Customer Support workflows

On a low Zendesk CSAT rating, routes the recovery by account value: high-value accounts get a CSM Slack alert and a calendar call.

Daily, it joins help-center search queries that returned zero results with tickets that had no KB match, drafts an article when docs can fix it.

When a Zendesk tag's ticket volume spikes above its baseline, an agent investigates the cluster, decides whether it's a known issue or a documentation gap.

When an article is flagged as decaying, an agent reads the recent tickets it failed to deflect, drafts an updated version with OpenAI.

Watches incoming Zendesk tickets, scores their sentiment, and when one account crosses a frustration threshold within a rolling window it opens a HubSpot task and pings…

Scans Zendesk macros against linked Confluence articles weekly and opens a deduplicated Linear issue for each macro that contradicts current docs.

When a Zendesk ticket is solved, checks whether any Confluence article could have deflected it; if not, files a Linear doc-gap task so the missing self-serve content gets written.

Looks up the sender's account tier in HubSpot, drafts a tone-matched Front reply from similar resolved threads.

On a schedule, compile the week's Sentry issues resolved in deploys, link each to the support tickets it affected.

Each month, correlates the prior month's Confluence KB edits with closed Front conversations to estimate how many would now be self-served.

When an NPS survey returns a detractor score (0-6), it logs the account in HubSpot, drafts an executive-signed apology.

Scans recent Zendesk tickets weekly, clusters questions that agents could not answer from existing docs.

On each low CSAT rating in Zendesk, an LLM diagnoses whether a missing or wrong help article caused the bad experience and, when it did, opens an Asana task to fix the specific…

When a complex Intercom how-to conversation is escalated, this records a Loom recap of the customer's issue and prior steps, files a linked Linear issue.

When a survey response scores 1-3 (a detractor), this drafts a personalized apology and make-good email.

When a VIP submits a low Intercom CSAT rating, drafts a recovery note with an LLM and books a callback on the account owner's Google Calendar within their working hours.

Detects high-urgency voicemails from a transcript, opens a prioritized Zendesk ticket with the summary.

Each week compares the AI-suggested Front drafts against what agents actually sent, scores edit distance and tone drift.

On a weekly schedule, reads all bug-ticket repro scores logged to Postgres, computes completeness trends and the most common missing fields.

Groups recent Intercom conversations by recurring intent, and for the most common topics with no canned reply.

On a schedule, fetches every localized Zendesk macro, round-trips each against its source variant.

When a ticket is solved without any macro applied, checks whether its answer matches an existing macro and, if not, logs it to Postgres as an uncovered answer so recurring gaps…

On a schedule, ranks Zendesk ticket tags by solved volume, and for any tag crossing a threshold without a matching macro.

On a schedule, reads unanswered gap questions, searches Confluence for the relevant policy or help article, and drafts a ready-to-review canned reply snippet for each.

When a new Zendesk ticket arrives, it matches the ticket against your library of known root-cause clusters and tags plus routes it in real time.

An agent reviews resolved Gmail support threads, decides which freehand answers deserve a help-center article, drafts them, and saves the docs to a Google Drive folder…

Weekly, matches recent repeat tickets against existing Notion help articles to find docs that exist but are not preventing tickets.

Records every Zoom support call's sentiment-dip findings as structured rows in an Airtable coaching tracker.

An agent-driven workflow that hourly reviews at-risk Front conversations, reasons about content, account context, and teammate skills from a Postgres profile store.

An agent reads each incoming Outlook support email, classifies intent and urgency, sets the matching SLA tier, drafts a first-response acknowledgement for agent review.

On a daily schedule, pulls the previous day's Front conversations, detects each one's language, writes the counts to Postgres.

When an agent sends a reply in Front, this compares the sent text against the suggested macro draft and your written policy.

On a weekly schedule, this clusters Front conversations that had no good macro match, drafts proposed new macros for the top recurring themes.

When an agent sends a reply built from a suggested macro, this measures how far the sent text diverged from the suggestion, logs the edit and reason to Airtable.

When an Intercom resolution bot fails to deflect a conversation and it escalates to a human, this captures the unanswered question, drafts a Notion KB article on the spot.

Daily, compares today's resolved-ticket clusters against a rolling baseline, and when a root cause spikes it opens or updates a single Linear issue for engineering…

On each new Intercom conversation, checks whether the customer's question is covered by an existing help-center article and, if not, alerts the team and logs the uncovered…

Mines tickets that received a negative satisfaction rating and had no article to point.

On a weekly schedule, diffs updated Notion help docs against open Intercom conversations.

On a weekly schedule, clusters the past week's Front conversations by topic, flags clusters that have no matching knowledge base article.

On each new Zendesk ticket, checks whether any help center article covers the question; if not, logs the gap to Linear and pings the docs channel in Slack so coverage grows over…

On each ticket solved, checks whether the agent's reply closely matches answers given on other recent tickets and, if so, DMs the agent suggesting they save it as a macro.

When a translated reply is sent in Front, it back-translates the sent message, measures meaning loss against the agent's original.

Drafts a tone-matched Front reply from similar resolved threads, then branches on the model's confidence: high-confidence drafts post silently.

A Paperclip agent reads each Intercom conversation, tags it, suggests a macro for routine issues.

When an account logs its third negative Intercom conversation inside 30 days, an agent confirms the pattern, posts a war-room alert to Slack.

Each quarter, classify all Zendesk tickets into product-feedback themes and load the labeled.

Weekly, it scans closed Zendesk tickets that never linked to a help-center article, clusters them by theme, drafts a new article for each gap.

Watches solved tickets as they close, and when an agent's free-text reply closely matches a recurring answer that has no macro.

Nightly, computes what share of agent replies were covered by a saved macro versus typed from scratch, broken down by topic and team.

When a Confluence help-center article is published or updated, immediately checks which Zendesk macros cite it and flags any whose canned answer now contradicts the new version.

Each night, compares every Zendesk macro against its linked Confluence help-center article and flags macros whose canned text now contradicts the current published doc.

Finds Zendesk macros that aren't linked to any help-center article, uses the LLM to match each to the most relevant Confluence doc.

Watches incoming Zendesk tickets for a sudden spike in a single topic with no existing article.

Receives refund requests from your customer portal via webhook, checks Stripe charge age, usage, and refund history.

When an Intercom conversation closes, detects whether the resolved question is already covered in Confluence and, if not, drafts a new Confluence page from the agent's reply…

Monthly, measures what share of Front replies were hand-typed versus macro-sent, identifies the biggest uncovered topics with an LLM.

Classifies inbound Front conversations by topic, and when several outage-related tickets cluster in a short window it raises a PagerDuty incident and broadcasts a heads-up…

Daily, extract feature requests from Intercom conversations, dedupe them into canonical themes.

Transcribes inbound support voicemails, scores each one for urgency and intent, and writes a prioritized callback task into Airtable with a one-line summary and suggested talking…