Walk into every client call prepared
Turn intake forms, previous notes and current context into a focused agenda, better questions and the points that deserve attention.
Custom AI agents for coaches and consultants
Save time every week with an AI agent you know how to use.
Most coaches and consultants know AI matters. The hard part is knowing where it saves time, what to build and how to use it. I find the right use case, build one agent around your method and train you on real work.
- Your best AI use case defined first
- One finished, tested agent you understand
- Instructions, tests and a guide you keep
Your first win is not an agent. It is knowing what to build.
Most people start with a tool. That is backwards. We start with the work you repeat and the result you want. In the first session, you learn what AI should do, why it matters and whether it is worth building.
Turn a conversation into action
Use notes or transcripts to create a client-ready summary, clear next steps and a useful follow-up email.
Help clients keep moving between calls
Give clients bounded guidance based on your method without pretending the agent is you or removing human judgment.
Stop starting from zero
Use your ideas, calls and frameworks to support research, proposals, onboarding, documentation and content without flattening your voice.
One useful agent. Built for you. Taught to you.
You do not get a generic chatbot, a folder of prompts or a 40-page manual nobody wants to read.
You get one agent built around real work in your business. Then we use it together until you understand where it helps, how to use it and when a person still needs to take over.
AI use case map
A clear definition of the user, task, inputs, output, value, boundaries and first result worth measuring.
Custom agent build
Instructions, questions, decisions and response formats structured around your method and standards.
Knowledge setup
The useful source material organised for the agent, with irrelevant or risky material removed.
Boundaries and human handoffs
Clear rules for what the agent can do, what it should refuse and when you need to take over.
A 20-case stress test
Normal, unclear, difficult and out-of-bounds situations tested before the agent is trusted with real work.
Real-work training
A working session using your own examples so you learn by doing, not by watching another generic AI tutorial.
Quick-start operating guide
A simple guide showing what to give the agent, how to use the output and how to spot when the answer needs human review.
Ownership package
Your instructions, knowledge map, tests and documentation delivered in files you can read and keep.
First 30-day improvement plan
A simple way to measure use, time saved, output quality, mistakes and the next improvement worth making.
You should not need me forever to use what I built.
By handover, you should be able to explain what the agent does, give it the right inputs, recognise a good output and know when human judgment is required.
You receive the instructions, knowledge map, tests and guide. The core logic is not hidden in a box only I can open.
- Learn using your own real examples
- Know how to spot a weak or risky answer
- Update the source knowledge yourself
- Keep the files that create the value
Built in the real world
AI agents are not a side project for me. I use them to run my advisory business and build software.
I learned this the expensive way: bad workflows, brittle automations, vague prompts, broken integrations and tools that looked clever but saved no time. I made those mistakes already. The products below are the receipts. You get the patterns that worked, the waste stripped out and a faster path to an agent you can trust.
Local Community Platform
An open-source platform for local communities to preserve posts, resources, member profiles and community-shaped events beyond a busy group chat.
What this proves Reusable product architecture, invite-gated membership, public participation, permissions and a real community running on the platform.
GitHub 
Command Center
The private system I use to manage projects, prospects, CRM, calls, content, publishing and AI agents in one place.
What this proves Operational context, agents connected to real work and a private system built around its user.
ServiceKit
A Jobber and ServiceTitan alternative with CRM, scheduling, dispatch, quotes, invoices, payments, timesheets, reviews and AI-assisted workflows.
What this proves Complete business workflows, multiple users, data, permissions and work that cannot fit inside a prompt.
Booking Kit
A branded booking system with availability, public booking pages, calendar connections, email, embeds, paid bookings and an owner dashboard.
What this proves Client experience, calendar integrations, availability rules and the operations that happen after the click.
PostKit
A self-hosted social scheduler for composing, queuing, publishing, media, accounts, retries and results.
What this proves Queues, APIs, states, retries and the unglamorous work that makes automation reliable.
Signal to Noise Ratio
A Chrome extension that tracks whether browser time is moving the work forward or leaking into distraction. All data stays in the browser.
What this proves Product judgment, local privacy and a small tool built to change one specific behaviour.
Media Downloader
A Chrome, Brave and Edge extension with a local yt-dlp server that detects media, downloads files and HLS or DASH streams, and keeps progress on the user’s machine.
What this proves Browser extension work, native messaging, a local server, stream handling, background state, retries, settings and desktop integration.
From AI confusion to one useful system.
Find the value
We identify the repeated work, the desired result and the first useful win. If an agent is overkill, I will tell you.
Build and test it
I turn your method into instructions, knowledge, boundaries and an agent that survives difficult cases.
Learn on your own work
We use the agent together with your actual examples. You see what changes, why it matters and how to improve it.
A useful agent starts with real work and real expertise.
AI can extend a method. It cannot invent the method that does not exist yet.
This tends to work when
- You already have paying clients
- Your work follows a method you can explain
- One task happens often enough to matter
- You want to understand and use the agent yourself
- You are willing to test it on safe, real examples
- You want better delivery, not the disappearance of the human work
It is the wrong build when
- You want a magic lead machine without a strong offer or distribution
- You expect AI to invent expertise you do not have
- The agent would give unsupervised medical, legal, financial or therapeutic advice
- You want five agents before proving one is useful
- You want the words AI agent on your website more than a better business process
This service is new. The ability to build is not.
The software on this page is real. What I do not yet have is a wall of case studies from this exact service. I would rather say that plainly and earn the proof through useful work.
Start with one task you repeat
Find the AI use case worth building first.
Tell me where time disappears in your week. I will help you work out whether AI should handle it, support it or stay out of it.