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:

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

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

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Celery

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

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

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‍

Some tips

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

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Conclusion

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

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

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

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

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Share templates inside the extension

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

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

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

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New navigation

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

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

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

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

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

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

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

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

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

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Romain & Alex

We've been busy, but not deaf!

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

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

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We listened and now we're presenting:

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

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

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

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

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

TL;DR:

• Most food & beverage support tickets during BFCM are predictable. Subscription cancellations, WISMO, and product questions make up the bulk—so prep answers ahead of time.
• Proactive CX site updates can drastically cut down repetitive tickets. Add ingredient lists, cooking instructions, and clear refund policies to product pages and FAQs.
• FAQ pages should go deep, not just broad. Answer hyper-specific questions like “Will this break my fast?” to help customers self-serve without hesitation.‍
• Transparency about stock reduces confusion and cart abandonment. Show inventory levels, set up waitlists, and clearly state cancellation windows.

In 2024, Shopify merchants drove $11.5 billion in sales over Black Friday Cyber Monday. Now, BFCM is quickly approaching, with some brands and major retailers already hosting sales.

If you’re feeling late to prepare for the season or want to maximize the number of sales you’ll make, we’ll cover how food and beverage CX teams can serve up better self-serve resources for this year’s BFCM. 

Learn how to answer and deflect customers’ top questions before they’re escalated to your support team.

💡 Your guide to everything peak season → The Gorgias BFCM Hub

Handling BFCM as a food & beverage brand

During busy seasons like BFCM and beyond, staying on top of routine customer asks can be an extreme challenge. 

“Every founder thinks BFCM is the highest peak feeling of nervousness,” says Ron Shah, CEO and Co-founder of supplement brand Obvi. 

“It’s a tough week. So anything that makes our team’s life easier instantly means we can focus more on things that need the time,” he continues. 

Anticipating contact reasons and preparing methods (like automated responses, macros, and enabling an AI Agent) is something that can help. Below, find the top contact reasons for food and beverage companies in 2025. 

Top contact reasons in the food & beverage industry 

According to Gorgias proprietary data, the top reason customers reach out to brands in the food and beverage industry is to cancel a subscription (13%) followed by order status questions (9.1%).

7 ways to improve your self-serve resources before BFCM

1. Add informative blurbs on product pages 
2. Craft additional help center and FAQ articles 
3. Automate responses with AI or Macros 
4. Get specific about product availability
5. Provide order cancellation and refund policies upfront
6. Add how-to information
7. Build resources to help with buying decisions 

1) Add informative blurbs on product pages

Because product detail queries represent 5.7% of contact reasons for the food and beverage industry, the more information you provide on your product pages, the better. 

Include things like calorie content, nutritional information, and all ingredients.  

For example, ready-to-heat meal company The Dinner Ladies includes a dropdown menu on each product page for further reading. Categories include serving instructions, a full ingredient list, allergens, nutritional information, and even a handy “size guide” that shows how many people the meal serves. 

2) Craft additional Help Center and FAQ articles

FAQ pages make up the information hub of your website. They exist to provide customers with a way to get their questions answered without reaching out to you.   

This includes information like how food should be stored, how long its shelf life is, delivery range, and serving instructions. FAQs can even direct customers toward finding out where their order is and what its status is. 

In the context of BFCM, FAQs are all about deflecting repetitive questions away from your team and assisting shoppers in finding what they need faster. 

That’s the strategy for German supplement brand mybacs. 

“Our focus is to improve automations to make it easier for customers to self-handle their requests. This goes hand in hand with making our FAQs more comprehensive to give customers all the information they need,” says Alexander Grassmann, its Co-Founder & COO.

As you contemplate what to add to your FAQ page, remember that more information is usually better. That’s the approach Everyday Dose takes, answering even hyper-specific questions like, “Will it break my fast?” or “Do I have to use milk?”

While the FAQs you choose to add will be specific to your products, peruse the top-notch food and bev FAQ pages below. 

Time for some FAQ inspo:

• Juice Plus+ FAQ
• Everyday Dose FAQ
• Chamberlain Coffee FAQ
• Jurassic Fruit FAQ
• Rebel Cheese FAQ
• The Dinner Ladies FAQ

3) Automate responses with AI or macros

AI Agents and AI-powered Shopping Assistants are easy to set up and are extremely effective in handling customer interactions––especially during BFCM.  

“I told our team we were going to onboard Automate for BFCM, so a good portion of tickets would be handled automatically,” says Ron Shah, CEO and Co-founder at Obvi. “There was a huge sigh of relief knowing that customers were going to be taken care of.” 

And, they’re getting smarter. Gorgias AI Agent’s CSAT is just 0.6 points shy of human agents’ average CSAT score. 

Here are the specific responses and use cases we recommend automating: 

• WISMO (where is my order) inquiries 
• Product related questions 
• Returns 
• Order issues
• Cancellations 
• Discounts, including BFCM related 
• Customer feedback
• Account management
• Collaboration requests 
• Rerouting complex queries

Get your checklist here: How to prep for peak season: BFCM automation checklist

4) Get specific about product availability

With high price reductions often comes faster-than-usual sell out times. By offering transparency around item quantities, you can avoid frustrated or upset customers. 

For example, you could show how many items are left under a certain threshold (e.g. “Only 10 items left”), or, like Rebel Cheese does, mention whether items have sold out in the past.  

You could also set up presales, give people the option to add themselves to a waitlist, and provide early access to VIP shoppers. 

5) Provide order cancellation and refund policies upfront 

Give shoppers a heads up whether they’ll be able to cancel an order once placed, and what your refund policies are. 

For example, cookware brand Misen follows its order confirmation email with a “change or cancel within one hour” email that provides a handy link to do so. 

Your refund policies and order cancellations should live within an FAQ and in the footer of your website. 

6) Add how-to information 

Include how-to information on your website within your FAQs, on your blog, or as a standalone webpage. That might be sharing how to use a product, how to cook with it, or how to prepare it. This can prevent customers from asking questions like, “how do you use this?” or “how do I cook this?” or “what can I use this with?” etc. 

For example, Purity Coffee created a full brewing guide with illustrations:

Similarly, for its unique preseasoned carbon steel pan, Misen lists out care instructions: 

And for those who want to understand the level of prep and cooking time involved, The Dinner Ladies feature cooking instructions on each product page. 

7) Build resources to help with buying decisions 

Interactive quizzes, buying guides, and gift guides can help ensure shoppers choose the right items for them––without contacting you first. 

For example, Trade Coffee Co created a quiz to help first timers find their perfect coffee match: 

Set your team up for BFCM success with Gorgias 

The more information you can share with customers upfront, the better. That will leave your team time to tackle the heady stuff. 

If you’re looking for an AI-assist this season, check out Gorgias’s suite of products like AI Agent and Shopping Assistant. 

{{lead-magnet-2}}

TL;DR:

• Conversational AI combines natural language processing, machine learning, and generative AI to create human-like interactions
• For ecommerce, it automates customer service, drives sales through personalized recommendations, and scales support 24/7
• Key types include chatbots, voice assistants, and AI agents that handle both support and sales tasks
• Implementation requires defining clear goals, choosing an ecommerce-ready platform, and connecting your tech stack

Conversational AI transforms how ecommerce brands interact with customers by enabling natural, human-like conversations at scale, helping reduce customer churn. Instead of forcing shoppers through rigid menus or making them wait for support, conversational AI understands questions, detects intent, and delivers instant, personalized responses. This technology powers everything from customer service chatbots to voice assistants, helping brands automate repetitive tasks while maintaining the personal touch customers expect. For ecommerce specifically, it means handling order inquiries, providing product recommendations, and recovering abandoned carts — all without adding headcount.

What is conversational AI?

Conversational AI is a type of artificial intelligence that allows computers to understand, process, and respond to human language through natural, two-way conversations. This means your customers can ask questions in their own words and get helpful answers that feel like they're talking to a real person.

Unlike basic chatbots that only recognize specific keywords, conversational AI actually understands what your customers mean. It can handle typos, slang, and complex questions that have multiple parts. The AI learns from every conversation, getting better at helping your customers over time.

Think of it as having a super-smart team member who never sleeps, never gets frustrated, and remembers every detail about your products and policies. This AI team member can chat with customers on your website, answer questions through social media, or even handle phone calls.

What are the key components of conversational AI?

Conversational AI works because several smart technologies team up to understand and respond to your customers. Each piece has a specific job in making conversations feel natural and helpful.

Natural Language Processing (NLP) is the foundation that breaks down human language into pieces a computer can understand. This means when a customer types "Where's my order?" the AI can identify the important words and grammar structure.

Natural Language Understanding (NLU) figures out what the customer actually wants. This is the smart part that realizes "Where's my order?" means the customer wants to track a shipment, even if they phrase it differently like "I need to check my package status."

Natural Language Generation (NLG) creates responses that sound human and helpful. Instead of robotic answers, it crafts replies that match your brand's voice and provide exactly what the customer needs to know.

The dialog manager keeps track of the entire conversation. This means if a customer asks a follow-up question, the AI remembers what you were just talking about and can give a relevant answer.

Your knowledge base stores all the information the AI needs to help customers. This includes your return policy, product details, shipping information, and any other facts your team would use to answer questions.

How does conversational AI work?

Conversational AI follows a simple three-step process that happens in seconds. Understanding this process helps you see why it's so much more powerful than old-school chatbots.

It processes input across voice and text

When a customer sends a message or asks a question, the AI first needs to understand what they're saying. For text messages from chat, email, or social media, the system breaks down the sentence into individual words and analyzes the grammar.

For voice interactions like phone calls, the AI uses speech recognition to turn spoken words into text first. Modern systems handle different accents, background noise, and natural speech patterns without missing a beat.

It detects intent and context with NLU

Once the AI has the customer's words, it needs to figure out what they actually want. The system looks for the customer's intent — their goal or what they're trying to accomplish.

For example, when someone asks "Can I return this sweater I bought last week?" the AI identifies the intent as wanting to make a return. It also pulls out important details like the product type and timeframe.

The AI also uses context from earlier in the conversation. If the customer mentioned their order number earlier, the AI remembers it and can use that information to help with the return request.

It generates responses with NLG

After understanding what the customer wants, the AI creates a helpful response. It might pull information from your knowledge base, personalize the answer with the customer's specific details, or generate a completely new response using generative AI.

The system also checks how confident it is in its answer. If the AI isn't sure about something or if the topic is too complex, it knows to hand the conversation over to one of your human agents.

What are the types of conversational AI?

Different types of conversational AI work better for different situations in your ecommerce business. Understanding these types helps you choose the right solution for your customers and team.

Chatbots handle scripted and AI-driven chat

Chatbots are the most common type you'll see on websites and messaging apps. Early chatbots followed strict scripts — if a customer's question didn't match the script exactly, the bot would get confused and give unhelpful answers.

Modern AI-powered chatbots understand natural language and can handle much more complex conversations. The best systems combine both approaches: using simple rules for straightforward questions and AI for everything else.

These chatbots work great for answering common questions about shipping, returns, and product details. They can also help customers find the right products or guide them through your checkout process.

Voice assistants manage speech-based requests

Voice assistants bring conversational AI to phone support and other voice channels. These aren't the old phone trees that made customers press numbers to navigate menus.

Instead, customers can speak naturally and get helpful answers right away. Voice assistants can look up order information, explain your return policy, or even process simple requests like address changes.

This works especially well for customers who prefer calling over typing, or when they need help while their hands are busy.

AI agents and copilots assist teams and customers

AI agents are the most advanced type of conversational AI. Unlike chatbots that mainly provide information, AI agents can actually take action on behalf of customers.

These systems connect to your other business tools like Shopify, your shipping software, or your returns platform. This means they can do things like:

• Process returns: Start a return and send the customer a shipping label
• Update orders: Change a shipping address or add items to an existing order
• Handle refunds: Issue refunds for eligible orders automatically
• Manage subscriptions: Skip shipments or update subscription preferences

Copilots work alongside your human agents, suggesting responses and pulling up customer information to help resolve issues faster.

What are the benefits of conversational AI for ecommerce?

Conversational AI delivers real business results for ecommerce brands. The benefits go beyond just making your support team more efficient — though that's certainly part of it.

24/7 availability means you never miss a sale or support opportunity. Customers can get help at 2 a.m. or during holidays when your team is offline. This is especially valuable for international customers in different time zones.

Instant responses prevent cart abandonment and customer frustration, improving first contact resolution. When someone has a question about sizing or shipping, they get an answer immediately instead of waiting hours or days for an email response.

Personalized interactions at scale drive higher average order values. The AI can recommend products based on what customers are browsing, their purchase history, and their preferences — just like your best salesperson would.

Cost efficiency comes from handling repetitive questions automatically. Your human agents can focus on complex issues, VIP customers, and revenue-generating activities instead of answering the same shipping questions over and over.

Multilingual support helps you serve global customers without hiring native speakers for every language. The AI can communicate in dozens of languages, opening up new markets for your business.

What are the most valuable conversational AI use cases for ecommerce?

Certain moments in the shopping experience create the biggest opportunities for conversational AI to drive results. Focus on these high-impact use cases first.

Pre-purchase questions are your biggest conversion opportunity. When someone is looking at a product but hasn't bought yet, quick answers about sizing, materials, or compatibility can close the sale. The AI can also suggest complementary products or highlight features the customer might have missed.

Order tracking makes up the largest volume of support tickets for most ecommerce brands. Customers want to know where their package is, when it will arrive, and what to do if there's a delay. AI handles these "Where Is My Order" questions instantly by pulling real-time tracking information.

Returns and exchanges can be complex, but AI excels at the initial screening. It can check if an item is eligible for return, explain your policy, and start the return process. For straightforward returns, customers never need to wait for human help.

Cart recovery works best when it's immediate and personal. AI can detect when someone abandons their cart and reach out through chat or email with personalized messages, discount offers, or answers to common concerns that prevent purchases.

Post-purchase support keeps customers happy after they buy. The AI can send order confirmations, provide care instructions, suggest related products, and handle simple issues like address changes.

How do you implement conversational AI in an ecommerce tech stack?

Getting started with conversational AI doesn't require a complete overhaul of your systems. The key is starting with clear goals and building your capabilities over time.

Step 1: Define goals and KPIs for automation

Start by analyzing your current support tickets to find the best automation opportunities. Look for questions that come up repeatedly and have straightforward answers. Common examples include order status, return policies, and basic product information.

Set realistic goals for your first phase. You might aim to automate 30% of your tickets or reduce average response time by half. Track metrics like:

• Automation rate: Percentage of tickets resolved without human intervention
• Customer satisfaction: How happy customers are with AI interactions
• Revenue impact: Sales influenced by AI recommendations or cart recovery

Step 2: Choose an ecommerce-ready AI platform

Not all conversational AI platforms understand ecommerce needs. Look for a platform that integrates directly with Shopify and your other business tools. This connection is essential for pulling real-time order data, customer history, and product information.

Your platform should come with pre-built actions for common ecommerce tasks like order lookups, return processing, and subscription management. This saves months of custom development work.

Make sure you can control the AI's behavior through clear guidance and rules. You need to be able to set your brand voice, define when to escalate to humans, and update the AI's knowledge as your business changes.

Step 3: Connect Shopify and key tools, then iterate

Start your implementation by connecting your Shopify store to give the AI access to order and customer data. Also integrate your key tools like shipping software, returns platforms, and loyalty programs.

Launch with a few core use cases like order tracking and basic product questions. Monitor the AI's performance closely and gather feedback from both customers and your support team.

Use this data to refine the AI's responses and gradually expand its capabilities. The best approach is iterative — start small, learn what works, and build from there.

What are the challenges and risks of conversational AI?

While conversational AI offers significant benefits, you need to be aware of potential challenges and plan for them from the start.

Accuracy concerns arise when AI systems provide incorrect information or "hallucinate" facts that aren't true. Prevent this by using platforms that ground responses in your verified knowledge base and product data rather than generating answers from scratch.

Brand voice consistency becomes critical when AI represents your brand to customers. Set clear guidelines for tone, style, and messaging. Test the AI's responses regularly to ensure they align with how your human team would handle similar situations.

Data privacy requires careful attention since conversational AI handles sensitive customer information. Choose platforms with strong security measures, data encryption, and compliance with regulations like GDPR. Look for features like automatic removal of personal information from conversation logs.

Over-automation can frustrate customers when complex issues require human empathy and problem-solving. Design clear escalation paths so customers can easily reach human agents when needed. Train your AI to recognize when a situation is beyond its capabilities.

Integration complexity can slow down implementation if your chosen platform doesn't work well with your existing tools. This is why choosing an ecommerce-focused platform with pre-built integrations is so important.

Turn conversations into revenue with conversational AI

Conversational AI transforms every customer interaction into an opportunity to build relationships and drive sales. The technology handles routine questions instantly while freeing your team to focus on complex issues and high-value customers.

The brands winning with conversational AI start with clear goals, choose the right platform, and iterate based on real performance data. They don't try to automate everything at once — they focus on high-impact use cases that deliver measurable results.

Ready to see how conversational AI can transform your ecommerce support and sales? Book a demo with Gorgias to explore AI Agent built specifically for ecommerce brands.

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TL;DR:

• You don’t need to rebuild your Help Center to make it work with AI—you just need to structure it smarter.
• AI Agent reads your content in three layers: Help Center, Guidance, and Actions, following an “if / when / then” logic to find and share accurate answers.
• Most AI escalations happen because Help Docs are vague or incomplete. Start by improving your top 10 ticket topics—like order status, returns, and refunds.
• Make your articles scannable, define clear conditions, link next steps, and keep your tone consistent. These small tweaks help AI Agent resolve more tickets on its own—and free up your team to focus on what matters most.

As holiday season support volumes spike and teams lean on AI to keep up, one frustration keeps surfacing, our Help Center has the answers—so why can’t AI find them?

The truth is, AI can’t help customers if it can’t understand your Help Center. Most large language models (LLMs), including Gorgias AI Agent, don’t ignore your existing docs, they just struggle to find clear, structured answers inside them.

The good news is you don’t need to rebuild your Help Center or overhaul your content. You simply need to format it in a way that’s easy for both people and AI to read.

We’ll break down how AI Agent reads your Help Center, finds answers, and why small formatting changes can help it respond faster and more accurately, so your team spends less time on escalations.

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How AI Agent uses your Help Center content

Before you start rewriting your Help Center, it helps to understand how AI Agent actually reads and uses it.

Think of it like a three-step process that mirrors how a trained support rep thinks through a ticket.

1. Read Help Center docs

Your Help Center is AI Agent’s brain. AI Agent uses your Help Center to pull facts, policies, and instructions it needs to respond to customers accurately. If your articles are clearly structured and easy to scan, AI Agent can find what it needs fast. If not, it hesitates or escalates.

2. Follow Guidance instructions

Think of Guidance as AI Agent’s decision layer. What should AI Agent do when someone asks for a refund? What about when they ask for a discount? Guidance helps AI Agent provide accurate answers or hand over to a human by following an “if/when/then” framework.

3. Respond and perform

Finally, AI Agent uses a combination of your help docs and Guidance to respond to customers, and if enabled, perform an Action on their behalf—whether that’s changing a shipping address or canceling an order altogether.

Here’s what that looks like in practice:

This structure removes guesswork for both your AI and your customers. The clearer your docs are about when something applies and what happens next, the more accurate and human your automated responses will feel.

A Help Center written for both people and AI Agent:

• Saves your team time
• Reduces escalations
• Helps every customer get the right answer the first time

What causes AI Agent to escalate tickets, and how to fix it

Our data shows that most AI escalations happen for a simple reason––your Help Center doesn’t clearly answer the question your customer is asking.

That’s not a failure of AI. It’s a content issue. When articles are vague, outdated, or missing key details, AI Agent can’t confidently respond, so it passes the ticket to a human.

Here are the top 10 topics that trigger escalations most often:

Each of these topics needs a dedicated, clearly structured Help Doc that uses keywords customers are likely to search and spells out specific conditions. 

Here’s how to strengthen each one:

• Order status: Include expected delivery timelines, tracking link FAQs, and a clear section for “what to do if tracking isn’t updating.”
• Return request: Spell out eligibility requirements, time limits, and how to print or request a return label.
• Order cancellation: Define cut-off times for canceling and link to your “returns” doc for shipped orders.
• Product quality issues: Explain what qualifies as a defect, how to submit photos, and whether replacements or refunds apply.
• Missing item: Clarify how to report missing items and what verification steps your team takes before reshipping.
• Subscription cancellation: Add “if/then” logic for different cases: if paused vs. canceled, if prepaid vs. monthly.
• Order refund: Outline refund timelines, where customers can see status updates, and any exceptions (e.g., partial refunds).
• Product details: Cover sizing, materials, compatibility, or FAQs that drive most product-related questions.
• Return status: State how long returns take to process and where to check progress once a label is scanned.
• Order delivered but not received: Provide step-by-step guidance for checking with neighbors, filing claims, or requesting replacements.

Start by improving these 10 articles first. Together, they account for nearly half of all AI Agent escalations. The clearer your Help Center is on these topics, the fewer tickets your team will ever see, and the faster your AI will resolve the rest.

How to format your Help Center docs for LLMs

Once you know how AI Agent reads your content, the next step is formatting your help docs so it can easily understand and use them. 

The goal isn’t to rewrite everything, it’s to make your articles more structured, scannable, and logic-friendly. 

Here’s how.

1. Use structured, scannable sections

Both humans and large language models read hierarchically. If your article runs together in one long block of text, key answers get buried.

Break articles into clear sections and subheadings (H2s, H3s) for each scenario or condition. Use short paragraphs, bullets, and numbered lists to keep things readable.

Example:

How to Track Your Order

• Step 1: Find your tracking number in your confirmation email.
• Step 2: Click the tracking link to see your delivery status.
• Step 3: If tracking hasn’t updated in 3 days, contact support.

A structured layout helps both AI and shoppers find the right step faster, without confusion or escalation.

2. Write for “if/when/then” logic

AI Agent learns best when your Help Docs clearly define what happens under specific conditions. Think of it like writing directions for a flowchart.

Example:

• “If your order hasn’t arrived within 10 days, contact support for a replacement.”
• “If your order has shipped, you can find the tracking link in your order confirmation email.”

This logic helps AI know what to do and how to explain the answer clearly to the customer.

3. Clarify similar terms and synonyms

Customers don’t always use the same words you do, and neither do LLMs. If your docs treat “cancel,” “stop,” and “pause” as interchangeable, AI Agent might return the wrong answer.

Define each term clearly in your Help Center and add small keyword variations (“cancel subscription,” “end plan,” “pause delivery”) so the AI can recognize related requests.

4. Link to next steps

AI Agent follows links just like a human agent. If your doc ends abruptly, it can’t guide the customer any further.

Always finish articles with an explicit next step, like linking to:

• A form
• Another article
• A support action page

Example: “If your return meets our policy, request your return label here.”

That extra step keeps the conversation moving and prevents unnecessary escalations.

5. Keep tone consistent

AI tools prioritize structure and wording when learning from your Help Center—not emotional tone. 

Phrases like “Don’t worry!” or “We’ve got you!” add noise without clarity.

Instead, use simple, action-driven sentences that tell the customer exactly what to do:

• “Click here to request a refund.”
• “Fill out the warranty form to get a replacement.”

A consistent tone keeps your Help Center professional, helps AI deliver reliable responses, and creates a smoother experience for customers.

LLM-friendly Help Centers in action

You don’t need hundreds of articles or complex workflows to make your Help Center AI-ready. But you do need clarity, structure, and consistency. These Gorgias customers show how it’s done.

Little Words Project: Simple formatting that boosts instant answers

Little Words Project keeps things refreshingly straightforward. Their Help Center uses short paragraphs, descriptive headers, and tightly scoped articles that focus on a single intent, like returns, shipping, or product care. 

That makes it easy for AI Agent to scan the page, pull out the right facts, and return accurate answers on the first try.

Their tone stays friendly and on-brand, but the structure is what shines. Every article flows from question → answer → next step. It’s a minimalist approach, and it works. Both for customers and the AI reading alongside them.

Dr. Bronner’s: Making tools work for the team

Customer education is at the heart of Dr. Bronner’s mission. Their customers often ask detailed questions about product ingredients, packaging, and certifications. With Gorgias, Emily and her team were able to build a robust Help Center that helped to proactively give this information.

The Help Center doesn't just provide information. The integration of interactive Flows, Order Management, and a Contact Form automation allowed Dr. Bronner’s to handle routine inquiries—such as order statuses—quickly and efficiently. These kinds of interactive elements are all possible out-of-the-box, no IT support needed.

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

Ekster: Building efficiency through automation and clarity

When Ekster switched to Gorgias, the team wanted to make their Help Center work smarter. By writing clear, structured articles for common questions like order tracking, returns, and product details, they gave both customers and AI Agent the information needed to resolve issues instantly.

"Our previous Help Center solution was the worst. I hated it. Then I saw Gorgias’s Help Center features, and how the Article Recommendations could answer shoppers’ questions instantly, and I loved it. I thought: this is just what we need." —Shauna Cleary, Head of Ecommerce at Ekster

The results followed fast. With well-organized Help Center content and automation built around it, Ekster was able to scale support without expanding the team.

“With all the automations we’ve set up in Gorgias, and because our team in Buenos Aires has ramped up, we didn’t have to rehire any extra agents.” —Shauna Cleary, Head of Ecommerce at Ekster

Learn more: How Ekster used automation to cover the workload of 4 agents 

Rowan: Clean structure that keeps customers (and AI) on track

Rowan’s Help Center is a great example of how clear structure can do the heavy lifting. Their FAQs are grouped into simple categories like piercing, shipping, returns, and aftercare, so readers and AI Agent can jump straight to the right topic without digging. 

For LLMs, that kind of consistency reduces guesswork. For customers, it creates a smooth, reassuring self-service experience. 

TUSHY: Balancing brand voice with automation

TUSHY proves you can maintain personality and structure. Their Help Center articles use clear headings, direct language, and brand-consistent tone. It makes it easy for AI Agent to give accurate, on-brand responses.

“Too often, a great interaction is diminished when a customer feels reduced to just another transaction. With AI, we let the tech handle the selling, unabashedly, if needed, so our future customers can ask anything, even the questions they might be too shy to bring up with a human. In the end, everybody wins!" —Ren Fuller-Wasserman, Senior Director of Customer Experience at TUSHY

Quick checklist to audit your Help Center for AI

Ready to put your Help Center to the test? Use this five-point checklist to make sure your content is easy for both customers and AI to navigate.

1. Are your articles scannable with clear headings?

Break up long text blocks and use descriptive headers (H2s, H3s) so readers and AI Agent can instantly find the right section.

2. Do you define conditions with “if/when/then” phrasing?

Spell out what happens in each scenario. This logic helps AI Agent decide the right next step without second-guessing.

3. Do you cover your top escalation topics?

Make sure your Help Center includes complete, structured articles for high-volume issues like order status, returns, and refunds.

4. Does each article end with a clear next step or link?

Close every piece with a call to action, like a form, related article, or support link, so neither AI nor customers hit a dead end.

5. Is your language simple, action-based, and consistent?

Use direct, predictable phrasing. Avoid filler like “Don’t worry!” and focus on steps customers can actually take.

By tweaking structure instead of your content, it’s easier to turn your Help Center into a self-service powerhouse for both customers and your AI Agent.

Make your Help Center work smarter

Your Help Center already holds the answers your customers need. Now it’s time to make sure AI can find them. A few small tweaks to structure and phrasing can turn your existing content into a powerful, AI-ready knowledge base.

If you’re not sure where to start, review your Help Center with your Gorgias rep or CX team. They can help you identify quick wins and show you how AI Agent pulls information from your articles.

Remember: AI Agent gets smarter with every structured doc you publish.

Ready to optimize your Help Center for faster, more accurate support? Book a demo today.

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