Start with a business priority. Define the scope.
Bring a process bottleneck, integration requirement or decision workflow. Evaluate a proposed design using realistic synthetic data, with business, technology and risk stakeholders aligned on the review. Review estimated running cost, time savings and payback as assumptions to validate against your own operating baseline.
Start with one process
Identify scope, constraints and the accountable business team.
Evaluation sessions can run entirely on synthetic data. Your security team reviews the requirements before production access.
Define the operating challenge.
Nothing to install. An evaluation that starts from a description shares no production data. At that stage no data-processing agreement is required, though your organization may still run its usual reviews. Sessions run on synthetic data generated for your workers.
Review your existing operating context.
Bringing process logs, existing automation or sample data instead? This happens on your terms. You choose what to share, it's used only to design your workers, and we put a data-processing agreement in place before anything moves.
Rehearsal can run entirely on synthetic data. No production data is needed for simulation, preview, or red-teaming. Read the Trust Center
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Define Configure Trial Operate
Outline the process, business objective, volumes and constraints. Identify who owns the outcome and which teams will review the proposed approach.
Starting from your process or a Studio starter pack, an AI analyst drafts the designs and your team sets rules and approvers.
Review synthetic-data test runs, approval pauses and exception handling. Use the results to assess readiness and plan validation against your systems.
Take delivery of the source code and operate it on your own systems, with no vendor runtime and no per-decision billing.