Practical AI automation without losing control

Connecting agents to real workflows, systems, and human approval paths. Older note, kept because it still describes the control question.

If the agent cannot see the system of record, or it can write more than a person would sign, you do not have an automation. You have a demo with a longer name.

We still start from the workflow, not from a model. Lead qualification, a support queue with an escalation path, and a document pile that already has a rule are common first processes. Isolated chat experiments are not.

Approval before production

Design the hand-off before anyone asks what happens when it is wrong. Name who can stop a write. Log what was read, what was proposed, and who signed. Adding that after the first incident is how a pilot earns a reputation it then cannot shake.

Integration is the constraint

An agent that cannot use the client’s own credentials, object permissions, and field-level security will either fail in production or succeed by borrowing a person’s login. Neither is acceptable. Integration depth is the work. The model call is the smaller part.

Where this sits now

The package we sell on the homepage is one supervised agent on one existing workflow, inside the systems the client already runs, at $9,900 to go live and $2,900 a month to run. A person still signs anything that can change money or a customer. That is a different conversation from a Salesforce programme or a dedicated engineering team.

If you have a support, sales, or document workflow that is still manual, book the free 30-minute assessment. Platform work and employed teams are described under systems and teams.