About · CHRM
Stay human. Let AI do the rest.
A design philosophy, in three notes. About what AI should do, what it shouldn’t, and why most enterprise software is currently doing the opposite.
AI that creates more work is a failure.
Most enterprise AI products in the last three years have made the same mistake. They generated something (a draft, a summary, a score) and handed it to a human to review, edit, approve, and file. The interface looks like progress. The work it produced is new work.
A salesperson now reviews the AI’s call summary. Then edits it. Then copies the relevant bits into the CRM. Then approves the AI’s drafted follow-up. Then edits it. Then sends it. The human is now doing what they did before, plus checking the AI’s homework.
Work surfaceis the right unit to measure. Anything that adds dashboards, review queues, approval steps, or another tab to open in the morning is failure — even if the model behind it is excellent. AI should reduce the work surface to zero in the places it can. Where it can’t, it shouldn’t be there at all.
CHRM is built around this. It does the work end to end. It writes to your CRM directly. It sends the follow-up directly. It flags the risk to the person who needs to act on it, not to a queue nobody opens. If a feature can’t do its work without asking the human to do more work first, it isn’t shipped.
“Work surface is the right unit.
AI that adds it is a failure, however good the model is.”
What humans are for, and AI never will be.
There are two things AI cannot do. Not in this generation, not in the next one, not in any version we can see from here. Building trust, and providing direction. Everything else is execution, and execution is where AI belongs.
A great salesperson is not faster at typing notes than the next person. They are the person a buyer trusts in the room when the quarter is closing and the technical answer is incomplete. A great sales leader is not better at reading dashboards. They are the person who decides which deals get protected and which campaigns get killed when the data is ambiguous.
Build trust.
The room with the buyer. The candid moment with a teammate. The conversation that decides whether your customer renews. These are not productivity problems. AI doesn’t belong here — except to clear the path so the human can be present for them.
Provide direction.
Where to point the team. Which deals to fight for. Which experiments to kill. Which playbook chapter is wrong. These are judgement calls, not output. AI can surface the evidence; only humans can decide.
The CHRM product is a long argument that these two jobs deserve most of a working day. Right now they get the leftovers. The average closer spends two to four hours a day on admin and the people they meet feel it. We’re building the layer that gives those hours back.
GTM engineers are the new Implementation Industrial Complex.
Twenty years ago, large enterprises ran on Oracle. The Oracle licence cost a million dollars. The Oracle implementationcost ten. A whole consulting industry grew around the gap — Accenture, Deloitte, the staff at boutique system integrators — paid handsomely to wire the product to the company that bought it.
That industry never disappeared. It evolved. Today its modern form sits inside revenue organisations under the title GTM engineer.The job is to wire your sales stack together. To connect Salesforce to Outreach to Gong to Apollo to Common Room to Clay to the next thing your CRO read about on LinkedIn. To maintain the wiring as each of those tools changes its API every quarter, deprecates its webhooks, and ships a new MCP server that doesn’t quite work yet.
The output of that work is fragile. The integrations break. The workflows degrade. The maintenance never ends. Mid-stage companies are spending the equivalent of two senior engineering salaries to keep the duct tape stuck to a stack that was supposed to be off-the-shelf software.
“We’re paying GTM engineers to maintain APIs
between products that should have just worked.”
CHRM is built so that this job doesn’t exist for our customers. You connect four things. We read your schema. We do the work. If an upstream API changes, we absorb it — you find out by reading our changelog, not by paying a contractor to fix it.
That is the bet. Not better AI. Not more workflow nodes. Software that does not require an Industrial Complex to make it run.
Stay human. Let AI do the rest.
That sentence is the product. The first half is a promise to the people on your team: that the work that requires a human is the work they’ll get to do. The second half is a promise to you — that the rest of it will be handled, by software that doesn’t need a quarterly project plan to deliver on its end of the deal.
If you’re building a revenue team and that resonates, talk to us.