Email → AI Step → Approval → Response
Operational AI runtime
Operational control layer for AI workflows.
TamePulse turns real business processes into AI playbooks with bounded autonomy, human approvals, published versions, and node-by-node audit.
Introductory demo
Two real cases: one pauses for human approval, the other continues automatically inside the playbook boundaries.
Gmail
Slack
Telegram
HubSpot
Sheets
Zendesk
Operational playbooks
Human-readable procedures, executed by AI, governed by the team.
A TamePulse playbook is not a chain of technical micro-nodes. It is a versioned business procedure with triggers, decisions, approvals, actions, and run history.
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.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
TamePulse 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 completeIssue classified as billing dispute
AI Step completeApproval required: refund over policy
Approval waitingTicket created in support queue
Action queued02 / 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 ticketsPolicy applied
Refunds above €500 require approvalAI 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 MarcoAI prepared action
Draft refund reply and ticket updateApproval requested
Marco · Operations leadTicket creation paused
Waiting on human decisionResume playbook
Create ticket and send response04 / 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 auditRun#1284production
Versionv12published
ActorAI + Marcoapproved
ApprovalMarcoconfirmed
OutputTicket + replysent
Trace6 step logscomplete
Early use cases
Start where operational pain is strong enough to deserve control.
B2B customer support
Email triage, urgency, drafted replies, internal tickets, and approvals.
Backoffice and internal requests
Classification, data checks, alerts, tasks, and decision audit.
E-commerce and agencies
Repeated requests across email, CRM, spreadsheets, Telegram, or Slack.
Operational reporting
Schedules, anomaly checks, AI summaries, and team notifications.
Private demo
Have a repetitive operational process in mind?
See how TamePulse 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.