Welcome to the deep dive your shortcut to being well-informed today
We're tackling a platform that really sits right at the intersection of customer
service open source flexibility and well cutting-edge AI
We are sinking our teeth into tile desk. It's a really powerful platform
That's I think fundamentally changing how companies build these sophisticated
LLM enabled chat bots and live chat solutions. It really is and it's crucial
because it hits two major demands
You know for enterprises at the same time. You've got the need for advanced
automation
definitely, but also that
Non-negotiable requirement for data control. It's an entire ecosystem really and
the fact that it's open source that elevates it way beyond just a standard
Sauce product. Absolutely and before we dive deep into the architecture and all the
agentic AI stuffed
How this boasts we want to give a quick nod to our supporter today safe server
Yeah, safe server is a great partner here
They focus specifically on hosting exactly this kind of flexible powerful open
source software
They really support you through the technical side of digital transformation
So if you're looking into deploying say custom secure AI solutions like tile desk
in a robust hosting environment
Well, you can find more info at www.safeserver.de
Perfect. So, okay our mission today. It's tailored specifically for you the
listener. We want to unpack tile desk from the ground up
We'll explain how it combines, you know classic live chat with these sophisticated
large language models
And crucially how you can actually deploy it using enterprise-grade tools like kubernetes
and docker
We want to make it accessible even if you're maybe just starting out with your
conversational AI strategy
Okay, so let's start with the basics the foundation tile desk
It actually began its life as an open source live chat platform built really robustly
using node.js and express
Standard stuff and it was designed for that asynchronous real-time customer support,
you know designed to scale but honestly
It's grown way beyond just being a chat widget now, right?
So if I'm looking at this, is it just like a better version of the old chat box?
I already have on my website or is it something more? It's really an entirely new
category. I think of it as a conversational app development
platform less like just a chat box and more like a
Complete no-code visual workbench kind of like tools like voice flow maybe but it's
purpose-built to empower you to create these highly sophisticated
LLm enabled bots bots that can handle complex multi-step tasks
It's really about building automated applications that just happen to communicate
conversationally
Okay, but for any company, you know worried about automating customer interactions
The fear is always losing that human touch or worse providing those frustrating
dead-end AI responses. So for enterprise adoption
What's the single most essential feature? Is it still being able to hand off to a
human? Oh, absolutely that human-in-the-loop capability is
Completely non-negotiable. It has to be there tile desk provides these out-of-the-box
chat bots that work seamlessly
side by side with human agents if the AI gets confused or maybe the customer
expresses something that needs real empathy or it's a
high-stakes problem
The interaction can instantly transition. Yeah, really smoothly to a human agent,
but here's the kicker
Okay, the human agent gets the full transcript and all the context from the AI
interaction
So there's zero frustration the customer doesn't repeat themselves and you get
reliable support
So the customer isn't starting over from scratch, right that alone fixes like 90%
of bad chat experiences
Probably now let's talk about managing communication across different platforms
that multi-channel challenge most businesses
They have to build separate bots for whatsapp for their website for Facebook
Messenger
Managing that content parity is just a nightmare exactly. That's the pain point
tile desk addresses really brilliantly
I think with its innovative multi-channel approach you design and configure your
chat bot replies only once you use rich media elements
You know images carousels buttons right there in the design studio
Then the platform's underlying engine it just auto adapts those responses to the
unique
Formatting needs of every single target channel web whatsapp Facebook Telegram SMS
even email Wow
So the key takeaway there is efficiency build it once deploy it everywhere and the
format just sorts itself out automatically
You're not rebuilding like six separate brains
You're just giving one brain the ability to speak six different format languages.
That's genuinely interesting for you know large-scale operations
Exactly it standardizes all that complexity. Okay. Let's move to the brains of the
operation the AI itself
We're hearing terms like a gentic AI operating system. Mm-hmm
It's one thing to answer a simple FAQ
But tile desk seems to be aiming for more complex workflows like information retrieval
operations stuff
Yeah, and what's absolutely crucial for any enterprise AI is accuracy. You just
cannot have bots hallucinating responses with customers
It's a non starter. This is where the knowledge base integration comes in and it's
powered by our agi
That's retrieval augmented generation essentially the AI agents are connected
directly to your company's internal documents your knowledge base articles
Maybe even external data sources. The AI isn't making up the answer
It's actually retrieving a verified source document and then formatting the answer
based on that source right our eggs
We've heard about our G before but the documentation specifically mentions a gentic
aria that sounds well
It sounds like it's tailored for large organizations with frankly messy data. It is
spot-on
Standard rag often hits this massive enterprise problem siloed knowledge
information is all over the place a
Single sort of monolithic RG system tries to index everything and that can lead to
relevance issues
The AI might pull a pricing sheet when it should have grabbed a warranty document
For example agentic RG solves this it lets you separate your knowledge into
multiple distinct repositories
So you might have one repository just for sales PDFs another one for HR policies
and maybe a third for technical
Support manuals. Okay interesting and how do they work together then do they talk
to each other you chain them together essentially
The system applies search fallbacks and guardrails
So it allows the agent to say check the technical docs first
And if that doesn't yield anything useful, maybe then it tries the general FAQ
repository
It ensures your answers are verified. They're routed correctly and retrieved from
the right specialized knowledge source
It prevents that cross-domain confusion that brings up the challenge of search
itself though users don't always ask questions perfectly, right?
They use messy colloquial language. What about this hybrid search? Does that
actually help solve that problem?
It really does because hybrid search cleverly combines two powerful techniques. It
uses traditional full-text retrieval
That's your basic keyword search, which is fast and great for exact matches and it
pairs that with semantic understanding
Which is powered by the LLMs themselves. So if a user types something complex like
My delivery was supposed to be here yesterday, but the tracking hasn't updated
since Tuesday
The system understands the intent behind it
It's a delivery issue needing tracing even if the exact keywords are kind of spread
out and imprecise
This leads to much smarter result and internally fewer missed resolutions
And the system doesn't just sit there executing instructions does it? It actively
learns
That's probably the defining feature the self-learning loop. The AI agent isn't
static
It actively listens to every single message exchange, especially those where the
interaction eventually had to transition to a human agent
That's key. It extracts crucial beta points common questions that stumped it before
and importantly the effective solutions
provided by the human agents
This new high quality knowledge is then automatically and seamlessly integrated
right back into the knowledge base
Huh? So every single time a human has to step in and you know save the day
They are without even realizing it training the AI to handle that exact scenario
better next time
That's continuous improvement baked right into the operational layer. That's pretty
clever precisely and we've seen this incredible learning capability
But great AI still needs sharp instructions to execute tasks reliably, right?
Which brings us to how tile desk actually makes designing complex workflows simpler
using their visual tools
We're talking about the no-code slow builder or the visual designer. Yeah for
anyone building automation complexity always kills usability
It just gets overwhelming
How does tile desk make managing these potentially huge complicated automation
routines manageable?
Especially when you're throwing AI logic into the mix it relies heavily on this
concept of prompt chains
This is really how you decouple the complexity if you try to write one massive
Monolithic prompt telling the LLM to handle say five different steps all at once
Well debugging that or updating it becomes basically impossible prompt chains break
that logic down
Instead you create a sequence of smaller smarter much more manageable prompts
Okay
So each little prompt module has just one specific job like check inventory or
calculate shipping costs something like that exactly and the full builder
Let's you do this orchestration with no code
You just drag drop and connect these smaller prompt blocks visually this give you
really granular control over the workflow
Makes it transparent much easier to debug and ensures the AI follows the exact
decision tree you designed step-by-step
Hmm that structure seems pretty essential and we start thinking about the multiple
specialized agents concept, too
If you can break down the workflow you can break down the agents as well, right?
You don't need one giant AI trying to be a genius at absolutely everything that
really works
Well instead you create AI agents with highly specialized roles
Maybe one for sales another for technical support a third for onboarding new users
And because the workflows are chained these specialized agents can actually
collaborate and even switch mid conversation
So if the sales agent identifies a technical issue cropping up
The replace bot action allows a seamless handoff to the specialized support agent
and crucially it passes all the context along
Okay to ground this powerful design in reality
Let's maybe look at a few specific use cases from the source material that
illustrate this kind of integration
The ecommerce assistant sounds like a great example of connecting AI directly to
core business systems
Oh, it's powerful because it moves way beyond just simple FAQ stuff. The ecommerce
assistant integrates directly with platforms
Like Shopify it syncs product catalogs in real time. It can display interactive
product carousels right within the chat window and
Crucially it allows the customer to conversationally build and modify their
shopping cart
That really transforms what might have been just a support bot into a genuine
conversion engine
And what about addressing that traditional time sync of customer service email ticketing?
Yeah, the AI email ticketing feature looks like a huge efficiency game. It links to
basically any corporate inbox
But here's the smart part before responding it actually learns from your historical
email threads and successful resolutions from the past
This means its automated responses are rooted in real high quality past
interactions that leads to more accurate often instant resolution of common queries
Which minimizes the backlog for human agents and then there's this simpler but
still critical
24-7 scheduling feature it connected Google Calendar or Outlook Calendar handles
availability checks confirms appointments all without any human intervention needed
Okay, that's a pretty comprehensive look at the functionality, but the real value
proposition. I think especially for technical or
Regulated industries is that tile desk is open source. This gives users profound
control over their infrastructure
So how do you actually deploy tile desk at scale? What does that look like? Right?
This takes us into the technical foundation and
Tile desk offers flexibility here, which is good for initial development
Maybe for trials or even smaller deployments the recommended option is Docker
compose. It's relatively easy
It's great for quickly standing up the necessary multi-container applications
locally just to see how the whole system the server dashboard
Database all interacts but Docker compose probably doesn't cut it when you need,
you know
5 nines reliability and massive user concurrency, especially with a real-time chat
application precisely
Yeah for production environments and definitely for enterprises that need high
availability and serious scalability
The clear recommendation is Kubernetes with Helm
Chat is fundamentally a real-time service. If your server hiccups or fails active
conversations just drop. That's bad
Kubernetes handles all that container orchestration
It manages automatic recovery load balancing rapid scaling of all the necessary
components things like the core tile
Desk server the dashboard the design studio the underlying MongoDB database
Kubernetes ensures continuous service even under peak loads
Okay, and the combination of being open source and having these controlled
deployment options enables that really critical concept of data sovereignty
Why is that such a massive differentiator these days because offering on-premises
deployment means organizations get complete actual physical control over their data
and their privacy
Full stop tile desks can be deployed entirely behind your corporate firewall. This
is often mandatory for sectors
Like finance health care or government entities and this control extends right down
to the most sensitive parts of the AI stack itself
Can you explain that control a little more specifically like what exactly do they
get to control pretty much everything that matters
They get local LLM integration
This means they don't have to send potentially proprietary or sensitive customer
data out to a third-party cloud providers LLM
Like open AI or entropic. Yeah, they can connect to and run open source models like
llama and Mistral
Completely locally within their own infrastructure. Yeah, furthermore. They
maintain vector store control
They can use local storage solutions like Q. Dran
Which for the less technical listener is basically the specialized database that
holds the semantic meaning of your documents for the our age system
Keeping that vector store local and secure behind the firewall is paramount for
compliance and privacy
That kind of flexibility and security isn't just a nice-to-have feature
It feels like a necessary strategic advantage for any large company serious about
digital transformation now
It absolutely is tile desk provides this powerful flexible open source way to
automate customer interactions
But it crucially maintains that human safety net while giving you
unprecedented control and security over the entire data infrastructure and the AI
stack
That's an excellent summary of why this platform is creating such a stir
And once again, we really want to thank our supporter for this deep dive safe
server
They help provide dedicated hosting solutions for digital transformation
initiatives including powerful open source platforms
Just like tile disk you can find out more at
www.safe server dot de and maybe here's one final provocative thought for you to
consider based on everything we've discussed today
We talked about how tail desks self-learning AI
Improves directly from human agent responses and feedback, right?
Especially from those escalated cases the platform also offers these AI coup pilot
tools that augment human agents in real time
Providing suggestions and knowledge during live chats
So think about this if the AI is learning directly from the humans and the humans
are simultaneously be augmented by the AI
How might this deep integration fundamentally change the role of the support staff
over time?
Could it turn them from simple agents focused just on resolution into expert AI
trainers focused on validating and refining the organization's core knowledge?
Something to mull over as you maybe explore these kinds of capabilities yourself.