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AI & automation

AI automation that removes a specific job, not a headline

Most AI projects fail because they were scoped as a strategy instead of a task. We build the narrow thing that works, prove it, then widen it.

The problem

Why most of this goes wrong

The common pattern is a business buys a broad AI capability, deploys it against everything at once, discovers it is wrong 15% of the time in ways nobody anticipated, and switches it off. The technology was fine. The scope was the problem.

The version that works is narrow and boring. One job, defined tightly enough that you can list what a correct outcome looks like. Adversarial testing before launch, so you find the failure modes rather than your customers finding them. A person on the other side of every escalation path. A review cadence so the thing does not rot.

We run AI inside our own contact centre every day, which means we have watched it fail in most of the ways it fails. That is the experience being sold here — not enthusiasm.

What you get

What changes once this is running

One job, defined properly

Scoped to a task with a clear right answer: book the appointment, answer from the documentation, move the record between two systems.

Tested adversarially first

We try to break it before your customers do — accents, background noise, unusual requests, deliberate confusion, and the cases where it should refuse to answer.

A human escalation path

Every deployment has a defined point where it stops and hands to a person, with the context attached. Silent failure is the failure mode that costs you customers.

Reviewed, not abandoned

The monthly fee covers hosting, monitoring, and transcript review. An unmonitored agent degrades, and the client blames whoever built it.

Approach

What we build

Voice agents that take routine inbound calls and hand the rest to a person. Chatbots trained on your own documentation, with the same handoff. Process automation that removes a repetitive internal workflow — the file that gets re-keyed into a second system, the report someone assembles by hand every Monday.

If a workflow is genuinely simple, we will tell you to use a rule rather than a model. Rules are cheaper, faster, and they do not invent things.

Priced by scope, not per product: voice agents and chatbots each run Simple, Standard, or Complex, from $1,800 to build. A readiness assessment is $1,500, credited against any project booked within 90 days. See the pricing page.

FAQ

Questions people ask

Will an AI agent make things up about my business?

It can, and that is the failure mode we design against hardest. Agents are constrained to your source material, tested against questions with no good answer, and instructed to escalate rather than guess. Confident invention is worse than an unanswered question, and we treat it that way.

What happens when the AI cannot handle a call?

It hands over to a person with the transcript attached, so the caller does not repeat themselves. The escalation triggers are designed with you before launch — emotional callers, unusual requests, anything outside the defined job, and low-confidence responses.

How long before it is live?

A scoped voice agent is typically four to six weeks: discovery, build, adversarial testing, then a supervised soft launch on a share of real volume before it takes everything.

Do we need our documentation in order first?

Better documentation gives a better agent, but you do not need to fix everything first. Part of the build is turning what you already have into a usable knowledge base, and finding the gaps where your own team has been improvising.

Next step

Find out what this would look like for you

A 20-minute call. We ask what breaks, what it costs you, and who handles it now. If we are not the right fit we will say so.