AI automation, built for the work you actually do

RPA records and replays clicks on a fixed screen interface. AI automation understands context, handles variation in inputs, and applies reasoning to decide what action to take. RPA breaks when a button moves or a field label changes. AI automation adapts because it reads the document or message the way a human would, not according to a rigid template. We use both in the right places, often in the same workflow.

Most first automations go live in 30 days from signed scope. Discovery takes two days. Design and approval takes one week. Build and testing takes two to three weeks, running against a copy of your data. You review the output before the automation touches your live systems. 30 days is the target; complex multi-system integrations sometimes run to six weeks.

Yes. We connect to your existing stack via APIs, webhooks, and file-based integrations depending on what each system supports. We have worked with Google Workspace, Microsoft 365, Salesforce, SAP, various SQL databases, and dozens of industry-specific platforms. We have never asked a client to switch platforms to make an automation work. The automation connects to what you already use.

We measure three things: time saved per task instance, error rate reduction compared to the manual baseline, and throughput increase at the same headcount. We measure the baseline before we start, not after, using a two-week time-and-motion study on actual instances. We report weekly during the first month so you see the lift before the next billing cycle, not after a quarterly review.

In every engagement we have run, automated staff have moved to higher-value work rather than out the door. The reason is consistent: the constraint was never the number of people, it was their capacity. When routine volume is handled by automation, the same team can take on more complex work, more clients, or better quality review. The constraint shifts from capacity to quality, which is a better problem to have.

No. Your data is used only to run your automations and produce the outputs you have authorized. We do not use client operational data to train any shared model, fine-tune any shared system, or improve automations for other clients. Data access is restricted to the specific systems and fields required for each automation, and that access is documented in the engagement scope.