AI automation services: how much of your team's week is work that software should be doing?

An AI automation consultancy finds the repetitive, judgement-light work inside your business and moves it to software. At AiS LABZ that means three things: LLM assistants grounded in your own documents, scoring models that rank leads or tickets by likely value, and workflow automation that connects the systems you already run. Every automation ships with a human approval gate and an audit log, so you can see what it decided and why.

Yes. We build retrieval over your own documents and records so the model answers from your material rather than from the open web, with access scoped to the same permissions your staff already have. Sensitive fields can be redacted before they ever reach a model, and every request and response is logged. You keep the data where it lives — we govern how the model is allowed to reach it.

Most of our automations pay back inside one to three months, because we start with the highest-volume, lowest-risk workflow rather than the most impressive one. The two-week diagnostic ranks candidate workflows by hours saved, error rate and revenue touched, and we build the top one first. If nothing in the list clears the payback bar, we tell you that instead of building it.

The diagnostic is a fixed fee. The build is scoped after modelling and priced per two-week increment, so you see the full number before work starts and can stop at the end of any increment. Once live, monitoring, retraining and support run month-to-month after a three-month setup period, cancellable at any month end.

As a partner, not a replacement. Our senior-only pod builds the model, retrieval and approval logic; your engineers own the integration points, because they know your systems best. Work runs in shared two-week increments with a live dashboard. At handover you get documented code, evaluation sets and runbooks, so your team can run and extend the system without us.

Three things. An hour with the people who do the workflow today, so we can map and time it. Read access to the CRM, helpdesk or document store the automation draws on, scoped to the project. And one decision owner to approve the ranked list after the diagnostic and sign off the approval rules. We provide everything else, including infrastructure.

Three risks: a model acting on wrong information, a model exposing data it should not see, and a team losing sight of what the system does. Confidence thresholds and a human queue handle the first. Permission-scoped retrieval and redaction handle the second. An audit log and live dashboard handle the third, so the system is never a black box.