AI automation and custom integrations that connect what you already run

Usually not. Most of what pays back is a queue, a rules engine, and a model called at two or three decision points, running on the cloud account you already have.

It escalates to a person either way. Every automation and every integration job we build has an explicit threshold or retry policy, and anything below it goes into a review queue with the reason attached rather than failing silently.

By what the systems on both ends actually support, decided during scoping rather than defaulted to in advance. Where Business Central, Dataverse or Power Automate do not have a connector that fits, we build on the supported API surface each side exposes rather than force a workaround through a tool that was not meant for it — and we pick something your team can still maintain in two years.

A scheduled reconciliation job catches it. Webhook delivery is at least once, not exactly once, so nothing we build depends on a webhook arriving as its only proof anything happened.

Not unless you explicitly choose a service that does so and accept that trade off. For most clients we use deployments with no training retention, and where data cannot leave your environment at all we use self hosted models.

Four to six weeks for a single AI workflow including exception paths; two to six weeks for a custom connector depending on the systems involved. The assessment that scopes either one takes about two weeks.

An integration platform is the right answer when the connectors you need already exist, the volumes are modest, and you want someone else operating it. A built connector wins when one end has no supported connector, when the transformation logic is genuinely yours, or when per-transaction pricing stops making sense at your volume. We cost both, including who maintains each one in year two, and the answer is often a platform for the standard flows and a built connector for the one that matters.