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Most companies are experimenting with AI. Very few are running it.

There is a wide gap between a team trying chatbots and an organization where AI reliably does real operational work every day. Closing that gap is mostly unglamorous: understanding a process well enough to automate it, deciding what the system is allowed to do on its own, and knowing what happens when it gets something wrong.

That is the work we are building toward, starting with IT and operations because it is the domain we know best.

Where this actually stands The first agent is in development. It has not been deployed with a customer yet, and we are not going to describe it as a finished product. We would rather build the first few alongside a small number of companies who have a repetitive operational problem worth solving and the patience to shape something new.

The problem

Pilots are easy. Production is the hard part.

Most AI efforts stall in the same place. A demo works, everyone is impressed, and then it meets reality: the model is confidently wrong once, nobody knows who is accountable, it has no access to the systems where the actual work lives, and there is no answer to "what happens when it does something we did not expect."

The interesting engineering is not the model. It is everything around it. What the system is allowed to do without asking. What it must escalate. How a human reviews a decision after the fact. Which actions are reversible and which are not.

Start recommend-only

The first version proposes and a person decides. That builds the track record you need before anything is allowed to act on its own.

Policy decides, not the model

The language model proposes an action. Deterministic rules decide whether it is permitted. That boundary is what makes the behavior predictable.

Narrow beats general

An agent that handles one recurring situation well is worth more than one that handles everything unreliably.

Measure against doing nothing

If it does not save measurable hours or catch things people miss, it should not ship. Interesting is not the bar.

The problem space

What we are building toward

These are the kinds of problems the work is aimed at, not a product catalog. What gets built depends on what a business actually needs, and the honest answer is sometimes that automation is not the right tool.

IT operations agents

Triage alerts, correlate them, and propose a fix, so a human is woken up for the ones that need judgment rather than all of them.

Internal knowledge agents

Answers that currently live in one employee's head, or in a document nobody can find, made retrievable by anyone who needs them.

AI-assisted troubleshooting

Shorten the path from symptom to cause by pulling together the context a technician would otherwise gather by hand.

Vendor and contract automation

Renewals, terms, and escalators tracked automatically instead of discovered after an auto-renewal has already fired.

Reporting automation

The recurring spreadsheet assembly that consumes a day every month and produces the same shape of answer each time.

Workflow automation

Handoffs between systems that were never designed to talk to each other, currently bridged by a person copying data.

How an engagement runs

We look at the process before we look at the technology.

Find the repetitive workSit with the people doing it. What happens every week, takes too long, and follows rules somebody could write down?
Put a number on itHours, error rate, delay, cost. If the number is small, we say so and you keep your money.
Decide what should be automated at allSome processes should be eliminated rather than automated. Some need a person for good reasons. Automating a bad process just makes it faster.
Build the narrow versionOne process, recommend-only, with clear boundaries on what it can do. Run it alongside the humans doing the work today.
Widen only once it has earned itMore autonomy after it has a track record, not before. Same for scope.

Design partners

We are looking for a few companies to build this with.

Not a pilot program with a price tag attached. A working relationship where you bring a real operational problem and we build against it, with terms we agree on up front given that you are taking a chance on something early.

Likely a good fit

A recurring operational process that eats real hours every week
Someone internally who owns the process and can explain how it actually works
Willingness to start with recommend-only rather than full automation
Patience with something being built rather than bought

Probably not a fit yet

Needing a finished, supported product on a fixed timeline
A process nobody can describe clearly, which usually means fixing it comes first
AI as a strategic objective rather than a specific problem to solve
Requirements that need certifications or compliance attestations we do not yet hold