Analyze Customer Survey Feedback
Daily workflow that groups survey responses by sentiment, uses AI to extract themes and recommendations.
Afternoon (est.) AI workflow
This directory currently includes 27 recipes for customer support, across 15 workflow patterns. It shows the range of work covered today, while making it easy to see where your own process needs a more specific build. Customer service work is full of repeatable decisions: identify the issue, find the right context, route the request, and keep the customer informed. The recipes in this collection show practical ways to put an agent in that loop without pretending every support conversation can be fully automated. You will find workflows for sorting incoming tickets, spotting recurring feedback, preparing responses, and connecting helpdesk activity to the systems the rest of the team uses. The strongest examples keep a human involved where judgment, policy, or a sensitive customer situation matters. They are useful starting points for teams that want a calmer queue and clearer handoffs, whether the trigger is a new ticket, a scheduled review, or a request from an agent.
Daily workflow that groups survey responses by sentiment, uses AI to extract themes and recommendations.
Daily automation that identifies today's pending e-commerce returns and sends personalized WhatsApp messages or voice calls to customers...
New Zenes tickets auto-sort instantly into Sales, Engineering, or Customer Support buckets.
On closed Intercom tickets, an agent scores clarity, tone, and resolution via GPT and logs results to Google Sheets.
New support ticket? The agent labels it, simplifies the description, finds similar resolved issues, and suggests a fix.
Weekly scan of customer tech stacks flags high churn risk when competitors like HubSpot are detected within 90 days of renewal.
Monitors customer health by analyzing support sentiment and usage trends to alert teams of churn risks with actionable next steps.
An AI assistant on WhatsApp that understands text, voice notes, images, and PDFs, remembering your last 10 messages to reply contextually.
Receives customer feedback via webhook, analyzes sentiment with AI, generates a branded PDF report, emails it to the user.
New Zendesk ticket? It finds the customer's recent WooCommerce order, adds details as a private note with status tags.
When a GoHighLevel deal completes, it automatically sends an NPS survey via Gmail, categorizes the response, saves it to Notion.
AI scans customer emails to auto-populate order numbers, account IDs, invoices, and claims without manual entry.
Transcribes sales calls with AssemblyAI and uses OpenAI to extract client intent, interest scores, and upsell opportunities into Supabase.
New Typeform onboarding request? The agent logs it to Sheets, validates the email, generates a branded HTML welcome message via AI.
Ingests PDF knowledge bases via Jotform and answers user chat questions using semantic search and Google Gemini.
Validates reported payment issues against transaction records using AI to prevent false escalations before creating tickets and notifying...
Fetches customer reviews from a sheet, classifies sentiment with GPT-4o-mini, and emails a doughnut chart summary to the team.
On new customer signup, the agent schedules a welcome meeting, generates a personalized HTML email via Gmail.
When a support ticket closes, the agent drafts a personalized feedback email with survey links and sends it via Gmail.
When a bug is reported via Marker.io, it automatically creates an Intercom conversation with the reporter's details and attaches all...
When an email hits your support inbox, it instantly creates a Trello ticket, emails the customer a confirmation.
Receives call transcriptions via webhook, summarizes them with AI, syncs to HubSpot, and alerts teams via Slack, WhatsApp, or email.
Fetches Zendesk tickets to build an interactive knowledge graph that visualizes topics, sentiment trends, and product gaps via AI.
New HubSpot support tickets are analyzed for sentiment and intent.
New Intercom message? The agent classifies it by category and urgency.
Captures Jotform feedback, uses AI to analyze sentiment, sends thank-you emails for positive reviews.
New Zendesk refund ticket? It checks the WooCommerce order, alerts Slack for damaged items, or emails customers for proof on other cases.
Nothing matches those filters
An AI agent can classify and route requests, summarize ticket context, draft replies, flag urgent issues, and turn feedback into follow-up work. The right boundary is important: use it to prepare and organize work, then keep people in control of sensitive decisions.
It can answer well-defined, low-risk questions when the source material is reliable. For refunds, account access, complaints, or unusual cases, a review step or clear escalation path is usually the safer design.
Start with a repetitive queue task that already has clear rules, such as tagging tickets, summarizing conversations, or sending an alert when a certain issue appears. That creates a useful result without asking the agent to own the whole customer relationship.