Customer Support·2–6 weeks·3–4 weeks

Voice-of-Customer Analysis

Nobody on the team reads 50,000 tickets per quarter. This does. Clusters into themes, ranks by volume + sentiment, flags emerging issues before they hit the changelog. Output: a one-page weekly memo for product + a Slack alert when a new theme breaks 1% of volume.

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Time to detect emerging issue

Days, not weeks

Product team unblocked

Weekly digest

Tickets analysed

100%

The problem

What this fixes.

—
Product team doesn't see what support sees
—
Bug clusters surface 6 weeks late
—
Roadmap decisions are loud-voice driven

How it works

Three jobs, on rails.

Cluster

Themes

Topic modelling across tickets / calls / reviews / NPS.

Rank

Volume + sentiment

Top issues weighted by hit rate and CSAT impact.

Alert

Emerging issues

Slack ping when a new cluster crosses 1% of weekly volume.

A real moment

One scenario, one outcome.

The scenario

Across Monday–Thursday, 87 customers mention 'mobile login loop' across tickets, App Store reviews and a Reddit thread.

The outcome

Friday memo surfaces it as cluster #1. Slack alerts trip at threshold mid-week. The bug ships to test on Monday — usually a 4-week lag, now 4 days.

Engagement

Scoped on a call.

Delivery

3–4 weeks

Engagement model

Pilot → retainer

Scope confirmed in a 30-minute call. Fixed scope, fixed timeline before you sign. We'll send a one-page proposal within 48 hours.

Book a call →

Same category.

Customer Support

Support Tier-1 Bot

A grounded chatbot that closes 40–70% of tickets and escalates the rest with full context.

1–2 weeks

Customer Support

Ticket Classifier & Router

Reads inbound tickets, sets priority, tags, sentiment — and routes to the right queue without a customer menu.

1 week

Customer Support

Agent Copilot

Suggests responses, pulls KB articles and drafts replies in real time inside your helpdesk.

1–2 weeks

Want this in your workflow?

Talk to us about your version.

Start a project

← All automations

Customer Support·2–6 weeks·3–4 weeks

Voice-of-Customer Analysis

Book a call Browse 50 automations

Time to detect emerging issue

Days, not weeks

Product team unblocked

Weekly digest

Tickets analysed

100%

The problem

What this fixes.

—
Product team doesn't see what support sees
—
Bug clusters surface 6 weeks late
—
Roadmap decisions are loud-voice driven

How it works

Three jobs, on rails.

Cluster

Themes

Topic modelling across tickets / calls / reviews / NPS.

Rank

Volume + sentiment

Top issues weighted by hit rate and CSAT impact.

Alert

Emerging issues

Slack ping when a new cluster crosses 1% of weekly volume.

A real moment

One scenario, one outcome.

The scenario

Across Monday–Thursday, 87 customers mention 'mobile login loop' across tickets, App Store reviews and a Reddit thread.

The outcome

Friday memo surfaces it as cluster #1. Slack alerts trip at threshold mid-week. The bug ships to test on Monday — usually a 4-week lag, now 4 days.

Engagement

Scoped on a call.

Delivery

3–4 weeks

Engagement model

Pilot → retainer

Scope confirmed in a 30-minute call. Fixed scope, fixed timeline before you sign. We'll send a one-page proposal within 48 hours.

Book a call →

Same category.

Customer Support

Support Tier-1 Bot

A grounded chatbot that closes 40–70% of tickets and escalates the rest with full context.

1–2 weeks

Customer Support

Ticket Classifier & Router

Reads inbound tickets, sets priority, tags, sentiment — and routes to the right queue without a customer menu.

1 week

Customer Support

Agent Copilot

Suggests responses, pulls KB articles and drafts replies in real time inside your helpdesk.

1–2 weeks

Want this in your workflow?

Talk to us about your version.

Start a project