TLDR: https://github.com/xarg/pghoard-k8s

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This is a small tutorial on how to do incremental backups using pghoard for your PostgreSQL (I assume you’re running everything in Kubernetes). This is intended to help people to get started faster and not waste time finding the right dependencies, etc..

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pghoard is a PostgreSQL backup daemon that incrementally backups your files on a object storage (S3, Google Cloud Storage, etc..).
For this tutorial what we’re trying to achieve is to upload our PostgreSQL to S3.

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First, let’s create our docker image (we’re using the alpine:3.4 image cause it’s small):

FROM alpine:3.4

ENV REPLICA_USER "replica"
ENV REPLICA_PASSWORD "replica"

RUN apk add --no-cache \
   bash \
   build-base \        
   python3 \
   python3-dev \
   ca-certificates \
   postgresql \
   postgresql-dev \
   libffi-dev \
   snappy-dev
RUN python3 -m ensurepip && \
   rm -r /usr/lib/python*/ensurepip && \
   pip3 install --upgrade pip setuptools && \
   rm -r /root/.cache && \
   pip3 install boto pghoard


COPY pghoard.json /pghoard.json.template
COPY pghoard.sh /

CMD /pghoard.sh

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‍

REPLICA_USER and REPLICA_PASSWORD env vars will be replaced later in your Kubernetes conf by whatever your config is in production, I use those values to test locally using docker-compose.

‍

The config pghoard.json which tells where to get your data from and where to upload it and how:

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{
   "backup_location": "/data",
   "backup_sites": {
       "default": {
           "active_backup_mode": "pg_receivexlog",
           "basebackup_count": 2,
           "basebackup_interval_hours": 24,
           "nodes": [
               {
                   "host": "YOUR-PG-HOST",
                   "port": 5432,
                   "user": "replica",
                   "password": "replica",
                   "application_name": "pghoard"
               }
           ],
           "object_storage": {
               "aws_access_key_id": "REPLACE",
               "aws_secret_access_key": "REPLACE",
               "bucket_name": "REPLACE",
               "region": "us-east-1",
               "storage_type": "s3"
           },
           "pg_bin_directory": "/usr/bin"
       }
   },
   "http_address": "127.0.0.1",
   "http_port": 16000,
   "log_level": "INFO",
   "syslog": false,
   "syslog_address": "/dev/log",
   "syslog_facility": "local2"
}

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Obviously replace the values above with your own. And read pghoard docs for more config explanation.

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Note: Make sure you have enough space in your /data; use a Google Persistent Volume if you DB is very big.

‍

Launch script which does 2 things:

1. Replaces our ENV variables with the right username and password for our replication (make sure you have enough connections for your replica user)
2. Launches the pghoard daemon.

‍

‍

#!/usr/bin/env bash

set -e

if [ -n "$TESTING" ]; then
   echo "Not running backup when testing"
   exit 0
fi

cat /pghoard.json.template | sed "s/\"password\": \"replica\"/\"password\": \"${REPLICA_PASSWORD}\"/" | sed "s/\"user\": \"replica\"/\"password\": \"${REPLICA_USER}\"/" > /pghoard.json
pghoard --config /pghoard.json

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Once you build and upload your image to gcr.io you’ll need a replication controller to start your pghoard daemon pod:

‍

apiVersion: v1
kind: ReplicationController
metadata:
 name: pghoard
spec:
 replicas: 1
 selector:
   app: pghoard
 template:
   metadata:
     labels:
       app: pghoard
   spec:
       containers:
       - name: pghoard
         env:
           - name: REPLICA_USER
             value: "replicant"
           - name: REPLICA_PASSWORD
             value: "The tortoise lays on its back, its belly baking in the hot sun, beating its legs trying to turn itself over. But it can't. Not with out your help. But you're not helping."
         image: gcr.io/your-project/pghoard:latest

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The reason I use a replication controller is because I want the pod to restart if it fails, if a simple pod is used it will stay dead and you’ll not have backups.

‍

Future to do:

• Monitoring (are you backups actually done? if not, do you receive a notification?)
• Stats collection.
• Encryption of backups locally and then uploaded to the cloud (this is supported by pghoard).

Hope it helps, stay safe and sleep well at night.

‍

Again, repo with the above: https://github.com/xarg/pghoard-k8s

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.

‍

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

‍

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..

‍

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

‍

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.

‍

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

‍

We chose Kubernetes too

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

‍

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:

‍

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

‍

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

‍

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)

‍

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.

‍

Postgres

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

‍

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:

‍

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

‍

‍

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.

‍

‍

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

‍

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.

‍

‍

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.

‍

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

‍

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.

‍

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.

‍

We listened and now we're presenting:

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

‍

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

‍

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.

‍

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

TL;DR:

• Conversational commerce replaces static support with real-time conversations. Instead of making customers wait or dig through FAQs, brands can respond instantly via chat, messaging apps, and voice assistants.
• The main types are live chat, AI assistants, messaging apps, and voice support. Each helps guide shoppers and answer questions instantly.
• It’s most effective during key moments like cart hesitation, post-purchase anxiety, and peak seasons. Proactive conversations reduce drop-offs and boost conversions.
• Start small and scale. Begin with repetitive questions or cart recovery, then layer in automation and AI as you grow.

While your competitors are still making customers wait days for email replies, the smartest brands are having conversations that close sales in real time.

Instead of forcing customers to search through FAQs or go through an automation loop, conversational commerce lets you have instant chats through live chat, messaging apps, and even AI assistants.

In this guide, we’ll explain conversational commerce, where it delivers the most value, and how to start using it to drive revenue and improve CX without overwhelming your team.

What is conversational commerce?

Conversational commerce means using real-time, two-way conversations as your storefront. Rather than bottling up questions in FAQ pages or forcing customers to wait for your support team to respond, you can instantly connect via:

• Chat
• AI agents
• Messaging apps
• Voice assistants

Maybe someone is on your product page and asks a question like, “Does this jacket run large?”. Through chat, they get an instant answer, increasing the chance of a sale. Or a shopper receives personalized recommendations via WhatsApp and checks out, all without leaving the app.

These channels allow you to meet customers where they already are, effortlessly. When paired with AI chatbots, you can deliver fast, accurate responses 24/7, even while your team is off the clock. That means better experiences for your customers and more sales captured for your brand.

Conversational commerce bridges the gap between shopping and support. It turns your support team (and AI tools) into revenue drivers by helping shoppers feel seen, heard, and ready to buy.

Types of conversational commerce

Conversational commerce means bringing your storefront into the flow of conversation, wherever that happens for your customers. 

Here’s where those conversations typically happen:

1. Live chat
2. AI assistants
3. Messaging apps
4. Voice assistants

1. Live chat

This is a chat widget on your site, often in the bottom right corner, where shoppers can ask questions and receive immediate answers from a human agent or automation.

It’s a quick path to support or purchase, which one agent can manage multiple chats from simultaneously, boosting efficiency and keeping things personal.

2. AI assistants

These smart helpers use Natural Language Processing (NLP) to understand what shoppers mean beyond what they type. They guide customers through questions, offer product suggestions, handle FAQs, and can sometimes complete transactions right in the chat, even handling post‑purchase support like order status or returns.

Natural Language Processing (NLP): The processing of understanding and interpreting natural language using computers. NLP is used in tasks such as sentiment analysis, summarization, speech recognition, and more.

3. Messaging apps

Think WhatsApp, Facebook Messenger, WeChat, and SMS—the apps where customers already spend their time in their day-to-day. Instead of sending them to shop on your website, you bring the shopping to them. Answer their questions, provide recommendations, and win purchases in a channel they already trust.

4. Voice assistants

Voice assistance isn’t limited to smart speakers like Siri and Alexa anymore.

Now, AI voice support lets brands deliver natural conversations over the phone, without needing a massive contact center team. These AI voice agents can:

• Answer common questions using branded knowledge
• Route calls or escalate when needed
• Handle returns, exchanges, or order tracking
• Personalize support based on customer intent and past behavior

AI-powered voice support combines the human feel of a phone call with the speed and accuracy of automation. It's especially useful for high-ticket products, customers who prefer calling, or peak season overflow when your human team is maxed out.

The benefits of conversational commerce for ecommerce brands

Conversational commerce isn’t a CX buzzword. When done right, it directly impacts your bottom line.

Here’s how it pays off for ecommerce brands:

1. Higher conversion rates
2. Faster and more efficient support
3. Bigger carts, fewer drop-offs
4. Stronger customer relationships

1. Higher conversion rates

When customers can ask questions and get answers in real time, whether it's sizing info, shipping details, or help choosing between products, they’re far more likely to hit “buy.”

Success story: Clothing brand Tommy John generated $106K+ in sales in just two months through conversation-led upselling and cross-selling, with a 15% conversion rate.

2. Faster and more efficient support

Conversational commerce tools like AI agents help offload the repetitive support tasks, including answering questions like “Where’s my order?” or “What’s your return policy?”

With that time back, agents get time back to:

• Handle complex or sensitive customer issues
• Follow up with VIP customers
• Collaborate with marketing and sales teams to improve processes
• QA conversations to enhance human and AI agent performance
• Update knowledge docs used by AI tools for more accurate resolutions

Instead of getting buried in basic tickets, your team gets to do the work that really moves the needle for your customers and your business.

Related: Every successful marketing campaign starts with a customer question

3. Bigger carts, fewer drop-offs

The right nudge at the right moment, like a personalized recommendation from an AI shopping assistant, can turn a single item into a full cart. You can also recover more abandoned checkouts by re-engaging customers directly through chat or a messaging app.

Read more: You’re missing out on sales without an AI shopping assistant—here’s why

4. Stronger customer relationships

Conversational commerce lets you meet customers with a human (or human-like) touch. When your brand is helpful, fast, and easy to talk to, shoppers remember and return. 

In the long run, that means better customer retention, higher lifetime value, and more organic growth through word of mouth.

When conversational commerce creates the biggest impact 

Conversational commerce shines brightest when the stakes are high or when the moment is just right.

Here are the critical moments where a real-time conversation can make all the difference:

1. When shoppers have items in their cart but are hesitating to check out
2. Right after customers place an order, and anxiety starts to kick in
3. During peak shopping seasons like Black Friday and Cyber Monday
4. When customers are browsing complex products like skincare, makeup, or apparel

1. When shoppers have items in their cart but are hesitating to check out

A customer’s on your product page, they’ve added an item to their cart, but are hesitating. Maybe they’re unsure about sizing, shipping time, or which variation to choose. This is where a quick, helpful chat, automated or human, comes in and becomes the difference between bounce and conversion.

Pro tip: Use proactive chat prompts based on page behavior to start the conversation before the shopper leaves.

2. Right after customers place an order, and anxiety starts to kick in

After a customer hits “place order,” expect more questions to roll into your inbox. Where’s my order? How do I track it? What’s your return policy? Post-purchase excitement—and anxiety—is normal, and a smart AI agent helps you get ahead of these questions while putting customers at ease.

3. During peak shopping seasons like Black Friday and Cyber Monday

Black Friday. Holiday rush. Product drops. These are prime opportunities to boost revenue—but they also flood your support team. Conversational commerce tools help you scale without sacrificing quality, keeping shoppers happy and sales flowing.

4. When shoppers are browsing complex products like skincare, makeup, or apparel

If you sell skincare, supplements, tech, or anything that requires a bit of education, your customers likely need guidance before they commit. A personalized conversation helps them find the right fit and feel more confident in their purchase.

What to consider before you start

Conversational commerce sounds exciting, and it is. But before you dive in, it’s worth thinking through a few key factors to set your team (and your customers) up for success.

1. Cost vs. ROI: Start small, scale smart
2. Team resources: Who’s managing the conversations?
3. Customer expectations: Meet them where they are

1. Cost vs. ROI: Start small, scale smart

You don’t need a full-blown chatbot army on Day 1. Start with your highest-impact touchpoints, like pre-sale FAQs or WISMO questions, and layer in automation over time. The goal is to generate clear ROI early, then expand once you see traction.

Here’s how to gradually implement automation into your CX process:

• Identify your top repetitive questions. Use your support data to pinpoint your most common tickets. For most brands, these are WISMOs, shipping concerns, and product-specific questions.
• Create macros for your most-asked questions. These macros will be used to answer the top recurring questions. For agents, this means no more copy-pasting the same responses.
• Build out self-service automation flows. Once you’re feeling more comfortable with automation, set up self-service flows to let customers resolve their own needs, like checking order status, starting a return, or finding their size.
• Automate your top channels. Don’t stop at email. Automate responses on live chat, Instagram DMs, and SMS too. Shoppers expect speed everywhere, not just on your site.
• Maintain impact, then introduce conversational AI. If your CSAT is still healthy after these changes, you can expand to using conversational AI for faster support and team efficiency.

The goal isn’t to automate everything, it’s to automate smartly so your team can spend time where it counts: high-touch sales, VIP support, and strategic growth.

Learn more: How Dr. Bronner's saved $100K/year by switching from Salesforce, then automated 50% of interactions with Gorgias

2. Team resources: Who’s managing the conversations?

Do you have in-house agents ready to handle live chat? Or do you need automation to handle the bulk of it? Make sure your setup aligns with your team’s bandwidth.

Pro tip: Tools like Gorgias AI Agent and Shopping Assistant can handle the support and sales heavy lifting, making them perfect for lean CX teams.

3. Customer expectations: Meet them where they are

Your customers aren’t just on your website. They’re messaging on Instagram, browsing via mobile, or checking their texts. To deliver great conversational commerce, you’ll want to show up in the places your shoppers already use.

Pro tip: Don’t spread your efforts too thin. Start with the channel that aligns with your goals and customer behavior, live chat, SMS, or social DMs, and build from there.

How to get started with conversational ecommerce in 2 steps

Ready to make conversational commerce part of your CX strategy? You don’t need to overhaul your tech stack or hire a whole new team. With Gorgias, you can start fast, stay lean, and scale smart.

Here’s how:

1. Start with AI Agent for 24/7 support

Gorgias AI Agent is designed to take repetitive tickets off your team’s plate, from “Where’s my order?” to “How do I make a return?” It understands natural language, pulls in relevant customer data, and responds in seconds—all using your brand’s approved knowledge.

The result is faster responses, fewer tickets, and more time back for your team.

2. Add Shopping Assistant to drive revenue

While AI Agent, covers the support front, Shopping Assistant is your digital salesperson. It engages high-intent shoppers in real time, recommends the right products, and even upsells or cross-sells based on what the customer is browsing.

Whether it’s helping someone choose the perfect shade or nudging them to complete their cart, Shopping Assistant is designed to increase AOV and reduce abandonment.

The future of ecommerce is conversational

Every time a shopper lands on your site, scrolls through Instagram, or replies to a shipping update, they’re opening the door to a conversation. The brands that show up quickly, helpfully, and with the right message, are the ones winning loyalty and revenue.

With AI Agent, you can automate accurate responses to common questions, giving your team time back without sacrificing customer experience. And with Shopping Assistant, you can turn those conversations into conversions, offering personalized recommendations, upsells, and discounts based on shopper intent.

You don’t need a massive team or months of setup to start. Just the right tools, and a strategy built for your customers.

Book a demo and learn how Gorgias helps you turn every conversation into an opportunity to grow.

{{lead-magnet-2}}

TL;DR:

• The best AI helpdesks offer smart ticketing, self-service, and sales automation. They combine multi-channel support, give teams flexible AI control, and double as an upselling tool that drives revenue.
• Each tool has a unique strength. Gorgias is best for ecommerce brands , Zendesk offers enterprise-level customization, Intercom is great for SaaS engagement, and Tidio is easy for small teams.
• There are also standalone AI tools that integrate with existing helpdesks. Platforms like Ada, Siena, and Yuma offer automation without requiring a full platform switch.
• Advanced AI features vary in price and availability. Some are bundled, while others charge per resolution or limit access to higher tiers.

Every delayed reply, missed ticket, or frustrated customer costs more than just satisfaction—it hits revenue, loyalty, and your brand reputation. That’s why more and more brands are investing in AI helpdesks to automate the tedious parts of their job.

But with so many options on the market, choosing the right AI helpdesk can feel overwhelming. Should you prioritize conversational AI? Multi-channel support? No-code customization? Or pricing that scales with your team?

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We’ve reviewed the 10 best AI helpdesks available in 2025, evaluating them across AI capabilities, ease of use, integrations, analytics, and pricing. 

How we evaluated the best AI helpdesks in 2025

To create this list, we evaluated each platform based on a combination of functionality, AI capabilities, usability, and industry applicability. 

Our goal was to provide a resource that CX leaders, ecommerce managers, and support teams can rely on when choosing a helpdesk that fits their business needs.

Here’s how we approached the evaluation:

1. Feature set assessment: Each tool was reviewed for its core helpdesk features, including ticket management, multi-channel support, workflow automation, and reporting capabilities.
2. AI sophistication: Platforms were evaluated on the depth of their AI offerings. This included natural language processing (NLP), predictive analytics, proactive messaging, and automated resolution capabilities.
3. Ease of use and setup: We considered setup time, onboarding complexity, and the learning curve for both agents and admins.
4. Industry applicability: We examined which industries each tool serves best. Some platforms are tailored for ecommerce, while others are more enterprise or service-focused.
5. Pricing transparency and scalability: We noted starting costs, AI feature availability by tier, and potential scaling considerations. Affordability and scalability were important, particularly for fast-growing teams that need to balance cost with AI functionality.
6. Supporting resources: We reviewed customer support, integrations, documentation, and community resources. A strong helpdesk not only provides AI features but also ensures teams can implement and optimize them effectively.

By following this methodology, we created a balanced, objective view of each helpdesk, highlighting what makes them unique, their strengths, limitations, and who will benefit most from them.

The best AI helpdesks of 2025

Gorgias

Gorgias is an AI helpdesk designed for ecommerce brands, helping teams streamline support while boosting both efficiency and personalization.

By unifying all customer touchpoints—email, chat, social media, voice, and SMS—into a single dashboard, Gorgias allows support teams to manage interactions without toggling between platforms.

Unlike most helpdesks, its AI capabilities go beyond basic automation. In addition to support, its AI can influence sales by assisting, recommending, and upselling to customers based on their shopping behavior.

Best for: Scaling startups and mature ecommerce enterprises looking to expand support capacity without increasing headcount

Potential limitations: Gorgias is focused primarily on ecommerce brands, which means it may be less suitable for companies that don’t use ecommerce platforms.

Pricing: Starts at $10/month, with advanced AI features available as an add-on.

Main features:

• Automated ticket routing: AI triages incoming customer queries and assigns them to the right agent.
• AI-generated responses: Provides instant, context-aware replies to common questions.
• Sentiment analysis: Flags frustrated customers to prioritize urgent tickets.
• Multi-channel AI support: Integrates across email, chat, Shopify, social media, and 100+ ecommerce apps.
• Macros and workflow automation: AI suggests relevant responses and automates repetitive tasks.

AI features:

• AI Agent: Conversational AI that can update, refund, and replace orders, cancel/skip subscriptions, and even carry out custom-made actions.
• Shopping Assistant: A proactive AI tool that guides, upsells, and recommends products to shoppers through chat. It helps CX teams increase sales and AOV.
• Auto QA: Upgrades service quality by automatically evaluating 100% of private text conversations, whether handled by a human or AI. Each message is scored on metrics like Resolution Completeness, Brand Voice, and Accuracy.

Zendesk

Zendesk is a widely adopted AI helpdesk solution that caters to teams of all sizes, from small businesses to large enterprises. It’s known for its robust ticketing system, extensive integrations, and customizable workflows, making it a versatile choice for teams across industries.

Best for: Non-ecommerce enterprises and businesses like airlines and banks

Potential limitations: Advanced AI features and enterprise-level plans can be expensive for smaller teams, and some users report that customization for niche workflows can be time-consuming.

Pricing: Starts at $25/month per agent, with advanced AI features and enterprise options available on higher tiers.

Main features:

• Unified ticketing: Centralizes requests from email, chat, phone, social media, and messaging apps.
• Macros and workflow automation: Automates routine responses and processes to reduce agent workload.
• Advanced analytics: Offers real-time dashboards and reporting to track support performance and customer satisfaction.
• Multi-channel support: Integrates seamlessly with major ecommerce, CRM, and communication platforms.

AI features:

• Copilot: Assists support agents in providing consistent replies, suggests next steps, and can even perform actions on agents’ behalf.
• AI triage: Automatically categorizes tickets and routes them to the appropriate team member.
• Zendesk QA: Scores the quality of interactions to help you get an overview of support performance.

Intercom

Intercom combines live chat, messaging, and AI automation into a single platform that focuses on proactive customer engagement. Its conversational AI makes it easy for teams to interact with customers in real time, while its automation tools help reduce response times and increase efficiency. 

Best for: SaaS companies, software companies, and mid-market teams

Potential limitations: Companies looking for a plug-and-play AI solution will need to invest time in setting up Intercom. Customers report a steep learning curve when creating workflows, organizing users, and implementing new automations.

Pricing: Starts at $39/month per seat. Fin AI is available as a standalone product for $0.99 per resolution (50 resolutions per month minimum) if you have an existing helpdesk.

Main features:

• Live chat and messaging: Provides instant support via website, mobile apps, and email.
• Inbox and workflow management: Centralizes customer conversations and automates repetitive tasks.
• Customer segmentation: Enables targeted messaging based on behavior, subscription plans, or engagement levels.

AI features:

• Fin AI: Intercom’s AI assistant responds to common questions, freeing agents to handle complex issues.
• Fin Tasks: Performs actions like retrieving order details, processing refunds, reorders, and more.
• Fin Insights: Provides a deep look into recurring trends and issues across conversations.

Gladly

Gladly is a customer service platform built around the concept of conversation-centric support, treating every customer interaction as a continuous dialogue rather than isolated tickets. 

Best for: Customer-focused brands that prioritize personalized, ongoing conversations over transactional support—especially retail, financial services, and subscription businesses that want to strengthen loyalty.

Potential limitations: Smaller teams may find it more than they need, and advanced customization can require professional services.

Pricing: Available on request, with plans typically tailored to enterprise support teams and scaled based on users and features.

Main features:

• Unified customer timeline: Combines all interactions—email, chat, social, SMS—into a single, chronological view.
• Personalized workflows: Tailors automation and routing to individual customer needs.
• Team collaboration tools: Enables seamless handoffs and internal notes for faster issue resolution.

AI features:

• Gladly Hero: Customer profiles created from conversations that include preferences, relationships, and purchase history.
• Sidekick Chat: Instant answers to requests like returns, account updates, and refunds.
• Sidekick Voice: Real-time, AI-powered phone support with SMS follow-ups.

Kustomer

Kustomer is a CRM-centric AI helpdesk that integrates customer support and relationship management in one platform. Its AI capabilities allow teams to automate repetitive tasks, route tickets intelligently, and gain insights into customer history, making it ideal for businesses with complex support workflows.

Best for: Mid-to-large enterprises that prioritize powerful, custom reporting

Potential limitations: Users report an unintuitive and laggy interface, which can slow down large support teams that handle high support volumes.

Pricing: Starts at $89/month per seat, with AI features available as add-ons.

Main features:

• Unified customer profiles: Consolidates all interactions, purchases, and support history in one view.
• Workflow automation: Streamlines processes with rules-based ticket routing and escalation.
• Advanced reporting: Tracks key support metrics and agent performance.

AI features:

• AI Agents for Reps: Offers real-time assistance, from drafting responses to updating records and summarizing conversations.
• AI Agent for Customers: Allows the creation of multiple AI Agents for specialized tasks.

Tidio

Tidio is an AI-powered live chat and messaging platform built for small to mid-sized businesses looking to combine automation with personalized support. Its ease of setup and affordability make it a strong choice for teams new to AI helpdesks.

Best for: Small to mid-sized ecommerce or service-based businesses looking for an easy-to-use AI chat solution to automate FAQs

Potential limitations: May not scale well for large enterprise businesses. 

Pricing: A free plan is available, with paid plans starting at $29/month per agent and AI features as add-ons.

Main features:

• Live chat and messenger integration: Supports website chat, email, and social messaging.
• Drag-and-drop chatbot builder: No coding required to deploy automated responses.
• Ticket management: Organizes queries for quick resolution by agents.

AI features:

• Lyro AI Agent: Conversational AI that answers questions based on support content.

Freshdesk

Freshdesk is a helpdesk platform that combines AI automation, omnichannel support, and workflow management. It’s known for ease of use and affordability, making it popular among SMBs and mid-market companies.

Best for: SMBs and mid-market companies looking for an affordable, easy-to-implement AI helpdesk

Potential limitations: Some advanced AI functionality is limited to higher-tier plans. Large enterprises may require additional configuration to fully leverage AI features.

Pricing: Plans start at $18/month per agent, with AI capabilities and advanced automation available on higher tiers.

Main features:

• Multi-channel ticketing: Consolidates email, chat, phone, and social support.
• Automation and workflows: Rules and macros automate repetitive tasks.
• Analytics and reporting: Provides insights into performance and customer satisfaction.

AI features:

• Freddy AI: Fetches order details, resolves questions, updates customer profiles, and more using approved data.

Standalone AI tools you can integrate with existing helpdesks

Not ready to move helpdesks? These standalone AI tools plug into your existing helpdesk to add automation, self-service, and conversational support.

Ada

Ada is focused on conversational automation, enabling teams to provide self-service solutions that reduce ticket volume while improving response times. 

Its no-code interface makes it accessible for non-technical teams, and its AI capabilities allow for personalized customer interactions at scale.

Best for: Large enterprise businesses looking to reduce support tickets through chat-based support

Potential limitations: Basic features that are free on competitor platforms cost extra on Ada, which limits smaller businesses looking for an all-in-one solution.

Pricing: Starts at $499/month for essential AI features. Higher-tier plans are available on request.

Main features:

• No-code chatbot builder: Quickly design and deploy AI chatbots across web, mobile, and messaging apps.
• Ticket deflection: Automates repetitive queries to reduce human agent workload.
• Multi-language support: Offers conversational support in multiple languages to serve global audiences.

AI features:

• Ada Voice: AI-powered phone support that can respond to customers, take action, and escalate issues in real-time.  
• Ada Email: Instant, personalized replies for email threads with the ability to hand off to agents.

Siena

Siena is focused on providing automated support for rapidly growing ecommerce and SaaS brands. With an emphasis on efficiency and self-service, Siena helps teams reduce ticket volume and respond faster, while giving managers visibility into performance metrics.

Best for: Mid-market ecommerce and SaaS companies that want to combine automation with insights

Potential limitations: Lacks clear visibility into AI performance, which can keep support teams in the dark about support performance and customer satisfaction.

Pricing: Starts at $500/month with automated tickets at $0.90 each. 

Main features:

• Omnichannel support: Handles email, chat, and social media from a single dashboard.
• Custom workflows: Automates repetitive tasks and ticket routing based on rules and customer data.
• Reporting and analytics: Tracks support KPIs and team performance in real time.

AI features:

• Customer Service Agent: Provides contextual, automated responses for common queries.
• Reviews Agent: Responds to every customer review with personalized feedback.
• Siena Memory: Stores key details from customer interactions and turns them into insights reports.

Yuma 

Yuma is focused on conversational automation and self-service solutions. It is designed to reduce agent workload while providing fast, personalized responses, making it appealing to growing ecommerce teams.

Best for: Established ecommerce brands looking to integrate sophisticated conversational AI alongside their current helpdesk

Potential limitations: Limited integrations with broader sales stacks mean brands prioritizing sales will have a hard time creating a smooth workflow.

Pricing: Starts at $350/month for 500 resolutions, with higher-tier plans for more resolutions.

Main features:

• Omnichannel support: Handles chat, email, and social messaging from one platform.
• Self-service portals: Allows customers to resolve common issues independently.
• Workflow automation: AI assists with repetitive tasks and ticket routing.

AI features:

• Support AI: Replies to customers on email, WhatsApp, SMS, and social media using your brand’s voice.
• Social AI: Instant responses to social media comments, DMs, and tags. ‍
• Sales AI: Guides shoppers to the relevant product and tracks bestsellers.

What features to look for in a good AI helpdesk

The best AI helpdesk makes support efficient, personalized, and scalable. 

Here’s a quick checklist of what to look for when evaluating an AI helpdesk:

• Smart ticket management
• Self-service workflows
• Multi-channel support
• Sales and upselling capabilities
• User-friendly AI controls
• Performance insights
• AI learning and improvement

Key takeaways from our review

The future of customer support is AI-driven, and the tools you choose today will define the efficiency, responsiveness, and satisfaction of your support team tomorrow. 

If it's still early in your AI helpdesk journey, we have additional resources to help you learn more from the pros before getting started:

• How CX Leaders are Actually Using AI: 6 Must-Know Lessons
• You’re Missing Out on Sales Without an AI Shopping Assistant—Here’s Why
• Why Consolidated Doesn’t Mean Compromised: Top 3 Myths Debunked

TL;DR:

• Most brands underuse their support tools: Gorgias has powerful features, but many teams don't take full advantage of them.
• Atidiv’s CX experts unlock Gorgias’s full potential: From tagging and macros to dashboards and rules, Atidiv ensures every feature drives value.
• Smart tagging creates strategic insights: Agents tag every interaction to surface product feedback, customer sentiment, and emerging trends in real time.
• Macros and Rules streamline support: Atidiv builds brand-consistent Macros and uses Rules to reduce manual work and clutter.

You don’t need more software—just better usage: Atidiv transforms existing tools like Gorgias into engines for efficiency, growth, and retention.

If you’re like most ecommerce brands, you’ve invested in great tools like Gorgias to streamline support, automate workflows, and deliver personalized experiences at scale. But here’s the hard truth: Having the tools doesn’t mean you’re using them well.

We see it all the time. Gorgias is live, Macros are written, a few Rules are set, and then… chaos. Tags go unused, dashboards lack insight, and your agents are still drowning in tickets.

That’s why leading brands aren’t just buying tech, they’re partnering with teams who know how to use it. That’s where Atidiv comes in.

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The tools are there. Most teams just don’t maximize them.

Gorgias is a powerful platform. Out of the box, it gives you:

• Custom tagging and views
• Automation rules to speed up repetitive tasks
• Macros that standardize your brand voice
• Real-time dashboards and revenue attribution

But without the right people using these tools effectively, it’s just noise. Atidiv’s CX specialists are trained Gorgias power users, and they make sure every feature works hard for your brand.

What happens when CX teams know the tool inside out

Here’s how Atidiv leverages Gorgias to drive real results:

Smart tagging for strategic insights

Atidiv agents don’t just respond to tickets, they tag every interaction with purpose.

• Common product issues? Tagged.
• Pre-sale objections? Tagged.
• VIP customers? You bet—tagged.

This turns your inbox into a live dashboard of customer sentiment, product feedback, and emerging trends, no extra software required.

Macros that actually get used

Atidiv writes and maintains Macros that go beyond “Thanks for reaching out.”

• Dynamic responses tailored to each issue
• Integrated links to help center articles or policies
• Embedded personalization that keeps your brand voice consistent

These aren’t just canned replies—they’re crafted CX responses built to scale.

Enhance your Macros with tags, snooze rules, Shopify actions, and other dynamic variables.

Dashboards that drive decisions

Every Atidiv client gets a customized Gorgias dashboard. It’s built by Atidiv’s Team Leads to track what matters:

• CSAT trends
• SLA performance
• Volume by tag or channel
• Revenue generated from support

No more wondering if your support is working, now you know.

Rules that eliminate repetition

We use Gorgias Rules to route tickets, send auto-replies, and tag intents, reducing ticket clutter by up to 30%.

The result? Agents spend more time on high-impact conversations and less time chasing tracking numbers.

Run your support on autopilot with Gorgias Rules that automatically trigger based on your chosen conditions.

A real-world example: What this looks like in practice

A fast-growing superfood brand came to Atidiv with Gorgias already live, but underutilized. They were answering tickets manually, tracking performance in spreadsheets, and dealing with repeat questions daily.

Within 30 days, Atidiv helped them:

• Build >10 custom macros
• Implement >5 auto-routing and tagging rules
• Clean up and standardize 50+ tags
• Created 15+ executive views
• Launch a real-time performance dashboard
• Reduce first response time by approximately 45%
• Retention analysis using tags
• Surface batch of products with bad taste based on tag trends

And no, they didn’t need to buy any new tools.

It’s not about more tech, it’s about more leverage

Most brands think their next CX win will come from another app or integration. But the real unlock often comes from better use of what they already have.

That’s what Atidiv offers:

• CX teams that are fluent in Gorgias
• Leadership layers that manage performance and QA
• Strategic use of features you’re already paying for
• Flexibility to scale up or down without hiring overhead

The bottom line

You don’t need to overhaul your tech stack. You need a team that can turn Gorgias into a strategic engine for support, growth, and insight.

Atidiv makes it possible, with trained agents, experienced leaders, and a deep understanding of what Gorgias can do when used to its full potential.

→ Want to get more out of the tools you already have? Let’s talk about how Atidiv + Gorgias can transform your support operation.