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Operational automation

Run operational automation with control.

Create operational playbooks with AI where useful, human approval where required, and full traceability by default.

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Introductory demo

Two real cases: one pauses for human approval, the other continues automatically inside the playbook boundaries.

TameAutomation

Human-readable procedures, executed by AI, governed by the team.

A TameAutomation playbook is not a chain of technical micro-nodes. It is a versioned business procedure with triggers, decisions, approvals, actions, and run history.

Email → AI Step → Approval → Response

Customer request triage

Classifies urgency, drafts a reply, creates a ticket, and queues approval when needed.

Schedule → Check → Decision → Action

Backoffice exception handling

Checks missing data, requests review, and sends alerts only when the case exceeds policy.

Trigger → Analysis → Summary → Notify

Operational reporting

Reads events, summarizes anomalies, and sends a traceable report to the operations team.

Operational AI notes

Follow how we build controllable AI playbooks.

Module decisions, demo updates, and practical examples for running AI workflows with operational control. No generic AI news.

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Not another builder

Why controlled AI needs an operational layer.

AI workflows are useful only when teams can understand, approve, resume, and audit what happened in every run.

Built for real processes

Email triage, support requests, backoffice checks, approvals, and handoffs.

Control before execution

Published versions, permissions, human approvals, and clear runtime boundaries.

Trace every decision

Inputs, policy match, branch taken, output proposed, and who approved it.

Enterprise path

Provider-agnostic by design

TameAutomation is built to run controlled AI workflows across different model providers. Teams can start with managed AI providers and move toward dedicated deployments, private models, or customer-controlled infrastructure when privacy requirements demand it.

01 / Runtime activity

Watch real executions move through the playbook.

Every run shows trigger, current node, duration, retries, and operational status without opening technical logs.

1,284 runs this month
Run history Live production

Customer email received

Trigger complete

Issue classified as billing dispute

AI Step complete

Approval required: refund over policy

Approval waiting

Ticket created in support queue

Action queued

02 / Decision trace

Every AI decision explains why it took that branch.

Inputs used, policy applied, confidence, and proposed output remain visible before high-impact actions execute.

Confidence 91%

Input used

Email body, customer tier, order value, last 3 tickets

Policy applied

Refunds above €500 require approval

AI decision

Classify as high-impact billing issue
91% confidence · policy match: refund approval

03 / Approval timeline

Risky actions pause at the right point.

Approvals, escalations, and resumes are part of the runtime, with reasoning, source data, and the identity of who approved.

Paused for Marco

AI prepared action

Draft refund reply and ticket update

Approval requested

Marco · Operations lead

Ticket creation paused

Waiting on human decision

Resume playbook

Create ticket and send response

04 / Audit log

Every run stays explainable weeks later.

Run history and step logs reconstruct the event, version, environment, inputs, outputs, and approvals.

Node-by-node audit
Run#1284production
Versionv12published
ActorAI + Marcoapproved
ApprovalMarcoconfirmed
OutputTicket + replysent
Trace6 step logscomplete

Early use cases

Start where operational pain is strong enough to deserve control.

View all use cases

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Have a repetitive operational process in mind?

See how TameAutomation turns an operational process into an AI-assisted playbook with approvals, run history and controlled execution.

Best for teams handling repetitive email, support, backoffice or operations workflows.

Book a demo See how it works