AI business automation
that removes hours,
not headcount.

Cost tracks the number of processes and the depth of system integration rather than any per-user fee. A single scoped workflow — document extraction, or a grounded support assistant — sits in a very different bracket to agents acting across your CRM, accounting and messaging stack. We scope during the process audit and quote a fixed figure against a written specification, with running model costs metered separately and visible to you.

The ones with high volume, meaningful minutes per task, and a measurable error rate. We time the work during the audit and score every candidate on those three dimensions. The result usually surprises people: the process everyone complains about is often not the one costing the most hours. You get the ranked list whether or not you proceed with the build.

That is not what we build or what we recommend. The automations that pay back reliably remove repetitive work: data entry, extraction, first-line triage, routine drafting. The same team then handles more volume and spends its time on judgement. We measure success in hours returned, not roles removed, and every consequential action keeps a human approval step.

Three ways. Answers are grounded in retrieval over your own documents with citations, so output is traceable to a source. The system is built with an explicit refusal path, so it declines rather than invents. And every change runs against an evaluation set drawn from your real cases, so accuracy is a number we monitor rather than an impression. Anything below the confidence threshold escalates to a person.

Your data is not used to train anybody's model. Personal data is redacted before any model call, processing runs on infrastructure and provider accounts you own, and retention is configured deliberately rather than left at a default. Every request is logged for audit, and where a workload is sensitive enough we run open-weight models inside your own boundary so nothing leaves it.

Yes, and that is usually the point. We integrate with your CRM, ERP, accounting, storage and messaging tools over their APIs rather than asking your team to adopt another interface. Where a system has no API, we work through database access or scheduled file exchange. Adding retrieval and drafting inside a tool people already open every day is far more likely to be used than a new tool beside it.

We time the manual process before automating it, and that baseline becomes the measure. After rollout we report the same measure — hours consumed, tasks handled, error and escalation rate — against it. If the number has not moved, that is reported as a finding rather than explained away. We then look at whether the process was the wrong candidate or the approach was.

The feasibility result lands in week three, before the main build is committed. A first automation is typically live in shadow mode by week eight and running supervised by week nine, with the hours-saved comparison in week ten. Starting with one scoped process rather than a platform programme is what makes that timeline realistic.