---
title: "Telehealth AI Customer Support: A 72% Resolution Story"
description: "A telehealth provider resolves 72% of ~2,600 monthly Gorgias support tickets with AI, holding 75% AI CSAT and saving around 142 hours every month."
url: https://myaskai.com/blog/telehealth-ai-customer-support-case-study
---
# Telehealth AI Customer Support: A 72% Resolution Story

A telehealth provider resolves around 72% of its monthly Gorgias support tickets with AI, holding a 75% AI CSAT score and saving roughly 142 hours every month.

Shipping prescription treatments to people's doors generates a very specific kind of support queue. The questions are exactly what you'd expect (where's my order, when does it ship, am I eligible, what happens next), they arrive on both chat and email, and they land in a small team that was growing faster than it could hire.

Today our AI agent handles the bulk of that queue directly inside Gorgias. It resolves around 72% of those tickets without a human, holds a 75% AI CSAT score, and hands the team back about 142 hours every month, off the back of a help center the AI largely wrote itself.

Here's how it came together.

## Telehealth AI customer service results on Gorgias at a glance

| Fact | A DTC telehealth provider |
| --- | --- |
| Industry | Healthcare, DTC telehealth and online pharmacy |
| Helpdesk | Gorgias (chat and email) |
| Support volume | ~2,600 tickets/month (~1,870 resolved by AI) |
| AI resolution rate | 72% |
| AI CSAT | 75% |
| Time saved | ~142 hours/month |
| Key features used | Order-status Task, Self-Learning, Train on Historic Tickets, Guidance, custom in-CRM Copilot |
| Knowledge sources | Help articles the AI drafted from past tickets, team-reviewed, then grown by Self-Learning |
| Go-live mode | Direct replies on chat and email from the start, no notes-only phase |

## What does the provider do?

The provider is a direct-to-consumer telehealth and online-pharmacy business. Customers get an online consultation, and prescription treatments are shipped to their door, across several consumer-health categories.

It's a Series A company, and like a lot of fast-growing healthcare brands, its support volume climbed faster than headcount could keep up with. Half the queue is ecommerce questions (where's my order, can I change my address, when's my next delivery), and the other half needs the careful, sometimes-human handling that anything health-related deserves.

## Which helpdesk does the provider use?

They run support on Gorgias, across both chat and email. Our [Gorgias AI agent](https://myaskai.com/ai-agent-integration/gorgias) plugs into that same inbox, so there was nothing to rip out and no new tool for the team to learn.

The thing that tends to matter most on a queue like this is how the AI is priced. When most of your tickets are repetitive order and FAQ questions, you resolve a lot of them, and the gap between paying per ticket and paying per resolution gets wide fast.

We charge per ticket (roughly ten cents), rather than [per resolution](https://myaskai.com/blog/per-conversation-vs-per-resolution-pricing), which keeps the economics sane when the AI is doing most of the answering.

## How did the provider train their AI customer service agent?

Here's the part I like most about this one. They didn't have a mature help center to point the AI at, so the AI built one for them.

Breakdown of how the AI built its own knowledge: past tickets, team review, Self-Learning, and 3,000+ tickets answered to date.

We used [Train on Historic Tickets](https://myaskai.com/features/train-on-historic-tickets) to auto-draft help articles from their past tickets, learning from how real questions had actually been answered. The team reviewed those drafts before any went live, so the agent started from their own answers rather than a thin set of generic articles.

From there, [Self-Learning](https://myaskai.com/features/self-learning) kept the knowledge growing. It drafts new articles by comparing the AI's reply to the human agent's actual reply on handed-over tickets, so the agent quietly widens what it can answer over time (you can read more on [how self-learning works](https://myaskai.com/blog/what-is-self-learning-ai)). To date, those auto-drafted articles have answered more than 3,000 tickets between them.

## How did the provider turn on 'direct replies' to customers?

They run the AI as a direct responder on both channels in Gorgias. A self-built help center plus clear rules on when to escalate gave them the confidence to let it reply to customers directly, rather than sitting in a notes-only mode forever.

Chat answers can be shorter and faster, email answers a little more thorough, and it's the same agent doing both.

## What was the biggest thing the provider did to improve their AI agent's resolution?

For an online pharmacy shipping physical product, one question dwarfs all the others: where's my order. So the single highest-leverage thing they built was a [Task](https://myaskai.com/features/tasks-and-tools) that checks order status automatically.

Four-step order-status Task flow: customer asks where's my order, the AI matches the Task, it checks the order system, and answers with the live status.

A Task is an agentic workflow, and here's the thing: instead of a human looking up an order and pasting the status back, the AI recognizes the question, calls the provider's order system itself, and answers with the live status.

That turns the most common ticket in the queue from a manual lookup into something the AI resolves end-to-end. Underneath it, the boring-but-effective Self-Learning keeps widening the range of things the agent can handle, and together they hold a 72% resolution rate steady.

[Play video](https://www.youtube.com/watch?v=4o7ebwPrp8w)

For context on that number, 72% is solid. Across a [large field of AI support deployments](https://myaskai.com/blog/ai-resolution-rate-benchmarks) the median resolution rate sits around 70%, and this provider is already a little above it before they've even connected live customer data (more on that in a moment).

## How does the provider customize their AI agent setup to work for their business?

A healthcare brand can't run a generic bot, so they tuned the agent around a few things in particular (the small settings that, in our experience, end up doing most of the work).

### Setting the tone and the escalation rules with Guidance

They configured two kinds of [Guidance](https://myaskai.com/features/guidance). Communication guidance sets the brand voice and sign-offs, so the AI sounds like them.

[Handover and escalation guidance](https://myaskai.com/features/chatbot-to-human-handoff) sets the rules for when the AI should stop and pass a ticket to a person, which for a healthcare brand is the part you really cannot get wrong.

### Giving agents an AI Copilot inside their CRM

Not all of their work lives in Gorgias. So our team built them a custom [Copilot](https://myaskai.com/features/copilot-internal-note-reply) inside their CRM, so the AI keeps agents efficient even on the work that happens outside the helpdesk.

### Resolving order questions end-to-end

The order-status Task we built is the other piece of ongoing tuning. As their systems and policies change, that workflow keeps the most common question in the queue resolving on its own.

## What impact is the provider's AI customer service agent having now?

The numbers from the last 30 days:

Three stats: 72% AI resolution rate, 75% AI CSAT, and around 142 hours saved every month.

- **72% AI resolution rate**
- **~2,600 tickets handled per month** (of which roughly 1,870 are resolved by the AI with no human involved)
- **~142 hours saved per month** (at roughly five minutes of agent time per resolved ticket)
- **75% AI CSAT** across the tickets the AI handled
- **3,000+ tickets answered to date** by the help articles the AI drafted for itself

The hours-saved figure is the one I always point to. 142 hours a month is the best part of a full-time agent the team never had to hire, on a queue that would otherwise keep growing with order volume.

## Where does the provider go from here?

More [Tasks and Tools](https://myaskai.com/features/tasks-and-tools) would let the agent move from answering to doing: changing a delivery address, arranging a re-ship, or updating a subscription, through their own systems with the right approvals (and the clinical guardrails kept firmly in place).

I'll be straight about what 'fully automated' looks like for a healthcare brand, because it's easy to oversell. It means the AI owns the big repetitive core (the order questions, the eligibility basics, the same FAQs arriving all day), while the team keep the clinical judgment and anything sensitive.

That's the split they've built, and it's why a 72% resolution rate comes with a 75% satisfaction score rather than at the expense of one. If you'd like more stories like this one, browse [all our case studies](https://myaskai.com/blog/tags/case-study-posts), see how [Edel Optics](https://myaskai.com/blog/my-askai-edel-optics-case-study-2026) and [YouGarden](https://myaskai.com/blog/my-askai-yougarden-case-study-2026) run the same playbook, or check [our pricing](https://myaskai.com/pricing) to model what this would cost at your own ticket volume.

- Case Study ### [How Edel Optics achieves 79% AI resolution, saving 150 hrs each month](https://myaskai.com/blog/my-askai-edel-optics-case-study-2026) Edel Optics jumped from 25% to 79% AI resolution with one change: plugging in live customer data. 92% CSAT. In German. Across multiple languages. 13 Oct 2025  6 min read
- Case Study ### [How YouGarden achieves 66% AI resolution, saving 965 hrs each month](https://myaskai.com/blog/my-askai-yougarden-case-study-2026) YouGarden saves 965 hours a month with AI — that's 6 full-time agents. They hit 82% peak resolution after one smart integration change. 16 Dec 2025  6 min read
- Case Study ### [How a digital-goods marketplace achieves 58% AI resolution, saving 1,768 hrs a month](https://myaskai.com/blog/digital-goods-marketplace-ai-customer-support-case-study) A digital-goods marketplace case study: 58% of 36,413 Intercom customer support conversations resolved by AI in 30 days, 92% AI CSAT, 1,768 hours saved. 10 Sept 2026  7 min read
- Case Study ### [How a premium accessories brand achieves 64% AI resolution, saving 153 hrs each month](https://myaskai.com/blog/my-askai-premium-accessories-case-study-2026) A premium accessories brand resolves 64% of its 2,900 monthly Gorgias tickets with AI, holds 85% AI CSAT and saves 153 hours a month across email and chat. 15 Jul 2026  10 min read
- Case Study ### [How a baby-textiles brand hit 72% AI resolution on Gorgias](https://myaskai.com/blog/baby-products-ai-customer-support-case-study) A DTC baby-textiles brand resolves 72% of ~1,000 monthly Gorgias tickets at 94% AI CSAT, saving ~59 hours a month across several European markets. 29 May 2026  7 min read
- Case Study ### [How a DTC hosiery brand runs AI copilot inside Gorgias](https://myaskai.com/blog/dtc-hosiery-ai-customer-support-case-study) A DTC comfort-hosiery brand runs My AskAI as a Gorgias copilot on ~2,400 tickets a month, with live Shopify and User Data API lookups behind each draft. 27 May 2026  11 min read

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