The same task, every week
Quotes, status updates, weekly reports, client intake. Work that follows a pattern, takes hours and depends on one person remembering to do it.
AI systems built into your daily work, owned by your team.
This is the hands-on part: agents that sort the inbox and draft replies, turn meetings into tasks, build the weekly report and open a folder for every new client. I build them with your team, on your accounts, with a person approving anything that leaves the business.
Where it stalls
Where a built system makes the difference.
Information stuck in inboxes and calls
Decisions made in meetings that nobody writes down. Client requests buried in email. The knowledge is there, it just isn't where the work happens.
A workshop that didn't change the routine
The team learned the tools, then went back to the old way. Without a system built into the daily routine, new habits fade.
What gets built
Systems around one real process at a time.
Every system is built around a real process, tested on real cases and documented so your team can run it. Typical builds:
- Email agents that sort the inbox each morning, flag what needs a person and draft replies in your writing style.
- Meeting agents that turn a recorded call into decisions, tasks with owners and a follow-up draft.
- Reporting workflows that pull the numbers and assemble the weekly or monthly report in your format.
- Intake and onboarding that open a folder, a record and a checklist for every new client.
- Dashboards that show the few numbers the team actually acts on.
- Governance: who may use which tool with which data, review steps, and a log of what the agents did.
Example, for illustration
Quote requests arrive by email. An agent reads each one, pulls the client's history, drafts the quote in your template and puts it in a folder marked ready for review. A person checks the numbers and sends it. Nothing goes out on its own.
How it runs
From one process to a system the team runs.
The work follows the five stages of the Leverage‑to‑Ownership Method.
Map one process Leverage
We pick one process with clear value and map it step by step with the people who do it. If there was an audit, we start from its roadmap.
Build and test on real cases Launch
I build the first version on your accounts and test it on real cases from recent weeks, side by side with the way the work is done today.
Train the people who use it Learn
The team learns to run the system, check it and correct it. Corrections go back into the system, so it improves with use.
Lock in the rules Lock In
Approval steps, access rules, documentation and a short runbook, so the system keeps working when I'm not in the room.
Hand over Own
Your team runs it. We meet to tune what's built and to decide on the next process, if there is one.
Work runs in weekly meetings, one process at a time. Each process is scoped and agreed before it starts, and you can stop after any of them.
Who brings what
What you provide, and what you own.
What you provide
- One owner on your side who decides and tests.
- Access to the accounts the system needs, through each tool's own permission settings.
- Real examples, such as past emails, reports and meeting recordings, so the system is tested on your work.
- Time each week from the people who will use it, mostly in the weekly meeting.
What you own at the end
- The systems themselves, running on your accounts, not mine.
- The instructions, prompts and settings, in files your team can read and change.
- A runbook: how to run it, how to correct it, what to do when something fails.
- The governance rules: access, approvals and the log.
Proof
What this looks like in practice.
A digital analytics firm: three AI systems at work
One mapping session, then three systems built on the firm's own Claude account: email triage with replies drafted in the owner's writing style and a folder opened automatically for every new client, meeting summaries with decisions and tasks, and client-branded reports built from analytics data.
All three were running within two weeks of the first working session. Anything that goes to a client stays a draft until the owner sends it. The client's name stays private until they agree to be named.
My own business runs the same way
Agents prepare my meeting summaries, follow-ups, client records and draft replies. I read, correct and approve what goes out. I use what I recommend.
The AI Daily Brief
Every morning a pipeline I built reads about 30 sources, checks each item against its source and publishes up to three updates in Hebrew and English. It runs on its own, within written rules on what it may publish.
Five stages, written down
Every engagement follows the Leverage‑to‑Ownership Method: map, train, build, lock in the rules, hand over. You always know which stage you are in and what you own at the end of it.
Questions
Straight answers, before the first call.
How long until something works?
Weeks, not months. In the engagement above, all three systems were running within two weeks of the first working session. Each process gets its own timeline in the plan before work starts.
What drives the cost?
How many processes, how many tools need to be connected, how clean the data is, whether outputs go to clients (which means more review), and how much training the team needs. You get a scoped proposal after the first conversation; there are no prices on this site.
What about tool and subscription costs?
The systems run on your own subscriptions, paid directly to the vendor. Where possible I build on the plans you already have, and I tell you in advance if a system needs a higher plan or usage-based billing.
Do we need a developer?
No. I build on tools such as Claude, ChatGPT, Gemini and automation platforms, connected to what you already use. If a process truly needs custom software, I say so, and it becomes a separate decision.
How is our data handled?
Everything runs on your accounts, under your own agreement with each vendor. I get the access each system needs and nothing more, and you can revoke it at any time. Client data is not copied into my accounts.
Does it work in Hebrew?
Yes. The agents read and write Hebrew, and the drafts follow your own writing style. Some tools are weaker in Hebrew, for example in telling speakers apart in recorded calls; I test that early and design around it.
What needs human approval?
By default: anything sent to a client, any payment, any deletion and any change to client records. The agent prepares, a person approves. Internal summaries and drafts can run on their own.
What happens when something breaks?
Each system reports what it did, so a failure shows up instead of going quiet. The runbook covers the common fixes, and support after launch is part of the scope we agree on.
Related guides
Longer answers to the questions above, from the field notes.
Not a fit
When this is not the right call.
- Deep custom software or production integrations: that is a job for a development team.
- Round-the-clock support for critical systems.
- Fully automatic sending to clients, with no person in the loop.
- Regulated data that may not go into commercial AI tools.
- A team that won't take part in testing and training.
Next step
Start with a 20-minute call.
There are no prices on this site. After the first conversation you get a scoped proposal in writing. On WhatsApp, the message opens with a few short questions, so the first reply can already be useful.
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