CUSTOMER SUPPORT
Detect Hourly Sentiment Spikes Across a Front Inbox
Every hour, samples the last hour of Front messages, scores their aggregate sentiment, and fires a Slack alert when the share of negative threads jumps above its rolling baseline.
How it runs
The automated pipeline, trigger to output.
- TriggerHourly schedule
- ActionFetch last hour of Front conversationsFront
- ActionScore sentiment and extract keywords with OpenAIOpenAI
- LogicCompare negativity rate to Airtable baselineAirtable
- OutputAlert Slack on spike and update baselineSlack
What it does
On an hourly schedule it pulls the messages received in the trailing hour from a Front inbox, scores each with OpenAI, and computes the percentage that are negative. It compares that against a rolling baseline stored in Airtable. When negativity spikes well above normal, it posts a Slack alert naming the suspected cause keywords so the team knows an incident or bad release is generating anger in real time.
When to use it
Use it when a single furious thread is normal but a sudden cluster of them signals a real problem — an outage, a botched deploy, a billing glitch. This watches the trend, not the individual message, so leads get one heads-up instead of fifty pings.
How it works
- 1An hourly schedule triggers the run.
- 2Front returns all conversations with activity in the last hour.
- 3OpenAI scores each message and extracts recurring complaint keywords.
- 4The flow reads the rolling negativity baseline from Airtable and compares the current hour against it.
- 5If the spike exceeds the threshold, Slack receives an alert with the negativity rate and top keywords; the new reading is written back to Airtable to update the baseline.
Set it up
What you configure once, before turning it on.
- 1Connect FrontShared inbox, conversations.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect AirtableBases, tables, views, automations.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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