Answer questions and resolve problems.
10 workflows
A Tines example workflow fires on a Stripe cancellation, lets an AI agent look up the customer's support tickets and account history, and posts a structured churn brief to Slack.
An infrastructure vendor's guide sets out business uses for OpenClaw and a phased rollout: pilot a low-risk workflow, add guardrails and approvals, then scale once value and safety are shown.
A small open-source evaluation shows a naive support agent deflecting every ticket while resolving about half, and a policy-gated agent that deflects fewer but resolves what it handles.
A merged helpdesk change that, when a ticket is resolved, asks an AI whether other customers are likely to ask the same thing and writes a draft article for managers to review if none exists.
An open-source support workbench classifies tickets into three tiers, drafts replies that cite help-centre articles, reads billing data in read-only test mode, and only proposes refunds that need a named approver.
An open-source ticket deflection engine escalates risky tickets directly, answers procedural ones by rule, and uses retrieval over documentation with three confidence bands for auto-reply, human review and escalation.
A framework for choosing a first AI automation by its worst-case outcome, connecting an inbox read-only, watching the first twenty runs, and naming who verifies output before widening the agent's permissions.
A solo developer describes an hourly support agent on a spare computer that classifies emails, replies to what it can answer from an instruction file, and forwards everything else to the founder.
An AI engineer describes a retrieval-augmented chatbot deployed for one client with over 500 PDFs, with the chunking and retrieval choices, and reports 94% answer accuracy against 61% for plain GPT-4.
A vendor's write-up of a 90-day support automation test at a SaaS company: staged rollout, a confidence threshold for human review, and fixing outdated docs before the AI could resolve most tickets.