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.

‍

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

‍

Launch script which does 2 things:

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

‍

‍

#!/usr/bin/env bash

set -e

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

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

‍

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

‍

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

‍

Future to do:

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

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

‍

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

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

‍

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

‍

Docker structure

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

‍

Here's the hierarchy of our docker images:

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

‍

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

‍

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

‍

We chose Kubernetes too

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

‍

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

‍

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

‍

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

‍

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

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

‍

Why Kubernetes again?

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

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

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

‍

Postgres

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

‍

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

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

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

‍

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

‍

‍

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.

‍

‍

Conclusion

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

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

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

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

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Share templates inside the extension

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

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

‍

‍

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

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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.

‍

‍

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.

‍

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.

‍

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.

‍

Romain & Alex

We've been busy, but not deaf!

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

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

‍

We listened and now we're presenting:

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

‍

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

‍

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

‍

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

‍

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

TL;DR:

• AI Agent is getting more accurate every month: It’s improved 14.9% this year thanks to better LLMs, constant updates, and user feedback.
• It writes more correctly than most humans: With a 4.77/5 language score, it’s nailing grammar, tone, and clarity better than human agents.
• It’s empathetic, too: AI Agent now shows more empathy and listens better than human agents.
• Brands are gaining confidence fast: Quality scores jumped from 57% to 85% in just a few months, and CX teams are noticing.
• Customers are almost as happy with AI as with humans: AI Agent’s CSAT is just 0.6 points shy of human agents’ average CSAT.

Handing trust over to AI can be intimidating. One off-brand reply and you undo the reputation and customer loyalty you’ve worked so hard to build. 

That’s why we’ve made accuracy our top priority with Gorgias AI Agent.

For the past year, the Gorgias team has been hard at work fulfilling the pressing  demand for accuracy and speed. AI Agent is getting smarter, faster, and more reliable, and merchants and their customers are happier with the output. 

Here’s the data.

{{lead-magnet-1}}

AI Agent delivers more accurate answers than ever

This year, AI Agent’s accuracy rose from 3.55 to 4.08 out of 5, a 14.9% improvement from January. This average score is based on CX agents' ratings of AI Agent responses in the product, on a scale of 1 to 5.

In the past year, we’ve improved knowledge retrieval, added new integrations, expanded reporting features, and asked for more feedback in-product.

We saw the steadiest leap in July, right after the release of GPT-5. AI Agent began reaching levels of consistency and accuracy that agents could trust.

AI Agent writes with more linguistic precision than humans

Clear, easy-to-understand language helps people trust what they’re reading. Website Planet found that 85% more visitors bounced from a page when typos were present. That’s why we’ve made it a priority for AI Agent to respond to customers with correct grammar, syntax, and tone of voice. 

The efforts have paid off: AI Agent scores a high 4.77 out of 5 in language proficiency compared to 4.4 for human agents. The result is error-free messages that are easy to read and consistent with your brand vocabulary.

AI Agent shows that empathy can be scaled

Accuracy isn’t just about saying the right thing; it’s also about how a message lands. For that reason, we track AI Agent’s communication quality. Did it reply with empathy? Did it exhibit active listening and respond with clear phrasing?

Recently, AI Agent is even scoring slightly above humans with 4.48 out of 5 in communication, compared to 4.27. This means AI Agent captures the nuance of every message by considering the background context and acknowledging customer frustration before it gives customers a solution. 

AI Agent resolves every part of a customer’s question

What happens when a ticket ends without a clear answer? Customers feel neglected and leave the chat still unsure. This can make your brand look out of touch, leaving customers with the lingering feeling that you don’t care.

But don’t worry, we built AI Agent to close that loop every time: AI Agent’s resolution completeness score sits at a perfect 1 out of 1, compared to 0.99 out of 1 for human agents. 

In practice, this means customers feel cared for and understood, while your team receives fewer follow-ups, giving them more time to focus on strategic, high-priority tasks.

Read more: A guide to resolution time: How to measure and lower it

Brand confidence is on the rise

Building a great product is a two-way conversation between our engineers and the people who use it. We listen, review feedback, ship changes, and measure what improves.

From January to November 2025, AI Agent quality rose from about 57% to 85%. August was the first big step up, and September kept climbing. Brands are seeing fewer low-quality or incorrect answers and more steady decisions.

This is proof that merchants and their shoppers are witnessing the improvements we’ve been making, for the better.

Related: The engineering work that keeps Gorgias running smoothly

Shoppers are rating AI support almost as high as human support

At the end of the day, what matters is how customers feel when they talk to support. Do they trust the answer? Do they find it helpful? Are they running into more friction with AI than without it?

Our data shows that customers are appreciating AI assistance more and more. Since the start of 2025, AI Agent on live chat has gotten a CSAT score 40% closer to the average CSAT of human agents. For email, the gap has narrowed by about 8%.

The goal is to eventually achieve a gap of zero. At this point, AI’s support quality is indistinguishable from that of humans. To get there, we’re focusing on practical improvements like accuracy, clear language, complete answers, and better handoff rules.

How we measure CSAT gap: The CSAT gap is calculated by subtracting AI CSAT from human CSAT. When the number is closer to zero, AI is catching up. When it’s negative, AI is still below human results.

Reliable AI interactions start with accuracy

Behind every accurate AI reply is a team that cares about the details. AI Agent doesn’t make up answers—it follows what you teach it. The more effort your team puts into maintaining an up-to-date Help Center and Guidance, the better the customer experience becomes.

As we look ahead to 2026, we’re focused on fine-tuning knowledge retrieval logic, refining Guidance rules, and continuously learning from feedback from you and your customers.

We’re proud of the strides AI Agent continues to make, and can’t wait for more brands to experience the accuracy for themselves.

Want to see how AI Agent delivers exceptional accuracy without sacrificing speed? Book a demo or start a trial today.

{{lead-magnet-2}}

TL;DR:

• Fast ≠ good. Chasing faster replies without accuracy or empathy leads to frustrated customers, burned-out agents, and declining CSAT.
• Speed-only AI backfires. Quick but wrong responses damage trust and increase ticket volume.
• Train your AI like a new hire. The best results come when AI learns your tone, workflows, and policies—not when it’s treated as plug-and-play.
• Balance speed with quality. Brands like Boody, Cocorico, and TUSHY show that when AI is trained thoughtfully, teams can scale automation and keep the human touch.
• Adopt an accuracy-first mindset. The future of CX belongs to brands that prioritize reliability, empathy, and consistency over being the fastest.

Speed gets all the glory in customer support. The faster the reply, the happier the customer. That’s not always true. When CX teams chase response times at the expense of accuracy or empathy, they often end up with the opposite effect. Frustrated customers, burned-out agents, and slipping CSAT are common when speed is the only priority.

As more teams adopt AI tools that promise instant results, the risk grows. Quick responses mean nothing if they’re wrong or robotic. 

In this post, we’ll unpack why “fast” doesn’t always mean “good” and how an accuracy-first approach to AI leads to better support, and stronger customer relationships in the long run.

The speed trap: why CX teams fall for it

Response time has become the go-to measure of “good” support. Dashboards light up green when messages are answered in seconds, and teams celebrate shaved-down handle times. 

But focusing on speed alone can create a dangerous blind spot.

When “fast” becomes the only KPI that matters, CX leaders make speed-at-all-costs decisions. They may roll out untrained AI tools, overuse canned replies, or push agents to close tickets before solving real problems.

On paper, the metrics look great. In reality, customer sentiment quietly drops.

It’s no surprise that 86% of consumers say empathy and human connection matter more than a quick response when it comes to excellent customer experience. 

Fast support might satisfy your dashboard, but thoughtful, accurate service is what satisfies your customers.

Pitfall #1: Maximizing speed and sacrificing quality

A chatbot replies instantly, but gives the wrong answer. The customer follows up again, frustrated. Now your ticket volume has doubled, your agents are backlogged, and the customer’s confidence in your brand has dropped.

That’s the hidden cost of speed-first support. When teams prioritize quick replies over correct ones, CSAT falls, costs rise, and trust erodes. Customers remember the experience, not the timestamp.

They want to feel understood and confident that their issue is solved. A fast reply that misses the mark doesn’t deliver reassurance, empathy, or clear next steps. It’s not speed they value. It’s resolution, accuracy, and a sense that someone genuinely cared enough to get it right. 

Bad AI answers sting more than slow ones because they feel careless. Especially when they repeat the same mistakes. Accuracy builds credibility; speed without it breaks it.

How Boody delivers high-quality replies while maintaining speed

Boody, for example, found the balance. With AI trained on their tone of voice and workflows, they reduced response times from hours to seconds while maintaining a high CSAT score and freeing agents for meaningful work. 

The bamboo apparel brand uses Gorgias AI Agent to reassure the customer that someone is on the way to help, especially for urgent situations. It’s been instrumental in collecting preliminary information for more nuanced situations, like photos and product numbers for warranty claims.

As Boody’s CX Manager, Myriam Ferraty, explained the key is using AI to provide instant low-effort answers when customers need a prompt response. 

“If a customer reaches out about product feedback or issues, AI Agent prompts the customer to give us all the information we need. When an agent gets to the ticket, they can jump into solution mode right away.” —Myriam Ferraty, CX Manager at Boody

Boody found a way to avoid the “fast but frustrating” trap by pairing speed with quality, and the numbers prove it:

• 99.88% faster first-response times: Boody’s AI Agent reduced average response times from 7 hours to just 31 seconds.
• 9+ hours shorter resolution times: Within one month of implementation, resolution times dropped significantly while accuracy stayed high.
• 26% of all interactions handled by AI: Their AI agent took on repetitive queries, freeing human agents for higher-value conversations.
• 10% revenue lift from support: With agents focused on community engagement and brand experience, customer interactions began driving measurable revenue.

These results show what happen when CX teams train AI thoughtfully, it can becomes a trusted extension of the support team, instead of only increasing speed booster.

Takeaway: Fast and good is possible, but only when your AI is trained, guided, and measured for precision, not just speed.

Read more: How CX leaders are actually using AI: 6 must-know lessons

Pitfall #2: Treating AI as plug-and-play

Many CX teams expect AI to “just work” out of the box. They install a shiny new tool, flip the switch, and hope it starts solving tickets overnight. But AI isn’t a magic button. It’s a new team member. And like any new hire, it needs training, context, and feedback to perform well.

Untrained AI can quickly go off-script. It might give inconsistent answers, slip into the wrong tone, or worse, hallucinate information altogether. The consequences are confused customers, damaged trust, and more cleanup work for your human agents.

AI performs best when it’s trained on your brand voice, policies, and knowledge base. The best CX teams don’t settle for default settings or cookie-cutter templates. They invest time to train their AI. That’s what turns it from a generic chatbot into a genuine brand representative.

How Cocorico’s well-trained AI led to customer trust (and laughter)

Cocorico, a French fashion brand, shows what this looks like in practice. Instead of setting AI loose, their team invested time in teaching it how to communicate naturally and on-brand. Within just a few months, they achieved:

• 48% automation rate, handling nearly half of all customer requests.
• 22-second average first-response time, without losing personalization.

At first, Cocorico’s Ecommerce Manager, Margaux Pourrain, admitted she was hesitant to trust AI, “We were apprehensive about launching AI. On the technical side, I thought, ‘Would the AI respond professionally? Would it respond appropriately? Could it create more work by requiring constant verification?’ On the customer experience side, I was nervous it would feel impersonal.”

Her doubts didn’t last long. Once trained on Cocorico’s workflows and brand tone, AI transformed how the team engaged with customers, “AI Agent responds so personally that customers often don’t realize they’re talking to AI. We’ve even seen customers interacting playfully and joking around with Maurice.”

Takeaway: With proper training and oversight, AI can become a trusted teammate that enhances customer experience rather than diluting it.

Read more: How AI Agent works & gathers data

Pitfall #3: Losing the human touch

When CX teams chase faster replies above all else, it’s easy to forget that great support involves connection. Agents and AI start focusing on closing tickets instead of solving problems.

Speed-only goals create fast but flat experiences that technically help customers but don’t feel human.

Over-automation can strip away the warmth and personality that make a brand memorable. Customers might get an answer in seconds, but if it lacks empathy or context, trust takes a hit. Research supports that brands that prioritize emotional intelligence in support interactions see stronger loyalty and retention rates.

How TUSHY keeps their AI playful, not robotic 

TUSHY, the bidet brand known for its witty tone, took a more thoughtful approach to automation. With Gorgias Shopping Assistant, pre-sale questions about compatibility, installation, and recommendations are handled automatically. This frees up human agents to focus on relationship-building conversations.

As Ren Fuller-Wasserman, TUSHY’s Senior Director of Customer Experience, explained, keeping conversations authentic was central to their approach:

“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!”

That human touch has paid off. TUSHY’s Shopping Assistant mirrors their playful brand voice and delivers real results:

• +20% increase in chat conversion rate overall
• 81% higher conversion rate compared with human agents
• 13× ROI from the Shopping Assistant investment

“Shopping Assistant has been a game-changer for our team, especially with the launch of our latest bidet models,” Fuller-Wasserman said. “Expanding our product catalog has given customers more choices than ever, which can overwhelm first-time buyers. Now, they’re increasingly looking to us for guidance on finding the right fit for their home and personal hygiene needs.”

Takeaway: Automation shouldn’t erase your brand’s humanity, it should amplify it. When AI is trained to reflect your tone and values, it can boost both efficiency and emotional connection.

The smarter path forward: accuracy-first AI

The future of customer support doesn’t involve being the fastest. Instead it means being the most reliable. Accuracy-first AI reframes automation from a race to respond into a strategy to build trust.

When customers get the right answer, in the right tone, every time, they’re more likely to stay loyal, even if it takes a few seconds longer.

So what does accuracy-first AI actually look like?

• Starts with training and clear guardrails: Like any new team member, your AI needs onboarding. These guardrails include context, escalation rules, and examples of what “great” looks like.
• Learns from past tickets and feedback: Continuous improvement keeps your AI sharp and aligned with evolving customer expectations.
• Reflects your tone and knowledge base: Every response should sound like you, not a generic script.
• Complements instead of replaces human agents: AI should take the repetitive load so humans can focus on empathy, problem-solving, and connection.

Accuracy-first AI is a mindset shift. Teams that treat AI as a coachable teammate, not a plug-and-play tool, will unlock faster resolutions and higher CSAT in the long run.

Read more: Coach AI Agent in one hour a week: SuitShop’s guide 

Build for accuracy, instead of speed

Speed might win you a customer’s attention, but accuracy is what earns their trust. Fast replies mean little if they’re wrong, off-brand, or robotic. The real differentiator in modern CX isn’t how quickly you respond, it’s how effectively you resolve issues and make customers feel understood.

AI should enhance your team’s expertise, not replace it. Train it on your tone, coach it like a new hire, and measure it on quality as much as efficiency.

The brands that will thrive in the AI era won’t always be the fastest. They’ll be the most reliable, human, and consistent. 

Looking for AI-led support that’s fast and human? Book a demo with Gorgias to see how accuracy-first automation can elevate your support.

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TL;DR:

• Happy agents lead to better CX outcomes. When agents genuinely enjoy using their platform, it boosts productivity, morale, and customer satisfaction.‍
• Gorgias makes agents’ lives easier. CX teams at Glossier, Tommy John, and Brunt Workwear unanimously preferred Gorgias over legacy systems.‍
• AI helps teams scale without losing the human touch. By handling basic inquiries, AI gives agents more time for high-impact conversations and personalized support
• Agent satisfaction drives business impact. Brands saw improved team efficiency, reduced operational friction, and revenue gains from more focused, empowered agents.

Everyone talks about how important it is for your ecommerce tools to drive business growth, boost productivity, and deliver a high return on investment. But the equally important (yet often overlooked) third layer is how a tool affects the people using it day-to-day. 

The hidden costs of sticking to the CX status quo

The moment CX and ecommerce leaders start noticing slipping KPIs, frustrated agents, or rising support costs, they ask themselves a question, “Is it time to look for something new?” Sticking with the same tool might seem easier — no demos, evaluations, migrations, onboarding, or retraining involved. 

But ignoring the shortcomings of your current CX platform can snowball into larger issues over time. 

When CX agents don’t like the platform they’re working in daily, bigger problems arise:

• Agent productivity declines 
• Morale and employee retention suffer
• Operational costs rise
• Customer experience takes a hit
• Poor data and reporting if agents aren’t using tags or ticket fields correctly

Beyond the thousands of dollars saved in operational costs or hours saved per ticket, Gorgias helps CX agents focus on what they do best — creating the best customer experience possible. 

When a platform makes agents’ lives easier, they have more time to focus on the moments that matter, like proactively reaching out to VIPs, sending surprise birthday gifts, or empathetically handling nuanced tickets. Not to mention, they enjoy doing it. 

At our annual customer conference, Gorgias Connect, we asked three CX leaders to share their experiences using Gorgias. Aside from the impressive FRTs and CX-generated revenue metrics, one theme stood out — they all mentioned how much their agents enjoyed using Gorgias. 

Glossier’s agents have more time to be expert product consultants 

Emily Weiss first launched a beauty blog and community, Into the Gloss, in 2010 as a space dedicated to sharing real information, advice, and tips with real people. 

This laid the groundwork for Glossier, launching in 2014 with a fresh “skin first, makeup second” philosophy. Amidst the “full glam” era of makeup defined by smoky eyes and bold lips, Glossier’s skincare-oriented approach disrupted the norm. 

From the beginning, Glossier has attracted a strong community thanks to its products designed based on community feedback and its social media presence. Today, more than a decade later, the brand has evolved, but its core principles have stayed the same.

As a customer-obsessed beauty brand, it’s no surprise that Glossier takes a thoughtful approach to customer experience. 

We sat down with Cati Brunell-Brutman, Head of CX at Glossier, to dive into how the team uses Gorgias to make their lives easier while creating better relationships with customers. 

Glossier’s proactive approach to customer experience

How do you approach customer experience at Glossier?

I always like saying customer experience vs. customer service because I think customer service feels like we’re just solving problems in a transactional way. Customer experience is proactive and involves looking at the entire customer journey. 

Our team interacts with customers from the moment they first land on the website to when they become repeat users of a product, and eventually, when they become subscribers. There are many opportunities along the way for our team to connect with people, engage in conversations, and make complementary product recommendations. 

This was what our founder really wanted this team to be—beauty editors. Everyone on the CX team is an editor (or a product expert), making curated recommendations. My vision for our CX team is to give them more time to lean into that. 

Simplifying workflows with AI to empower agents

What are you doing differently now to make sure that your team and your business are more resilient? 

My motto for the year is simplify and automate. I don't want anyone on my team to spend their whole day in a Google spreadsheet. So I’m asking questions like, ‘What can I automate? How can I connect tools?’

I really look to my team, especially the newer members, for this, and encourage them to ask, 'Why do we do this?' Because if the answer is because we've always done it that way, that's not a good enough answer for me. 

I’m focusing on finding those moments to simplify things so that the team can concentrate on impactful work, such as creating connections and engaging with people. That’s what I really want my team to focus on because it’s what brings value to their work, our customers, and the brand. 

How did your team react when you switched to Gorgias from your previous platform? 

We actually had our agents weigh in on this. We showed them demos of all the platforms we were considering and had them attend the meetings to speak with the teams. 

Then, we ran a poll in Slack and asked the team, ‘If you were making this decision, what platform would you choose?’ All of the agents unanimously voted for Gorgias. So, we’re definitely fans. 

How has implementing AI into your CX strategy affected the team?

Throughout the industry, I think people are concerned that there’s going to be a transition to a state where CX is 100% AI, everybody is going to lose their jobs, and customers won’t be able to talk to a person. 

But as we've implemented AI at Glossier, we’ve maintained the same team size as when we first started. We just have so much more automation of things like with WISMO tickets, returns, exchanges, and basic tickets that we don’t need a human to answer with macros for six hours straight. 

Deeper human-to-human connections powered by better tools

With the additional capacity, what can your team now focus on?

The team is actually able to do more work because they're not dealing with an antiquated technical system, which makes their jobs easier and also saves us money in the long run.

Now, our agents can perform tasks that actually require a human. AI can send out tracking links, and people can do the people work. 

We receive a lot of questions about our products, like how to use them or specific recommendations. And that's when we want a person to sit down, look at the customer’s selfie, and do a shade match. Then our editors can ask follow-up questions about what the shopper is looking for and why. 

What makes your agents unique, and how does Gorgias help support them?

One of the things that I really love about Glossier is that our editors — our agents — are people, and we have customers who know them by name. 

It’s really unique, and they’re almost like internet celebrities within our community. I'll go to our Reddit page and see customers posting screenshots of their conversations with our agents, and other customers will reply saying ‘Oh my gosh, yes!’ or ‘They helped me too!’ 

Customers will DM us things like ‘This editor recommended a lipstick for me. It was great, I love it. Can that person recommend a blush for me as well?’ 

Being able to aggregate all those conversations across social media DMs, emails, and chats in one place is invaluable.

Where would your team be without Gorgias? 

Having a really bad time in Gmail.

Tommy John found that Gorgias was the perfect fit for its CX team

In 2008, Tom Patterson was a medical salesperson frustrated with ill-fitting undershirts. This problem he faced every day was the catalyst for him to found Tommy John, a dual-gender underwear, loungewear, and apparel company. 

Tommy John launched with its flagship product, the Stay-Tucked Undershirt, to solve Tom’s initial struggle that he knew other customers were also facing. Fast-forward a few years, and Tommy John expanded into more categories with innovative underwear product lines 

Customer comfort has always been the main priority for Tommy John, embedded in everything from product design to its Best Pair Guarantee. The CX team is responsible for maintaining a customer experience that is just as smooth and seamless as the products they're buying. 

Max Wallace, CX Director at Tommy John, shared his experience migrating from a legacy platform to Gorgias and how it impacted his team. 

The search for a platform that supported both customers and agents

What motivated you to find a new platform? 

We knew we had to seriously explore other options when we were assigned yet another Customer Success Manager on our former platform after having gone through several in a short span. It felt like we were starting from scratch every time, which made it challenging to elevate our CX alongside such a critical partner.

We wanted to do right by our customers and our agents, ensuring they had the reporting and tools they needed, plus more. Gorgias really offered all of those things.

What was most important to you and your team when evaluating helpdesks? 

We didn’t want anything that was reinventing the wheel. One platform we looked at wasn’t doing the agents justice by only allowing them to view their own tickets. 

We really wanted our agents to have a holistic understanding of the volume we’re receiving, which Gorgias provides. Now they have this fleshed-out understanding of every customer interaction, and that’s been a game-changer. They’ve been loving it.

Gorgias gave agents the tools they needed to thrive 

How has Gorgias impacted agent productivity and impact? 

We have definitely seen greater speed and productivity.  Even something as simple as macro suggestions has helped steer new agents in the right direction. That’s going to be huge during peak seasons, like BFCM. 

And the fact that agents can move seamlessly between conversations without losing context means they’re handling more interactions, faster, with less frustration. They feel confident in their workflows, rather than being bogged down in repetitive tasks.

Within two months, using Gorgias’s AI Agent has enabled agents to minimize time-consuming manual tasks and spend more time with high-intent customers, generating over $100,000 in sales. 

I’m confident Gorgias will help us achieve our goal of making selling and CX much more integrated. We do want to reward our team for their efforts in driving sales, and we can track conversion rates per agent in Gorgias.

Why was voice integration such a priority for your team?

Before, our agents didn’t have visibility into previous phone calls that other agents had taken. I can't tell you how many times there has been confusion regarding what's going on with the customer because our agents did not have visibility into the customer’s history. We’d have to pull the call recording, pass it along, and by then, the customer would have already been waiting.

So it was essential for us to find a helpdesk that we could use voice with. Now with Gorgias Voice, agents can look back in the timeline, listen to the call, or even read a transcript or AI-generated summary. That’s just been amazing, and they’re loving that. 

Tying revenue back to call tickets, where most of our upselling and cross-selling happens, has been another huge win.

Tommy John’s agents unanimously prefer Gorgias

How did agents react after the switch?

The number one thing that validates that we made the right decision is that our agents truly love Gorgias.

Two weeks after going live, we asked, ‘Do you feel you will be more efficient working in Gorgias than our previous platform?’ And it was unanimous — Gorgias, completely. And this was just two weeks in, with everyone still getting their feet wet. 

We sent out a survey, and seeing every single person answer in favor of Gorgias told me everything I needed to know about how quickly the team was adapting and how much they preferred the platform.

What has been the CX team’s feedback after using Gorgias for a while? 

Gorgias has really paid off for our agents in terms of their efficiency. Being able to transition seamlessly from a phone call to a follow-up email with just one click is amazing. And having all of that in the timeline — phone calls, emails, chats — that can’t be beat. 

Brunt Workwear’s team stays engaged by helping more customers each day

Eric Girouard founded Brunt Workwear in 2019 to fill a gap in the market for comfortable, high-quality workwear for skilled tradespeople. He came from blue-collar roots himself, and many of his friends and family also work in the trades.

Eric started the company in his garage, focusing on direct-to-consumer sales. Brunt Workwear aims to create products that aren’t just for tradespeople, but are actually built by them.

The workwear brand incorporates a significant amount of customer feedback into the design process to create products that actually make their lives easier. Brunt Workwear’s commitment to its customers is even more evident in its product naming convention — each product is named after a specific tradesworker. 

When we spoke with Ruth Trieger, Director of Customer Experience, she shared how the CX team achieves its goal of making solutions as easy as possible for their busy customers — and why agent satisfaction can’t be overlooked.

How Brunt Workwear makes every customer feel at home

How do you think about the state of CX today?

The best retail or CX advice I’ve ever received is to think of everyone who walks into your store or visits your website as someone entering your home. For every visitor, you will do some basic things, such as taking their coat or offering them something to eat or drink. But if you truly want to make someone feel welcome, you’re going to meet them in a way that aligns with their preferences and makes them feel like they’re a part of something. 

When you make someone feel welcome, they build an emotional connection with a brand that far transcends any product. That’s a powerful thing. 

As I consider customer experience and the growth of AI, I realize there is a constant need to deliver fantastic experiences while using the right amount of resources. If you can do that while still creating a memorable experience, you have a customer for life.

Making life easier for customers and agents with an intuitive platform 

What is your goal when designing experiences for Brunt Workwear’s customers?

Our customer is very busy and very hardworking. They have very little spare time. So if or when something goes wrong, I encourage my team to think, ‘How can we make the solution as easy as possible?’ That’s our goal — to put ourselves in their shoes and reduce friction wherever we can. 

AI can handle repetitive questions, allowing our agents to jump in quickly when nuance or empathy is needed most. What matters is making sure we are there for customers in the moments that really count.

How does Gorgias help your team achieve these goals compared to previous platforms you’ve worked with?

I come from a customer service training background, and I am used to teams needing weeks to train someone on a platform. With Gorgias, I was able to navigate the system myself in very little time. 

As a young but fast-growing brand, we have to be very nimble and change things quickly. Gorgias enables us to do that with a level of ease I've never experienced in my career, so we’re really grateful for the platform. 

I love that our agents can interface with the platform in a way that is very easy, which is good for them. From a productivity and metrics standpoint, if they’re moving easily through a platform, I also know that means they’re able to accomplish more touchpoints with our customers — more phone conversations, more emails, more chats. And that means we are helping more people. 

How does improved agent satisfaction tie back to business results?

At the end of the day, if you don’t have a happy, high-functioning team, you have literally nothing in all the world. We have a talented team, and the more customers they interact with, the more likely those people are to stay with the brand. So we see an increase in customer lifetime value when our agents can spend more time with our customers. 

Gorgias helps agents move from mountains of tickets to meaningful connections 

What additional opportunities does AI open up for your team?

AI is not replacing the human touch; it’s giving us more room to lean into it. It reduces friction so that CX agents can take on higher-value work like running close-the-loop programs, proactively reaching out on the phone, and answering faster. 

If a customer is asking, ‘Where is my order?’, I don’t need to take up an agent’s time with that because AI can get them a simple, fast answer. Then, when another customer needs somebody’s time, they’re there because that person isn’t answering a mountain of tickets. 

That’s the exciting part, AI handles the repetitive stuff, and our agents get to focus on making real connections. 

How has Gorgias enabled you to communicate the value of CX to the broader business internally? 

The reporting in Gorgias has allowed us to become a true strategic partner in the business. CX sees everything: what’s working, what’s not, and what customers are asking about. For every new product launch, every campaign, and every change, my team is on the front lines. With Gorgias’s reporting, we can bring that insight back to the rest of the organization and help shape smarter decisions.

What’s been cool is that we’re now part of the feedback loop in a much more meaningful way. Without Gorgias, we would not be able to add the same level of value as a strategic partner. That’s where I see our role continuing to shift — becoming more proactive, faster at serving customers, and a critical business function. 

At the end of the day, CX knows what’s working, what isn’t, and how customers are feeling. The more we vocalize that, the better off the entire company is.

Choose a CX platform that your CX team actually wants to use

Happy, empowered agents deliver the kind of experiences that keep customers loyal and businesses growing. 

Glossier, Tommy John, and Brunt Workwear show what’s possible when teams have a platform designed for them. More efficiency, more impact, and more human connections. Because when agents love their platform, everyone wins.