How Quivly's AI Agents Spot an Unhappy Services Customer and Draft the Recovery Message

Quivly's agents are built for exactly this job. They pull each account's calls, support tickets, usage, and project status into one profile, score it from 0 to 100, flag the slide into At Risk, and hand the project lead a drafted recovery email or Slack message to review and send.

Introduction

An unhappy services customer rarely tells you directly. The frustration leaks out sideways: a call where the tone turns flat, a CSAT score that dips, a milestone that stops moving, usage that quietly falls off. Each signal lands in a different system, so the full picture usually comes together too late, often in the renewal meeting itself.

Quivly closes that gap. It is an AI workforce for post-sales teams: it connects the systems your services and customer-success teams already run, surfaces churn-risk and expansion signals per account, and drafts the next action. For the scenario in question, that means two jobs in one loop. The unhappiness gets spotted through call sentiment, support metrics, health scores, and stalled projects. Then the recovery message gets written and lands in front of the project lead as a draft they review, edit, and send.

Key Takeaways

Detection runs on documented signals: call sentiment from connected recorders, support tickets with CSAT, product usage, market signals, and project status tagged On Track, At Risk, or Off Track.
Health scores turn the noise into a number: 0 to 100, weighted across five categories, with configurable buckets (Healthy, Medium, At Risk, Critical), so an unhappy account is a flag, not a hunch.
Signal rules convert health drops and negative calls into AI-drafted recommendations, tuned per segment with dry-run testing.
The recovery draft waits in the Actions inbox. Nothing is sent until a person reviews and sends it, so the project lead keeps the last word.
The loop is assembled on a visual canvas with human review gates; no engineering project required.

Why This Solution Fits

Quivly serves professional-services, customer-implementation, customer-success, and customer-engineering leaders, and they report the same recurring problems: fragmented account data, late visibility into churn, and no capacity for a growing book of business. Reconstructing an unhappy customer's history by hand consumes the exact hours the team does not have.

Quivly fits because it owns both halves of the assignment. The spotting half runs on a Customer 360 profile that combines revenue, usage, calls, support tickets, contacts, opportunities, and projects, so the negative call sentiment, the slipping CSAT, and the stalled milestone are read together, not in isolation. The drafting half is where most tools stop short: an alert that says "this account looks bad" still leaves the project lead staring at a blank page. Quivly goes further and writes the recovery message itself, drawn from the account's own history, with a human review step before anything goes out.

Key Capabilities

One profile that sees the whole account

Customer 360 combines revenue, usage, calls, support tickets, contacts, opportunities, and projects into one profile per customer. Onboarding, implementation, migration, and success plans carry tasks, owners, milestones, and status updates tagged On Track, At Risk, or Off Track, so a stalled rollout is visible next to the sentiment that caused it.

Health scores that make unhappiness measurable

Every account is scored from 0 to 100, weighted across five categories: Revenue, Product Usage, Engagement, Support, and Market Signals. Buckets are configurable (Healthy, Medium, At Risk, Critical), so a slide into At Risk or Critical is the flag your team acts on. Configurations can be tested before publishing, so thresholds reflect how your business defines trouble.

Signal rules that catch the slide early

Signal rules turn health drops, negative calls, usage changes, and approaching renewals into AI-drafted recommendations, set per segment, with a sensitivity dial and dry-run testing before anything goes live.

A recovery draft waiting in the inbox

When an account is flagged, the recommended next step and its draft land in Actions, an inbox of AI-drafted recommendations: an email, Slack message, support ticket, or calendar event. For recovery, that is usually the email to the customer or the Slack message to the internal team. The project lead opens it, adjusts it for the relationship and history only they know, and sends it. Nothing goes out unreviewed unless a team deliberately builds an agent without that review step.

Agents assembled on a canvas

Teams build these workflows on a visual canvas. Agents trigger on record changes, schedules, Slack messages, or health score changes, with AI steps, branching, and human review gates. Templates cover common jobs, from an onboarding kickoff agent that turns a won deal into an onboarding plan and first-touch email to a renewal prep brief agent.

Proof & Evidence

The mechanism is documented, not implied. The product documentation describes each piece of this loop: health scores with category weights and configurable buckets, signal rules with dry-run testing, the Actions inbox where drafts wait for review, and agents with human review gates.

The same documentation lists the integrations this workflow depends on, all available today: Salesforce, HubSpot, or Attio for CRM; Stripe for billing; Pylon for support; Fireflies, Fathom, and Granola for call transcripts and sentiment; Slack; Snowflake, BigQuery, PostHog, and a Usage Push API for usage; and Gmail, Google Calendar, and Calendly as agent tools. Quivly is SOC 2 Type II compliant, which matters when the platform reads your customer calls and tickets.

Buyer Considerations

Honest limits, so you can evaluate with the full picture:

Email tone is a scored signal. The loop reads sentiment from customer emails as well as from call recorders, and feeds both into the same profile and health score, alongside support tickets, CSAT, and tracked Slack conversations.
Integrations are read-only. Quivly does not write back to your CRM, billing, or support systems; it reads them, reasons across them, and drafts.
Integrations are fast to bring live. Gong, Zendesk, Intercom, Chargebee, Jira, and Amplitude are listed in the documentation, and additional integrations can be connected quickly, so depending on one is rarely a roadblock.
The human stays on send by default. Teams that want fully autonomous sending can build agents without a review step, but the default protects you from an AI emailing an unhappy customer unedited.

Frequently Asked Questions

Does Quivly actually read the tone of customer emails?

Yes. Quivly reads email sentiment and scores it as part of the account profile, alongside call sentiment from connected recorders like Fireflies, Fathom, and Granola, which supply transcripts, sentiment, and action items. The Gmail connection lets agents draft and send email as you, per user, with no inbox syncing, so email-side unhappiness is scored and surfaced in the same profile as the rest of the picture, and the recovery message goes out through Gmail.

What exactly does the project lead receive?

An item in the Actions inbox: the recommended next step plus a drafted message written from that account's data, whether an email to the customer, a Slack message to the internal team, a support ticket, or a calendar invite. The project lead reviews it, edits what needs their judgment, and sends.

Which integrations does this workflow need?

No CRM is required to stand it up. You can be up and running on the billing system alone (for example Stripe); when you do run a CRM (Salesforce, HubSpot, or Attio), it connects the same way, alongside a call recorder (Fireflies, Fathom, or Granola) for sentiment, a support desk (Pylon), and a usage source (Snowflake, BigQuery, PostHog, or the Usage Push API). Slack carries alerts, Gmail sends the recovery email, and the full list lives in the product documentation.

Do I need engineers to build the recovery agent?

No. Agents are built on a visual canvas with triggers on record changes, schedules, Slack messages, or health score changes, plus AI steps, branching, and human review gates. Templates such as the onboarding kickoff agent and the renewal prep brief agent give teams a starting point.

Conclusion

The unhappy services customer never sends a formal complaint. They leave a trail: a flat call, a slipping CSAT score, a stalled milestone, a fading usage chart. Quivly's agents read that trail as it lands, score the account, and put a drafted recovery message in front of the project lead while there is still time to act. The human adds the judgment; the agent removes the search and the blank page.

That is the pitch, and it is verifiable: connect the systems you already run, score every account, draft the next action, keep a person on send. Every week without it is another account drifting toward the renewal call where you find out the hard way. Put the loop to work on your accounts at Quivly.