An AI operating system for the work your team does by hand
I map how your operations actually run, document the rules nobody wrote down, and put AI agents to work on the biggest manual jobs, each measured against a baseline your team signs. Built for B2B tech companies hiring just to keep up.
Your next hire is a knowledge layer
Most AI projects stall because the agents don't know how your business runs. The rules live in people's heads, old Slack threads and exceptions nobody wrote down.
So I start with the knowledge layer: your SOPs, exceptions and decision rules, documented in Notion and approved by the people who own them. Every agent runs on that, not on guesswork.
Sound familiar? You're hiring to keep up. Your team re-keys data between tools, chases approvals and answers the same internal questions every day. You know AI matters, but not where to start.
The first agent goes live in shadow mode next to your manual process, measured against a baseline you signed. You see exactly how much time it saves.
Audit, Document, Build. First agent live in about 30 days.
Audit, weeks 1 to 2. We map your operation end to end, cost every manual handoff and re-keying step, and review SSO, access and data flows. You get a ranked roadmap and a signed time baseline.
Document, weeks 2 to 4. Your SOPs, exceptions and decision rules go into a Notion knowledge layer, approved by each owner. Every agent gets a written spec and eval cases from real past work.
Build, one agent at a time. Each agent runs in shadow mode first, goes live at L2 with a person approving its actions, and moves to L3 after three clean reviewed runs.
The work we take off your team
Back office. Invoicing, reconciliation, expense coding and contract data extraction, taken out of copy and paste.
Customer. Support triage, an FAQ engine built on your own docs, onboarding coordination and churn watch.
Operations. Status updates, meeting notes turned into tasks, approval routing and the weekly ops report.
Intelligence. Competitor watch, market research and an internal assistant that answers from your SOPs.
Identity and access. Every agent gets its own scoped identity through your SSO and SCIM, and every action is logged.
Revenue workflows like lead routing and CRM cleanup run through GTM Engineering.
How results are measured
Step 1. In the Audit we time each task with the person who does it. They sign off the baseline in writing.
Step 2. Every approved agent run is logged: what it did, who approved it, and the baseline time it replaced.
Step 3. After 30 live days we measure again. Hours back times your loaded hourly rate, annualised, is the time saved for that workflow.
Step 4. A workflow that stops saving time gets fixed or switched off.
Step 5. Each month you get a one-page report per workflow: runs, hours back and dollars back. Nothing is self-reported.
Is this a fit?
Good fit: a B2B tech company with repeatable operational work, people hired mainly to move data between tools, and a leader who can own the rollout and sign off SOPs.
Not a fit yet: processes that change every week, no one who can approve an SOP, or anyone looking for a chatbot demo instead of a measured result.
The standard: every workflow is measured against a baseline your team signed, and one that saves no measurable time gets fixed or switched off.
Pay for a working system, not activity
This offer: three phases with named deliverables. An Audit, your SOPs documented as a knowledge layer, then agents built one at a time and measured against a baseline your team signed. You own the knowledge layer, agents and run logs, all in your accounts.
An AI agency retainer: you pay monthly for hours and deliverables, whether or not anything saves time. The context about how your business runs usually stays with the agency.
Another ops hire: you pay a salary to move the same data between the same tools, and the knowledge leaves when they do.
Built safely in your accounts
You hold the keys. Your knowledge layer, agents, runbooks and test cases live in accounts your company owns.
Every agent has its own identity. Each one is provisioned through your SSO with the minimum access it needs, and every action is logged.
Your tools, connected. Notion for knowledge and runbooks, Relevance AI when a workflow crosses teams, Slack where your team already works.
Models chosen by testing. Picked on your workflow, not by loyalty to a vendor. If a better model shows up, we swap it and retest.
Agents earn autonomy on evidence. Every agent goes live at L2 Copilot, with a person approving each action. It moves to L3 Autopilot after three clean reviewed runs, and to L4 only once it has its own evals, a verifier and one logged fix that passed.
Who you will work with
Kevin Payne is an AI engineer and GTM operator who installs AI workflows for B2B SaaS teams. 10+ years in B2B SaaS demand generation and RevOps across Techstars, a16z and YC portfolio companies.
I run my own business and personal life on the same kind of AI operating system, seven days a week: Notion as the knowledge layer, agents that do the work, and a run log that shows what they did.
Want to know what your manual work costs? Book a free 30-minute coffee chat above. Prefer email? Write to info@kevintpayne.com.
Your questions
Who is this for?
Have you done this before?
What if my team will not adopt it?
Who pays for software and AI usage?
Is our data safe?
Do I have to continue after the Audit?
How is this different from GTM Engineering?
How do you measure time saved?
What if a workflow saves nothing?
