Custom AI employees.
Built for your business.

That's your decision, not ours, and we'd encourage you not to lead with it. What we sell is capacity, coverage and consistency: the same team handling several times the volume, every lead followed up, every invoice chased, a response at 2am. The stronger move is usually to redeploy people onto work that genuinely needs a human rather than to cut headcount. If the economics eventually change what your org chart looks like, that's a business decision you'll make with better information than you have today.

We'll tell you, on the first call, before you've paid anything. If the work is repetitive, digital, rules-and-judgement based and performed through software, there's a good chance an AI employee can hold meaningful parts of it. If it depends on physical presence, relationships that can't be delegated, constantly shifting undocumented rules, or data we can't reach, we'll say so. A no from us is worth more than a yes from someone who bills either way.

An authority matrix written before a line of code exists. Every action the employee could take is classified as autonomous, requires-approval, or forbidden. The forbidden ones are enforced in code, not requested in a prompt. Issuing refunds, changing bank details, deleting records, negotiating outside set limits: these are structurally unavailable to the employee, not merely discouraged. On top of that: a probation period, escalation rules, an audit trail of every action, and spending limits. The full mechanics are in how we handle your access.

You decide the wording, and it gets written down before anything is built. How the employee introduces itself, and how it answers questions about what it is, is agreed with you and written into the job description alongside the authority matrix, before the build starts. If your industry or your jurisdiction sets a specific disclosure rule, that gets fixed in the same document.

Whichever produces the most reliable employee for your job, and we'll tell you exactly what we picked and why. We're not a reseller for any model provider, so we have no reason to force one into a role it's wrong for. Model choice is an implementation decision we make on evidence and revisit when better options appear. What you're buying is the job being done, not a particular vendor's logo.

For a role touching one or two systems, typically two to four weeks to deployment, then a three-week probation before full autonomy. Builds spanning several systems run four to eight weeks depending on how cooperative those systems are. The honest variable is almost never the AI. It's how quickly we can get credentialed access to your systems and a decision-maker who can answer questions about edge cases.

Two models, agreed before development starts, never after. In managed employment, we own and operate the infrastructure and you pay to keep the employee on staff. That is the common choice, and the cheaper one. In an owned deployment, you take the code and run it on your infrastructure; that costs substantially more and the IP boundaries are written into the agreement up front. Your data is always yours in both.

They go into an access-controlled secrets vault, never into code or prompts, and the employee works under its own service account scoped to the narrowest permissions the job allows. Every action it takes is logged, you can revoke the account in your own systems at any time, and when the engagement ends, access ends with it. The full mechanics are in how we handle your access.

There's no price list, deliberately. Every employee is designed around one specific job, so the quote is built the same way: one implementation figure and one ongoing management figure, in writing, after the free design call and before you commit a dollar. On the call we also do the arithmetic on what the job costs you today, so you can judge the quote against something real. If the numbers don't favor you, that's what we'll tell you.

Monitoring and incident response, model and usage costs, integration maintenance when your systems change underneath us, failure review on anything that went wrong, new training for edge cases the employee hasn't met before, small workflow improvements, security updates, and a written monthly performance review. An AI employee left unattended degrades. Your systems change, your policies change, edge cases accumulate. Ongoing management is what keeps it employed rather than merely installed.

Two things are true before you're ever depending on it. It does not deploy at all if it misses the pass mark on the acceptance test you agreed up front, so an employee that can't do the job never reaches your customers. And once it is live it works under its own service account in your systems, which you can disable yourself at any time; it stops there and then, without asking us first.