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.

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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.

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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.

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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.

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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:

‍

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

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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.

‍

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.

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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.

‍

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

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?

{{lead-magnet-1}}

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.

TL;DR:

• Train your AI like a new hire. Give it tone guidelines, review weekly, and keep refining to stay on-brand.
• Adapt AI to real customer behavior. Adjust tone and timing to improve satisfaction, even if the answer stays the same.
• Use AI to drive sales, not just support. Top brands use it to answer product questions and guide pre-purchase decisions.
• Start small and improve as you go. Begin with one common question and test often to build momentum.

If you’ve been side-eyeing AI and wondering if it’s just hype, you’re not alone. A lot of CX leaders were skeptical, too:

“I used to be the loudest skeptic,” said Amber van den Berg, Head of CX at Wildride. “I was worried it would feel cold and robotic, completely disconnected from the warm, personal vibe we’d worked so hard to build.”

But fast forward to today, and teams at Wildride, OLIPOP, bareMinerals, and Love Wellness are using AI to do more than just deflect tickets. They’re…

• Cutting costs without cutting corners
• Driving revenue before a customer even checks out
• Delivering fast, on-brand, human-feeling support at scale

Here are six lessons you can steal from the brands doing it best.

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1. Think of AI as your sidekick

We need to get one point across clearly: AI isn’t about replacing your support team.

For brands with lean CX teams, burnout is a serious problem. And it’s one of the biggest reasons AI adoption is accelerating.

“I was constantly seeing the same frustrating inquiries—sponsorship asks, bachelorette party freebies, PR requests… 45% of our tickets were these kinds of messages,” said Nancy Sayo, Director of Consumer Services at global beauty brand, bareMinerals.

“Once I realized AI could handle them with kindness and consistency without pulling in my team, I was sold.”

Instead of thinking of AI as a replacement, think of it as an enhancement. 

It’s about making sure your CX team doesn’t burn out answering the same five questions 50 times a day.

With Gorgias AI Agent, Nancy’s team now uses automation to absorb the high-volume, low-conversion noise, freeing up their seasoned agents to focus on real revenue-driving moments.

“We use AI to handle low-complexity tickets. And we route higher-value customers to our human sales team—people who’ve been doing makeup for over a decade and really know what they’re doing.”

TL;DR? The smartest teams use AI to take the weight of repetitive tickets (“Do you ship internationally?” “Can I get free samples?”) off their shoulders so agents can focus on conversations that build trust, drive loyalty, and increase LTV.

2. Train your AI like a team member

While you can get started with AI quickly for simple queries, we don't recommend using it “out of the box.” And honestly, that’s a good thing.

Brands that “set it and forget it” are missing the point. Because if you want AI to sound exactly like your brand—not like every other chatbot on the internet—you need to give it the same context you’d give a new hire.

Amber van den Berg, Head of Customer Experience at baby carrier brand Wildride, wrote out detailed tone guidelines, including:

• Dos and don’ts for customer conversations
• Approved Dutch-to-English translations
• Example replies for nuanced, emotional questions
• Pre-written macros for product recs and delivery issues

“Lisa, our AI agent, is basically a super well-trained intern who never sleeps. I give her the same updates I give my human team, and I review Lisa’s conversations every week,” said Amber. “If something feels off-brand, too robotic, or just not Wildride enough, I tweak it.”

The feedback never stops, and that’s what makes Lisa so effective.

Related: Meet Auto QA: Quality checks are here to stay

3. Let AI mirror the pacing of real conversations

Even when AI gets it right, customers might not always feel like it did. Especially if the tone of voice is off or if your customer base just isn’t used to automation.

“Our CSAT was low at first,” said Nancy Sayo of bareMinerals. “Even if the response was accurate and beautifully written, our older customers just didn’t want to interact with AI.”

So Nancy’s team adapted. Rather than giving customers a blunt “no” to product requests, they restructured the flow:

“If someone asked for free product, we’d say, ‘We’ll send this to the team and follow up.’ Then, 3-5 days later, the AI would close the loop. It softened the blow and made customers feel heard—even if the answer didn’t change.”

That simple tweak raised CSAT and created a better customer experience without requiring a human to step in.

Inside Gorgias, teams like bareMinerals review AI performance weekly, not just to catch mistakes, but to optimize for tone, satisfaction, and brand feel. They use:

• CSAT reporting to spot dips in sentiment
• Conversation analytics to flag where AI may be losing trust
• Macro editing to quickly adjust common replies

AI gives you the flexibility to test, tweak, and tailor your approach in a way traditional support channels never could. 

4. Use AI to drive sales—not just support

Too many CX teams still treat AI like a glorified autoresponder. But the most forward-thinking brands are using it to guide shoppers to checkout.

“Our customers often ask: ‘Which carrier is better for warm weather?’ or ‘Will this fit both me and my taller partner?’” said Amber van den Berg, Head of CX at Wildride. “Lisa doesn’t just answer—she gives context, recommends features, and highlights small touches like the fact that a diaper fits in the side pocket.”

With Gorgias Shopping Assistant, brands can turn AI into a proactive sales assistant—answering product questions in real time, referencing what’s in the customer’s cart, and nudging them toward the best option with empathy.

5. CX insights should power the rest of your business

Great support doesn’t stop at the inbox. At Love Wellness, CX is the connective tissue between ecommerce, product, and marketing.

“We meet quarterly with our CX and ecommerce teams to review top questions, objections, and patterns,” said Mckay Elliot, Director of Amazon at Love Wellness. “That feedback goes straight into product development and PDP optimizations on both DTC and Amazon.”

But it’s not just a quarterly ritual. Feedback sharing is embedded in the culture, and they do this with a Slack channel dedicated to customer feedback. 

Dropping in insights is part of the team’s daily and weekly responsibilities. It helps everyone stay close to the content, and it sparks real collaboration on what we can improve. They then use those insights to improve ad messaging and content.

Your team has so much data they can review between channels like email, SMS, chat, and social media—both compliments and complaints. You need to be willing to listen to every customer’s needs.

Read more: Why customer service is important (according to a VP of CX)

6. Don’t overthink it, start small

One of the biggest mistakes brands make with AI? Trying to do too much, too soon.

Rolling out AI should feel like a phased launch, not a switch flip. The best results come from starting simple, testing often, and iterating as you go.

“We started with one simple question—‘Do you ship internationally?’—and built from there,” said Amber van den Berg of Wildride.

“And if it doesn’t work? You can always turn it off,” added Anne Dyer, Sr. Manager of CX & Loyalty Marketing at OLIPOP. “The key is to test, review, and keep iterating. AI should enhance your human experience, not replace it.”

If your helpdesk supports it, start in a test environment to preview answers before going live. Then roll out automation gradually by channel, topic, or ticket type and QA every step of the way.

For most brands, the best starting point is high-volume, low-complexity tickets like:

• “Where’s my order?”
• Subscription pauses or cancellations
• Returns and exchanges
• Store policies and FAQs

You don’t need to solve everything on day 1. Just commit to one question, one channel, and one hour per week. That’s where real momentum starts.

Related: Store policies by industry, explained: What to include for every vertical

How do you measure the impact of AI in CX?

Most CX teams are used to tracking classic metrics like ticket volume and CSAT. But when AI enters the mix, your definition of success shifts. It’s not all about how fast you handle tickets anymore—it’s about how customers feel after conversations with AI, team efficiency, and the quality of every interaction.

Here are the metric CX teams used to track without AI—and what they track now with AI:

The best use of AI makes space for human touch

AI isn’t here to replace your CX team. It’s here to free them up, so they can focus on deeper, more meaningful conversations that build loyalty and drive revenue.

So if you’re on the fence, start small. Train it. Review weekly. Build the muscle.

You’ll be surprised how quickly AI becomes your favorite intern.

If you want more tips from the experts featured today, you can:

• Watch the full webinar
• Get the AI in Action ebook