results looped back into scoring every week. Built inside your CRM, not beside it.

Most tools sit on the same messy data and act outside the system of record, which is why they become shelfware. Buy for generic, high-volume tasks. For anything cross-functional or tied to your own process, build on what you own.

A baseline before the build, then hours returned, cost per task, accuracy against a test set, override rate and the revenue line it touches, on a monthly register your leadership reads.