At Gorgias we recently switched our flask & celery apps from Google Cloud VMs provisioned with Fabric to using docker with kubernetes (k8s). This is a post about our experience doing this.

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Note: I'm assuming that you're somewhat familiar with Docker.

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Docker structure

The killer feature of Docker for us is that it allows us to make layered binary images of our app. What this means is that you can start with a minimal base image, then make a python image on top of that, then an app image on top of the python one, etc..

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Here's the hierarchy of our docker images:

• gorgias/base - we're using phusion/baseimage as a starting base image.
• gorgias/pgbouncer
• gorgias/rabbitmq
• gorgias/nginx - extends gorgias/base and installs NGINX
• gorgias/python - Installs pip, python3.5 - yes, using it in production.
• gorgias/app - This installs all the system dependencies: libpq, libxml, etc.. and then does pip install -r requirements.txt
• gorgias/web - this sets up uWSGI and runs our flask app
• gorgias/worker - Celery worker

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Piece of advice: If you used to run your app using supervisord before I would advise to avoid the temptation to do the same with docker, just let your container crash and let k8s handle it.

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Now we can run the above images using: docker-compose, docker-swarm, k8s, Mesos, etc...

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We chose Kubernetes too

There is an excellent post about the differences between container deployments which also settles for k8s.

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I'll also just assume that you already did your homework and you plan to use k8s. But just to put more data out there:

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Main reason: We are using Google Cloud already and it provides a ready to use Kubernetes cluster on their cloud.

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This is huge as we don't have to manage the k8s cluster and can focus on deploying our apps to production instead.

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Let's begin by making a list of what we need to run our app in production:

• Database (Postgres)
• Message queue (RabbitMQ)
• App servers (uWSGI running Flask)
• Web servers (NGINX proxies uWSGI and serves static files)
• Workers (celery)

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Why Kubernetes again?

We ran the above in a normal VM environment, why would we need k8s? To understand this, let's dig a bit into what k8s offers:

• A pod is a group of containers (docker, rtk, lxc...) that runs on a Node. It's a group because sometimes you want to run a few containers next to each other. For example we are running uWSGI and NGINX on the same pod (on the same VM and they share the same ip, ports, etc..).
• A Node is a machine (VM or metal) that runs a k8s daemon (minion) that runs the Pods.
• The nodes are managed by the k8s master (which in our case is managed by the container engine from Google).
• Replication Controller or for short rc tells k8s how many pods of a certain type to run. Note that you don't tell k8s where to run them, it's master's job to schedule them. They are also used to do rolling updates, and autoscaling. Pure awesome.
• Services take the exposed ports of your Pods and publishes them (usually to the Public). Now what's cool about a service that it can load-balance the connections to your pods, so you don't need to manage your HAProxy or NGINX. It uses labels to figure out what pods to include in it's pool.
• Labels: The CSS selectors of k8s - use them everywhere!
• ‍

There are more concepts like volumes, claims, secrets, but let's not worry about them for now.

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Postgres

We're using Postgres as our main storage and we are not running it using Kubernetes.

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Now we are running postgres in k8s (1 hot standby + pghoard), you can ignore the rest of this paragaph.

The reason here is that we wanted to run Postgres using provisioned SSD + high memory instances. We could have created a cluster just for postgres with these types of machines, but it seemed like an overkill.

The philosophy of k8s is that you should design your cluster with the thought that pods/nodes of your cluster are just gonna die randomly. I haven't figured our how to setup Postgres with this constraint in mind. So we're just running it replicated with a hot-standby and doing backups with wall-e for now. If you want to try it with k8s there is a guide here. And make sure you tell us about it.

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RabbitMQ

RabbitMQ (used as message broker for Celery) is running on k8s as it's easier (than Postgres) to make a cluster. Not gonna dive into the details. It's using a replication controller to run 3 pods containing rabbitmq instances. This guide helped: https://www.rabbitmq.com/clustering.html

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uWSGI & NGINX

As I mentioned before, we're using a replication controller to run 3 pods, each containing uWSGI & NGINX containers duo: gorgias/web & gorgias/nginx. Here's our replication controller web-rc.yaml config:

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apiVersion: v1
kind: ReplicationController
metadata:
 name: web
spec:
 replicas: 3 # how many copies of the template below we need to run
 selector:
   app: web
 template:
   metadata:
     labels:
       app: web
   spec:
     containers:
     - name: web
       image: gcr.io/your-project/web:latest # the image that you pushed to Google Container Registry using gcloud docker push
       ports: # these are the exposed ports of your Pods that are later used by the k8s Service
         - containerPort: 3033
           name: "uwsgi"
         - containerPort: 9099
           name: "stats"
     - name: nginx
       image: gcr.io/your-project/nginx:latest
       ports:
         - containerPort: 8000
           name: "http"
         - containerPort: 4430
           name: "https"
       volumeMounts: # this holds our SSL keys to be used with nginx. I haven't found a way to use the http load balancer of google with k8s.  
         - name: "secrets"
           mountPath: "/path/to/secrets"
           readOnly: true
     volumes:
       - name: "secrets"
         secret:
           secretName: "ssl-secret"
And now the web-service.yaml:apiVersion: v1
kind: Service
metadata:
 name: web
spec:
 ports:
 - port: 80
   targetPort: 8000
   name: "http"
   protocol: TCP
 - port: 443
   targetPort: 4430
   name: "https"
   protocol: TCP
 selector:
   app: web
 type: LoadBalancer

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That type: LoadBalancer at the end is super important because it tells k8s to request a public IP and route the network to the Pods with the selector=app:web.
If you're doing a rolling-update or just restarting your pods, you don't have to change the service. It will look for pods matching those labels.

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Celery

Also a replication controller that runs 4 pods containing a single container: gorgias/worker, but doesn't need a service as it only consumes stuff. Here's our worker-rc.yaml:

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apiVersion: v1
kind: ReplicationController
metadata:
 name: worker
spec:
 replicas: 2
 selector:
   app: worker
 template:
   metadata:
     labels:
       app: worker
   spec:
     containers:
     - name: worker
       image: gcr.io/your-project/worker:latest

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Some tips

• Installing some python deps take a long time, for stuff like numpy, scipy, etc.. try to install them in your namespace/app container using pip and then do another pip install in the container that extends it, ex: namespace/web, this way you don't have to rebuild all the deps every time you update one package or just update your app.
• Spend some time playing with gcloud and kubectl. This will be the fastest way to learn of google cloud and k8s.
• Base image choice is important. I tried phusion/baseimage and ubuntu/core. Settled for phusion/baseimage because it seems to handle the init part better than ubuntu core. They still feel too heavy. phusion/baseimage is 188MB.

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Conclusion

With Kubernetes, docker finally started to make sense to me. It's great because it provides great tools out of the box for doing web app deployment. Replication controllers, Services (with LoadBalancer included), Persistent Volumes, internal DNS. It should have all you need to make a resilient web app fast.

At Gorgias we're building a next generation helpdesk that allows responding 2x faster to common customer requests and having a fast and reliable infrastructure is crucial to achieve our goals.

If you're interested in working with this kind of stuff (especially to improve it): we're hiring!

We've released a new version of the Chrome Extension, with sharing features and a new navigation bar. We hope you'll love it!

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Share templates inside the extension

Before, the only way to share templates with your teammates was to login on Gorgias.io.

If you're on the startup plan, when you create a template, you can choose who has access to it: either only you, specific people, or your entire team.

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The account management section is now available in the extension, under settings.

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New navigation

Tags are now available on the left. It's easier to manage hundreds of templates with them.
You can also navigate through your private & shared templates. Shared templates include templates shared with specific people or with everyone.

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We hope you'll enjoy this new version of our Chrome Extension. As usual, your feedback & questions are welcome!

Today, we’re thrilled to announce that we’ve raised a $1.5 million Seed round led by Charles River Ventures and Amplify Partners, to help build our new helpdesk.

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We’re incredibly grateful to early users, customers, mentors we’ve met both at and Techstars.

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We started the journey with Alex at the beginning of 2015 with our Chrome extension, which helps write email faster using templates. We’ve been pleased all along with customers telling us about how helpful it was, especially for customer support.

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While building the extension, we’ve realized that a big inefficiency in support lies in the lack of integration between the helpdesk, the payment system, CRM and other tools support is using. As a result, agents need to do a lot of repetitive work to respond to customer requests, especially when the company is big.

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That’s why we’ve decided to build a new kind of helpdesk to enable customer support agents to respond 2x faster to customers. You can find out more and sign up for our private beta here.

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When a company has a lot of customers, support becomes repetitive. We want to provide support teams with tools to automate the way they treat simple repetitive requests. This way, they have more time for complex customer issues.

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We'll now focus on this helpdesk and on growing the team, oh, and if you'd like to join, we're hiring! We're super excited about this new helpdesk product. If you’re using the extension, don’t worry.

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Romain & Alex

We've been busy, but not deaf!

Last few months we got lots of feedback about our extension and found to our delight that most people are satisfied, but still a few recurrent issues came up:

• The HTML/WYSIWYG editor sucks.
• No support for Outlook.com.

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We listened and now we're presenting:

• A brand new editor
• Support for outlook.com
• More on the Rich-Text editor

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WYSIWYG editors for the web are notoriously buggy and are just difficult to develop.

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I have yet to see one that is bug free. There are few venerable editors that do a good job like TinyMCE, FKEditor or CKEditor.. but they are big and all have edge cases that break the intended formatting and add a lot of garbage html.

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There are newer good quality editors in town such as Redactor. The one that got my attention and finally landed in Gorgias is this wonderful editor called which is super lightweight, uses modern content-editable (no i-frames) and 'just works' most of the time. That's not to say it's perfect, but it's good enough and I'm satisfied with it's direction in terms of development.

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Enjoy it and as always send us bug-reports or feedback on: support@gorgias.com

A few things to know before you connect Gorgias MCP:

• Compatible AI tools: Claude, ChatGPT, Cursor, and any other MCP-compatible client
• Cost: Included in your existing Gorgias paid plan at no extra cost.
• AI Agent: Not required. Helpdesk-only customers have full access.
• Availability: Currently in open beta, open to all paid plan customers.

Ask your helpdesk anything, and get a real answer from your actual data in seconds.

Gorgias MCP connects your Gorgias account directly to Claude, ChatGPT, Cursor, or any other MCP-compatible AI tool. No more exporting data or re-explaining context every time you need a different angle on your tickets. Ask a question, follow up, and execute, all in the same conversation.

This guide covers what Gorgias MCP is, how to connect it, and six specific workflows you can run in your first session.

What is Gorgias MCP?

The Model Context Protocol (MCP) enables AI systems to link directly with other tools. 

The Gorgias MCP provides a first-party bridge, giving you a secure, direct connection between your chosen AI assistant and your Gorgias account.

Once connected, your LLM of choice can:

• Read and search your tickets and customer history
• Analyze patterns across CSAT scores, ticket volume, and resolution time
• Review your macros, rules, and tags
• Audit your AI Agent guidances and handoff tickets
• Draft new AI Agent guidances based on your existing setup
• Check user permissions and team configuration

How to connect Gorgias MCP

Setup takes about five minutes. Here is what you need and how to do it.

What you need:

• A Gorgias paid plan (any plan, AI Agent not required)
• An active subscription to Claude, ChatGPT, Cursor, or another MCP-compatible tool

Steps:

1. Open your AI tool and navigate to its settings. Look for an Integrations, Connectors, or MCP section.
2. Add a new MCP server using this endpoint: mcp.gorgias.com/mcp?gorgias_subdomain=YOUR-SUBDOMAIN. Replace YOUR-SUBDOMAIN with the part of your Gorgias URL that comes before .gorgias.com.
3. Authorize the connection via OAuth. You will be prompted to log into your Gorgias account.
4. Open a new chat and ask your first question.

The exact steps vary slightly depending on which AI tool you use. 

Read more: The Gorgias MCP setup guide specifically walks through Claude, ChatGPT, and Cursor.

6 workflows to run in your first session

Each workflow below includes the exact prompt to copy. Adjust the specifics to match your store, and run it in your first session.

1. Find out why your CSAT dropped last month

CSAT dips are easy to spot in your dashboard. The cause is harder. Figuring it out usually means opening tickets one by one, building a custom filter, and reading through results one by one.

The prompt:

• "Show me all tickets from the last 30 days with a CSAT rating of 1 or 2. Group them by the most common themes and tell me what customers said."

What you get: A grouped breakdown of the patterns behind your low scores, based on what customers wrote.

Follow-up prompts:

• "Were any of these customers VIPs or high-order-value shoppers?"
• "Which agent handled the most tickets in this group?"

2. Audit your AI Agent handoffs and close the biggest gap

Handoff reports tell you how many tickets AI Agent escalated to your team. They rarely tell you why. Diagnosing the root cause and writing new guidance to fix it are usually two separate tasks that happen days apart, if at all.

With Gorgias MCP, you can do both in the same conversation.

The prompt:

• "Show me the last 50 tickets that AI Agent handed off to a human agent. What are the three most common reasons it couldn't resolve them?"

Then, without starting a new chat:

• "Draft a new AI Agent guidance to handle [the top reason from above]. Use the format of my existing guidances."

What you get: A diagnosis of where AI Agent is struggling and a ready-to-review draft guidance to address the top gap. You apply it manually in Gorgias, but the thinking is done.

Follow-up prompts:

• "Are these handoffs concentrated on a specific product category or ticket tag?"
• "How do these handoff reasons compare to last month?"

3. Map customer friction points to your product pages

This is one of the most powerful early use cases customers have found. By connecting Gorgias MCP with the Shopify MCP, you can link what customers complain about in tickets to the product pages where that friction lives.

To use this workflow, connect both Gorgias MCP and Shopify MCP oto your AI tool first.

The prompt:

• "What are the most common complaints or questions about [product name or category]? For each one, note whether the issue could be addressed by improving the product description, adding sizing information, or clarifying the return policy."

What you get: A prioritized list of friction points tied to specific content gaps on your product pages, ready to hand off to your product or merchandising team.

Follow-up prompts:

• "Which of these friction points appears most often in tickets tagged as returns?"
• "Are any of these issues specific to one variant or size?"

4. Get a custom ticket volume breakdown without building a report

Gorgias's built-in reports cover the core metrics well. But when you need a specific cut, ticket volume by intent for a particular channel, or resolution time broken down by tag, getting there requires an export and a spreadsheet.

Ask for exactly what you need instead.

The prompt:

• "Break down our ticket volume for the last 30 days by intent. Show me the top 10 intents, the volume for each, and the average resolution time per intent."

What you get: A custom breakdown you can copy directly into a slide or share with your team, built from a single question.

Follow-up prompts:

• "Which intent has the highest first-contact resolution rate?"
• "Compare this to the previous 30-day period. What changed the most?"

5. Find every topic your macros don't cover

Every support inbox has repeat questions that agents are still answering manually. Some of those topics may already have a macro, but others show up again and again without consistent macro usage. This workflow helps surface the best candidates to standardize next.

The prompt:

• “Look at all tickets from the last 60 days. Which topics or questions appear most often, and for which of them do we see little or no macro usage? List the top 10.”

What you get: A ranked list of frequent support topics that appear to be handled manually most of the time, helping your team prioritize where a new or better macro could save the most effort.

Follow-up prompts:

• “Draft a macro response for the most common topic in that list.”
• “Which of these topics has the longest average first response time?”
• “Which of these topics already use macros sometimes, but not consistently?”

6. Clean up your tag taxonomy

Tag libraries tend to grow organically. Over time, teams end up with overlapping labels like “return,” “returns,” and “return-request,” along with tags that are rarely used and naming patterns that make reporting harder to trust. This workflow helps identify cleanup opportunities based on how tags are actually used on tickets.

The prompt:

• “Review the tags used on tickets in my Gorgias account. Flag any that appear to overlap in meaning, are used fewer than five times total, or seem inconsistent with the others. Group your suggestions by consolidation opportunity.”

What you get: A list of likely tag issues grouped by type — duplicates, low-use tags, and inconsistent naming — based on actual ticket usage, with consolidation suggestions your team can act on.

Follow-up prompts:

• “For the duplicates you flagged, which version is used more often? That’s the one I’ll keep.”
• “Which of these low-use tags have appeared at all in the last 90 days?”
• “Which consolidation opportunities would have the biggest reporting impact?”

Get more out of the data you already have

Everything in the workflows above is already sitting in your Gorgias account. Gorgias MCP just makes it conversational.

Not sure where to start? Try workflow 1 (CSAT drop report) or workflow 6 (tag cleanup) first. Both deliver results quickly and work regardless of how your account is configured.

Gorgias MCP is currently in open beta and available to all paid plan customers.

Try Gorgias MCP now →

TL;DR:

• Gaia for Zendesk is a free Chrome extension that analyzes your Zendesk ticket history and identifies gaps in your AI Agent setup.
• It generates ready-to-review guidances, instructions, voice-of-customer analysis, and Copilot procedures — nothing is applied without your approval.
• Install it in five minutes and run your first analysis in under a minute.

Getting more out of your Zendesk AI Agent comes down to better configuration. The problem is that auditing your own setup requires time you don't have.

Gaia for Zendesk is a free Chrome extension from Gorgias that clears that backlog in minutes. It connects to your Zendesk account, reads your tickets, and generates the components your AI Agent needs to resolve more conversations without escalation.

Below, you'll find everything you need to get started: how to install Gaia, what it can do, and the use cases teams are already putting it to work for.

Jump to:

• Who is this for?
• What is Gaia for Zendesk?
• Why use Gaia instead of configuring it yourself?
• How to install Gaia for Zendesk
• Four ways to use Gaia for Zendesk

Who is this for?

Zendesk Suite admins and agents who want to set up or improve their Zendesk AI Agent (and, optionally, Zendesk Copilot). 

Throughout this article, "AI Agent" refers to Zendesk's own AI Agent feature, not Gorgias's product. Gaia is the Gorgias-built Chrome extension that helps you configure it.

What is Gaia for Zendesk?

Gaia for Zendesk is a free Chrome extension built by Gorgias that connects to your Zendesk account and analyzes your real ticket history.

It autonomously transforms that data into the core components your Zendesk AI Agent needs to resolve more tickets without human intervention: guidances, instructions, voice-of-customer insights, and Copilot procedures.

Gaia opens automatically as a side panel on any .zendesk.com page. You choose a workflow, Gaia runs the analysis and generates structured drafts, and you review and approve what should be applied to your Zendesk workspace.

What Gaia can do:

• Improve your Zendesk AI Agent: Scans escalated tickets, identifies gaps in your existing setup, and proposes targeted updates to your guidances.
• Create your first instructions: Generates 15 best-practice AI Agent instructions, adapted from Gorgias templates and tailored to your ticket data.
• Analyze your tickets: Produces a voice-of-customer report covering ticket volume, intents, and escalation patterns from the last 90 days.
• Create your first procedures: Drafts 10 Copilot procedures using the Gaia WHEN/IF/THEN format (requires the Copilot add-on in Zendesk).

Good to know: Gaia is autonomous in how it analyzes your data and generates recommendations, but nothing is applied without your approval.

Why use Gaia instead of configuring it yourself?

How well your AI Agent performs comes down to how well it is configured. Clear instructions, well-defined intents, and up-to-date procedures are what separate an AI Agent that resolves tickets from one that escalates them — and building that foundation manually takes time most teams do not have.

Gaia removes that constraint by turning your existing support data into structured, ready-to-review outputs:

• Skip the blank page. Start from drafts generated from your own ticket history instead of building from scratch.
• Get from setup to first results faster. Gaia analyzes up to 90 days of tickets in minutes and produces implementation-ready content, so you move from setup to value in hours, not weeks.
• Stay in control. Every output is surfaced for review so you can validate accuracy and alignment with your policies.
• Keep improving over time. Re-run Gaia regularly to capture new intents, identify gaps, and maintain coverage as your business evolves.

Tip: Teams that define five or more strong guidances typically see a meaningful lift in the share of tickets their AI Agent resolves without escalation. Gaia is designed to help you reach and expand beyond that baseline quickly.

How to install Gaia for Zendesk

Installation takes about five minutes. You'll need admin access to your Zendesk account to generate an API token.

Make sure you're using Google Chrome or another Chromium-based browser (such as Edge or Brave). Gaia is not available for Safari or Firefox at launch.

Follow these steps:

1. Install the Gaia for Zendesk extension. Go to the Chrome Web Store listing for Gaia for Zendesk by Gorgias and click Add to Chrome. Confirm the permissions to complete installation.‍

2. Generate a Zendesk API token. In Zendesk, navigate to Admin Center › Apps and integrations › Zendesk API. Enable Token access if needed, then click Add API token. Copy and store the token securely (it will not be visible again).

3. Open the Gaia extension and add your credentials. Click the Gaia icon in your Chrome toolbar, then open Settings. Enter: ‍

• ‍Zendesk subdomain: the part before .zendesk.com in your account URL (for example, acme if your URL is acme.zendesk.com).‍‍
• ‍Email: the email of the Zendesk admin who created the API token.‍
• API token: paste the token you generated in step 2.

4. Click Save. Gaia will validate the connection.

5. Open any Zendesk page. Navigate to any page in your Zendesk account. Gaia will appear as a side panel where you can select a workflow and begin.

Four ways to use Gaia for Zendesk

Here are the four most common ways teams use Gaia for Zendesk.

1) Audit and improve an existing AI Agent setup

Best for: Teams already using Zendesk AI Agent or Answer Bot but not reaching their automation goals

Select Improve my AI Agent. Gaia analyzes escalated tickets against your current guidances to identify missing intents, unclear instructions, and outdated logic, then proposes prioritized improvements.

2) Bootstrap AI Agent from a blank slate

Best for: Teams starting without an established AI configuration

Select Create my first instructions. Gaia generates a foundational set of 15 instructions covering common ecommerce scenarios (order status, refunds, cancellations, shipping, returns).

3) Run a voice-of-customer analysis

Best for: Support, CX, and operations teams planning improvements or reporting on performance

Select Analyze my tickets. Gaia summarizes ticket volume, top intents, and escalation drivers to highlight where automation or process improvements will have the greatest impact.

4) Roll out Copilot procedures

Best for: Teams using Zendesk Copilot who want consistent, scalable agent workflows

Requires the Copilot add-on in Zendesk. Select Create my first procedures. Gaia converts real agent behavior into structured WHEN/IF/THEN procedures that standardize how common scenarios are handled.

Your AI Agent is resolving less than it should — and it’s costing you

Gaia connects to your Zendesk account, reads your ticket history, and shows you exactly where your setup is falling short. Install the free Chrome extension and run your first analysis in under a minute.

Get Gaia for Zendesk for free →

TL;DR:

• Managing support across multiple channels without a system is the core problem, not the volume of tickets.
• A knowledge base of your most common questions is the foundation for everything else, from templates to AI tools.
• Automate in layers: start with auto-acknowledgment, then AI-assisted drafts, then full automation only for zero-judgment replies.
• When free tools stop keeping up, a helpdesk connects all your channels and gives you full customer context in one place.

If you're a small business owner handling support solo, you’re familiar with the following: questions coming in from every direction, hours spent typing out replies, and customers waiting days for an answer. What you’re missing is a system that holds it all together.

Below, we’ll walk you through how to build a reliable customer support operation solo, starting with free tools and simple processes, with options to scale as your business grows.

{{lead-magnet-1}}

Why customer support feels overwhelming for small businesses

Running support solo is hard for a few specific reasons:

• Every channel is its own inbox. Instagram DMs, email, WhatsApp — a customer's history is spread across different tools, so every interaction starts with zero context.
• Your best answers live in your head. Until they're written down, no new hire can help without escalating back to you.
• The same questions come in on repeat. Most small businesses find that nearly all of their support tickets are variations of the same handful of questions. Without a system, you're writing the same replies from scratch every time.
• There's no triage. Urgent issues and FAQs land in the same inbox with equal weight, so prioritizing takes judgment and time.

Most of the fixes in this guide address one of those problems directly. The fastest wins come from consolidating where support happens and getting your knowledge out of your head and into a format that works for you, and eventually, for the tools that can help you.

Related reading: Why consolidated doesn’t mean compromised: Top 3 myths debunked

Step 1: Pick one channel and own it

Before adding any tools or automation, identify where the majority of your inquiries come from. Pull a rough count over the last 30 days across your active channels. If one channel tops the list, that's your starting point.

If ticket volume is roughly the same across channels, choose whichever works best for your needs. Which channel lets you respond most effectively? Which one can keep detailed and retrievable records? Where do customers like to receive answers?

A quick overview of the top support channels: 

• Email supports longer answers, searchable history, and batching replies, meaning you can respond to 10 emails at once.
• DMs are faster but naturally become back-and-forth exchanges that can turn into lengthy message threads. Most platforms also don’t offer deep search beyond keyword matching.
• Live chat sets response time expectations that are hard to meet without a dedicated support agent.

For most small businesses, email is the stronger primary channel. Once you've chosen yours, redirect everyone to it: a link in your Instagram bio, a WhatsApp auto-reply pointing to your email, or a contact form confirmation that sets response time expectations.

You don't have to abandon your other channels. Just choose one as the place where conversations get resolved.

Related reading: How to implement an omnichannel customer service strategy

Step 2: Build your support knowledge base

The single most helpful thing you can do for your support operation is write down your best answers. Everything else — templates, AI tools, VA training, and customer-facing FAQs — depends on this foundation.

How to build it:

1. For one to two weeks, save every reply you send. Paste them into a running Google Doc or Notion page as you go.
2. Look for the patterns. Shipping timelines, return policies, pricing questions, order status, and product fit will come up again and again.
3. Turn the best version of each recurring answer into a clean, reusable write-up.

Efficiency tip: Use AI to speed this up. Export past support emails, chat histories, or even a rough doc of notes, and paste them into Claude or ChatGPT. Ask it to group recurring questions into categories and draft answers for each. What might take an afternoon of manual work takes just 20 minutes.

Put the knowledge base to work in two ways

Internally: This doc becomes the main reference for anyone or any tool, answering on your behalf. It's the foundation for the canned responses in the next section, and the training material if you ever bring on a new teammate.

Externally: A customer-facing FAQ reduces inquiries before they happen. If a customer can answer their own question at midnight without waiting for you, that's one less ticket in your inbox. Add FAQs to your website footer, a dedicated FAQ page, or directly on product pages for questions specific to that item.

Tip for social-heavy brands: Your FAQ content doesn't have to live only on your website.

• On Instagram, save FAQ content as dedicated Highlights (e.g., "Shipping," "Returns," "Sizing") so customers can find answers without DMing you.
• A pinned post on Instagram or Facebook can address your single most common question before it gets asked.
• On TikTok, pin your most-viewed FAQ-style videos to your profile so new visitors see them immediately.

Step 3: Set up canned responses and templates

Once your knowledge base exists, turn it into templates. This is the fastest operational win available to a one-person support operation.

In Gmail, canned responses let you insert a full reply with two clicks. Set them up for your top five to ten questions, and you'll cut your average reply time significantly. Most other email clients have an equivalent feature.

A few tips for making templates work:

• Start with your highest-volume questions, not your most complex ones. The goal is to reduce time spent on repetitive replies, not to script every possible scenario.
• Treat templates as a starting point. One sentence of personalization, using the customer's name or referencing their specific order, goes a long way toward making your pre-written replies feel human.
• Keep a master doc as your source of truth. Even if you're using Gmail templates, maintain a doc with all your answers in one place. It's easier to update, share, and eventually hand off.

You don’t need a formal template system yet. A Google Doc you copy from is still a real improvement over writing from scratch. Don't wait for the perfect setup.

Step 4: Add automation gradually, in the right order

The instinct when you're overwhelmed is to automate everything at once. The better approach is to layer it in, starting with the lowest-risk options and working your way toward AI tools only once you trust the outputs.

Layer 1: Automatic acknowledgment to set expectations

Set up an immediate reply on every channel that confirms you received the message and gives an expected response time. This one change reduces follow-up messages significantly. Customers don't need an instant answer, they need to know you're there. Most email clients and helpdesks offer this for free.

Layer 2: AI-assisted drafts to reduce time spent typing

Tools that draft a reply for you to review before sending are the right entry point for AI in a small business support operation. You edit instead of write from scratch, which is meaningfully faster, and you stay in control of what goes out. This addresses the most common concern about AI in support: not that it can't help, but that it might say something wrong without you knowing.

Layer 3: Automated replies to reduce follow-ups

Order confirmations, shipping notifications, and return receipts are fully automatable. The answers are always the same, no context is needed, and customers expect them to be automated. Start here before moving to anything more complex.

Layer 4: AI auto-responses to answer repetitive questions

At this point, you trust the accuracy of your knowledge base and are comfortable with how your AI tool responds to customers. Now, you can consider letting it automatically respond to straightforward FAQs. 

Be transparent about it. A simple label like "Hi, I'm a support assistant" sets the right expectation and reduces frustration if the answer misses. Only apply this layer when the previous three are working reliably.

What to look for when you're ready for a helpdesk

Free tools can only do so much. When other parts of your business end up being neglected because of all the time you spend on support, it’s time to move to paid tools and services.

A helpdesk solves a specific problem that free tools can't: it connects your channels so every conversation, regardless of where it started, lives in one place with full context.

What a helpdesk gives you that a shared inbox doesn't:

• A unified inbox that pulls in email, social media DMs, WhatsApp, phone, and contact forms together
• Customer history attached to every ticket, so you can see past orders and past conversations before you reply
• Rules and routing that automatically tag, assign, or respond to certain ticket types
• Shared access for a VA or team member, without the risk of two people replying to the same ticket

Signs it's time to make the move:

• You're spending more than two hours a day on support
• Customers are following up because their original message got missed
• You're considering bringing on help, and you need a system they can work within

Helpdesks with free tiers or trials:

When evaluating any of these, prioritize integration with your ecommerce platform, ease of setup, and whether the AI features assist your replies or send automatically without review. For most small businesses just getting started, assisted drafting is the right fit before committing to full automation.

Quick wins you can implement today

These are the changes that take under an hour and make an immediate difference:

• Set an auto-reply on every active channel with your expected response time
• Start a Google Doc with your top 10 most common questions and your best answers to each
• Set up Gmail templates for your five most frequent replies
• Add a FAQ section to your website, even a short one
• If Instagram or TikTok are primary channels for your audience, repurpose your FAQ content as Highlights or pinned posts
• Pick one secondary channel and add a redirect message pointing customers to your primary one

None of these require a paid tool or a technical setup. They just require an hour of focused work and the recognition that a small amount of structure now saves a large amount of time later.

Get the foundation right, and the tools will follow

Good support at small scale comes down to sequence, not software. Consolidate your channels, document your best answers, build templates, and layer in automation only once the basics are working. Each step makes the next one easier.

When free tools stop keeping up, Gorgias connects your channels, integrates with Shopify and BigCommerce, and includes AI features that assist your replies rather than replace your judgment. 

Start a free trial now →

TL;DR:

• AI agent ROI is about business impact, not just ticket deflection, so your report should connect automation to cost savings, team capacity, customer experience, and revenue.
• Executives care about five core metrics: cost savings, automation success rate, customer experience impact, team capacity, and revenue impact.
• Quality matters as much as speed, because faster replies only prove ROI when AI resolves issues accurately and avoids repeat contacts or unnecessary handoffs.
• A strong AI ROI report explains the math, including AI-handled interactions, time saved, cost per human-handled ticket, AI tool costs, and revenue attribution rules.
• AI ROI reporting should become a regular workflow, with daily performance checks, weekly handover reviews, and a monthly business summary for leadership.

Your AI agent is answering tickets, but leadership wants proof that it’s paying off.

That proof can’t stop at ticket deflection or faster replies. To show real AI agent ROI, you need to connect automation performance to cost savings, team capacity, customer experience, and revenue impact.

This guide breaks down the metrics that matter, how to calculate them, and how to turn AI reporting into a business case executives can understand.

Why proving AI agent ROI is harder than it sounds

AI agent ROI is hard to prove because most teams measure activity, not impact.

Ticket deflection doesn’t always mean resolution: A deflected ticket is not always a solved problem. A customer may abandon the conversation, ask the same question later, or contact your team through another channel.

Automation rate needs context: A high automation rate can look impressive in a report. But it needs to be paired with metrics like CSAT, handover rate, repeat contact rate, and resolution time to show whether AI is handling the right tickets well.

Speed can hide quality issues: AI can reduce FRT and resolution time quickly. But fast answers only prove ROI when they’re accurate, helpful, and complete.

Cost savings need a clear calculation: Leadership needs to know how your team calculated savings. That means connecting automated interactions to agent time saved, average handle time, cost per ticket, and AI tool costs.

Revenue impact is easy to miss: AI agents can influence purchases, recommend products, or recover carts. Those results are harder to prove when AI reporting, support data, and ecommerce data live in separate tools.

ROI needs a complete view: No single metric proves AI agent ROI. The strongest reports connect efficiency, customer experience, team capacity, and revenue impact.

The 5 metrics executives actually care about

To prove AI agent ROI, focus on metrics that connect AI performance to business outcomes.

Leadership does not need every AI stat in your dashboard. They need to know whether AI is lowering costs, helping the team scale, protecting customer experience, and contributing to revenue.

1. Cost savings

Executives care about whether AI is reducing the cost of support without creating more work somewhere else.

Track:

• automated interactions
• time saved by agents
• cost per human-handled ticket
• cost per AI-handled interaction

Cost savings show how much money your AI agent saves by handling customer interactions instead of a human agent.

Show how many interactions AI handled, what those interactions would have cost your team, and what it costs for AI to handle them instead.

AI ROI is not just about cutting costs. It’s about helping the business handle more volume without increasing support costs at the same pace.

2. Automation success rate

Is your conversational AI actually giving customers correct, high-quality answers?

Track:

• AI automation rate
• success rate
• handover interactions
• repeat contact rate 

Automation success rate shows whether your AI agent is actually resolving customer interactions without human help.

A high automation rate with high escalations may indicate poor AI quality.

A lower automation rate with strong CSAT and fewer repeat contacts may show that AI is handling the right tickets well.

The best AI programs optimize for successful resolution, not maximum automation.

3. Customer experience impact

AI should improve efficiency without hurting the customer experience.

Track:

• customer satisfaction score (CSAT)
• first response time (FRT)
• resolution time
• human response time after AI handoff
• repeat contact rate

Customer experience metrics show whether customers are getting faster, helpful support from AI.

Speed is not the same as quality.

AI can reduce FRT and resolution time, but those gains only matter when customers still get accurate, complete answers.

The strongest AI reports show that customers got help faster and still had a good experience.

4. Team capacity

Executives care about whether AI helps the team scale.

Track:

• time saved by agents
• automated interactions
• closed tickets
• coverage rate
• workload handled by AI

Team capacity shows how much repetitive work AI removes from the queue.

This matters because human agents can spend more time on complex issues, high-value customers, retention risks, and revenue-generating conversations.

Team capacity is not the same as headcount reduction.

A stronger story is that AI helps the same team handle more customer demand without adding the same amount of cost or pressure.

5. Revenue impact

Executives care about whether AI contributes to revenue, not just cost savings.

Track:

• revenue influenced
• orders influenced
• revenue per interaction
• AOV
• purchase rate from AI-assisted conversations

Revenue impact shows whether AI helps shoppers choose products, get answers before purchase, use discounts, or recover carts.

Revenue attribution needs a clear window.

Explain how your team defines an AI-influenced purchase, such as an order placed within a set number of days after an AI-assisted conversation.

AI agents are becoming part of the shopping experience, not just a way to reduce support tickets.

How to build an executive AI ROI report

An executive AI ROI report should show what changed because of your AI agent.

Start with the outcome leadership cares about most, then add the proof underneath.

Step 1: Lead with the headline result

Start with the clearest business impact. That might be cost saved, time saved, revenue influenced, or tickets resolved without human help.

For example: “Our AI agent resolved 8,000 interactions this month and saved the team 420 hours.”

This gives leadership the answer before they have to interpret the data.

Step 2: Show how you calculated it

Explain the math behind the headline result.

If you’re reporting cost savings, show the number of AI-handled interactions, average cost per human-handled ticket, and cost per AI-handled interaction.

This makes the number easier to trust.

Step 3: Add quality checks

Next, prove the AI agent is not just handling volume.

Add success rate, handover interactions, CSAT, and repeat contact rate if available.

This shows whether AI is solving issues well, not just removing tickets from the queue.

Step 4: Connect the result to team capacity

Then show how AI changed the team’s workload.

Use time saved, automated interactions, and queue impact to show whether agents had more time for complex issues, retention risks, or sales-focused conversations.

This turns AI reporting into an operations story.

Step 5: Add revenue impact if AI supports shopping

If your AI agent answers pre-purchase questions, include revenue metrics.

Show revenue influenced, orders influenced, revenue per interaction, and AOV.

Make the attribution window clear, such as purchases made within three days of an AI-assisted conversation.

Step 6: End with what improves next

Close the report with the actions your team will take next.

That might include updating AI instructions, improving handoff rules, filling help center gaps, or reviewing low-CSAT conversations.

This shows leadership that AI performance is being actively managed, not passively monitored.

Common mistakes teams make when reporting AI ROI

AI ROI reporting falls flat when the numbers look impressive but don’t answer the real business question.

Avoid these common mistakes when you’re building your report.

1. Leading with ticket deflection

Ticket deflection is useful, but it doesn’t prove ROI on its own.

A ticket can be deflected without being resolved. Pair deflection with success rate, CSAT, handovers, and repeat contact rate to show whether AI actually solved the issue.

2. Treating automation rate like the goal

A higher automation rate is not always better.

The goal is to automate the right conversations well. If automation rate rises while handovers, repeat contacts, or poor CSAT scores also rise, your AI agent may be creating hidden work.

3. Ignoring the handoff experience

A handoff is not a failure when it gets the customer to the right person faster.

But leadership should know what happens after AI escalates a ticket. Track human response time after AI handoff so you can spot delays, routing gaps, or tickets that need clearer escalation rules.

4. Reporting cost savings without the math

Cost savings need context.

Show how you calculated the number, including AI-handled interactions, average cost per human-handled ticket, agent time saved, and AI tool cost. This makes the ROI story more credible.

5. Leaving revenue out of the report

AI agents can do more than reduce support volume.

If your AI agent helps shoppers choose products, answers pre-purchase questions, recommends SKUs, or offers discounts, include revenue impact. Executives need to see where AI supports both efficiency and sales.

6. Measuring AI in a separate tool

AI reporting gets harder when support data, automation data, CSAT, and revenue live in different systems.

Disconnected reporting makes it harder to prove what changed because of AI. A stronger setup gives your team one place to track AI performance across support, customer experience, and revenue.

How to build AI ROI reporting into your day-to-day workflow

AI ROI reporting works best when it becomes a regular operating habit.

A monthly report can show leadership the results, but your team needs a daily and weekly rhythm to understand what’s improving, what’s breaking, and where AI needs coaching.

• Check performance daily: Review automated interactions, handovers, success rate, CSAT, and any unusual spikes in ticket volume. This helps your team catch issues early, before they show up in an executive report.
• Review handovers weekly: Look at the most common reasons AI escalated tickets to a human agent. Then decide whether the fix is better AI instructions, clearer help center content, more accurate product data, or a new handoff rule.
• Connect AI performance to team workload: Track whether AI is actually giving agents more capacity. Look at time saved, queue volume, AHT, and the types of tickets agents are handling.
• Watch quality, not just automation: Pair automation rate with CSAT, repeat contact rate, handovers, and resolution time. This helps your team avoid chasing a higher automation rate at the expense of customer experience.
• Share a monthly business summary: Turn the daily and weekly signals into a simple leadership update. Show what AI handled, what it saved, how it affected customers, how it affected the team, and what your team is improving next.

See AI agent ROI in one place

Proving AI ROI gets harder when your support, automation, and revenue data live in separate tools.

Gorgias’s AI Agent brings AI-specific reporting into the same helpdesk your team uses every day, so you can track what AI handled, what it saved, and how it contributed to the customer experience.

Book a demo to see how AI Agent helps ecommerce teams measure and improve AI support from one customer experience platform.

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