Cost of Operations

“We need to do more work at a lower cost.”

Where can software, AI, agents, and better-connected systems take work out of the operating model rather than simply make people a little faster?

AI does the routine work.

Talk it over
FIG. 2  ·  GROWTH AGAINST HEADCOUNT Y1Y2Y3Y4Y5 REVENUE PEOPLE The distance never changes. Every increment arrives with a hire attached. THE LINES NEVER SEPARATE Expand
McKesson
Intuit Mailchimp
Cox 2M
Scientific Games
Office Depot
NAPA
CommuniCare
Sound familiar?

You grew. Your margin did not.

A trained person reads a case, makes a call, types it in. Multiply that by the month and the only lever left is another hire.

Every new client arrives with a req attached

Sales closes on Friday. Recruiting hears about it on Monday.
Three months before a new hire is useful. Six before they are good.
The same case gets handled differently depending on who picked it up.
Someone leaves and you pay for the training twice.

You bought the licenses. The headcount did not move.

Everyone types faster. The same number of people still do the work.
It drafts the note. It does not decide what belongs in the note.
It handles the routine sixty percent, which was never the expensive part.
Nobody can point at a role that got smaller.

Nobody sells the thing you actually need

Your rules are yours. They are not in anyone’s product.
The exceptions are where the money is, and exceptions are what products handle worst.
Your most experienced people spend the day writing things down instead of deciding.
You have looked. It does not exist.

“We’re doing this all manually. It’s not scalable. It’s all human capital.”

Growth officer, care-navigation company

How we solve it

Put AI in the work, not on the desktop

A license makes one person faster. Building it into the process is what changes what the process costs.

Discover phase
Discover

Watch the work as it is really done, including the exceptions nobody wrote down.

Experiment phase
Experiment

Model the hardest call first. If the accuracy will not hold, you know in weeks.

Engineer phase
Engineer

Build it into the workflow, connected to the real systems, with the evidence trail people need to trust it.

Optimize phase
Optimize

Watch the accuracy in production. A model that drifts quietly is worse than no model.

The full method →

Nobody pays for efficiency that doesn’t save real money. The value is often in the risk you remove: the audit you’d fail, the fine you’d pay.

Where to start

Start with Forward Deployed Engineering

AI pilots don’t create value until they fit the way people actually work. Forward Deployed Engineering puts senior engineers in your operation until one workflow runs in production.

It starts with the workflow

A two to four week look at the data, systems, business rules, and people the work depends on.

A pilot in production

An eight to twelve week pilot on real records, with human review and a measured result.

Any model

Claude, GPT, Gemini, or open source, whatever fits your security and economics.

Yours from day one

The code, the integrations, the documentation, and the measurement framework.

An implementation service, not consulting and not staff augmentation. The team owns the outcome until the workflow earns trust on the floor.

If you don’t yet know which workflow to start with, an Assessment ranks them by impact and builds the business case first.

Not sure which fits? Call us to talk it over.

How Forward Deployed Engineering works →

Tell us which job keeps costing you a hire

Thirty minutes. Walk us through the work and who does it, and we will tell you whether it can be encoded and what that takes.

An architect takes the call, not a salesperson.
We will tell you honestly whether we can help.
And roughly what it would take.
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