DEV Community: hamza qureshi

Building Real-Time Chat That Doesn’t Break at Scale (and Actually Uses AI Properly)

hamza qureshi — Mon, 27 Apr 2026 07:52:10 +0000

Building Real-Time Chat That Doesn’t Break at Scale (and Actually Uses AI Properly)

Most teams underestimate chat. When you try to go past the demo, complexity rears its ugly head pretty quickly. You’re no longer just rendering messages. You’re dealing with:

real-time delivery guarantees
concurrency across users and sessions
message ordering and consistency
retries, offline states, and reconnections

The Problem With Traditional Chat Architectures A typical chat setup looks something like this:

REST endpoints for sending messages
WebSockets or polling for receiving updates
A database for message persistence
Some background jobs for notifications It works—until it doesn’t. At scale, you run into:
Latency issues (especially across regions)
Message duplication or ordering bugs
Connection instability under load
Complex state management on the client
Difficult horizontal scaling Now add AI on top of that and things get even messier. Because AI isn’t just another API call—it introduces:
streaming responses
context management
dynamic querying of data
higher compute variability Suddenly your “chat feature” becomes distributed systems engineering.

What Changes When You Add AI Assistance Most teams approach AI in chat like this:

User sends a message
Backend forwards it to an LLM
LLM returns a response
Response is displayed This works for demos, but breaks in production. Why? Because real users don’t just ask isolated questions.

They:

reference previous context
expect accurate, product-specific answers
trigger workflows, not just responses So now your system needs to:
maintain conversation state
inject relevant context dynamically
query internal data sources
decide when to respond vs. act

You are no longer building chat at this point, you are building an AI orchestration layer.

Here’s how a smarter, real-time system looks when chat and AI work together, not in separate layers.

First up, always-on, fast connections. Instead of just sending messages, use WebSockets (or something similar) for everything—streaming AI replies, live updates, quick interactions. Forget about polling or unnecessary waiting around.

Next, every time something happens, treat it like an event. When users send messages, AI keeps streaming responses, there’s a system action, or a notification pops up—each one’s an event. This makes it way easier to piece things together, track what’s going on, and build on top later.

Then there’s the AI layer. Don’t just toss raw prompts at it. Make sure the AI actually uses what’s happening—inject structured context, keep session memory alive, grab info from your own data whenever you need it. That way, AI responses are tied to your product and aren’t just random guesses.

AI replies should stream out as they’re generated. No waiting until everything’s processed. Users get feedback right away, which feels faster and keeps things moving.

Finally, you need infrastructure that can actually scale. That means handling thousands of connections at once, dealing with message volume spikes, and managing AI response times that aren’t always predictable. So build with horizontal scaling, smart connection management, and efficient message brokering right from the start.

Where DNotifier Fits In

Forget patching everything together yourself. DNotifier hands you a setup where real-time messaging just works, connections scale smoothly, events move through a tidy pipeline, and AI slides right into the message flow. So instead of wrestling with WebSocket servers, message queues, AI integration, or rolling your own notification system, you just tap into a platform that’s got all of that baked in.

Practical Use Cases

Once you’ve wired up this architecture, you get way more than basic chat. Imagine AI-driven support that actually gets what users need. Or in-app copilots guiding people step-by-step. You’ve got real-time notifications tied to user actions, plus automated tasks triggered just by chatting. Everything runs on the same solid backbone.

The Real Shift

Here’s where people get it wrong—they treat chat as just a UI detail. It’s so much more. Chat is basically a new interface for your whole system. Add AI, and suddenly it’s a query layer, a control panel, and the main way users interact—all wrapped into one spot.

Final Thought

If you keep treating chat as an optional add-on, you’ll end up rebuilding it every time your app grows. But when you treat it like true infrastructure—real-time, event-driven, and built for AI—you get a system that doesn’t just reply to users. It actually works side-by-side with them.

ai #realtime #ai layer #orchestration #tools available #dnotifier

The End of Waiting: How Agentic AI Is Replacing Static Dashboards

hamza qureshi — Thu, 16 Apr 2026 09:37:49 +0000

Let’s be honest—analytics dashboards haven’t really evolved in how we use them.

Yes, they look better.
Yes, they’re faster.
Yes, they have more charts.

But the core problem remains unchanged:

If the exact insight you need isn’t already there, you’re stuck.

The Real Bottleneck Isn’t Data — It’s Access

Every stakeholder has experienced this.

You open your analytics dashboard looking for a specific insight. It’s not there.

So the process begins:

You reach out to the product team
It gets added to a backlog
It waits for prioritization
Development begins
It gets tested and eventually deployed

All of this just to answer a single question.

By the time the insight is available, the urgency is gone.

Dashboards Are Static. Questions Are Not.

Business questions are dynamic:

What caused yesterday’s drop in engagement?
Show retention for users from a specific campaign
Compare performance across custom segments

But dashboards are:

Predefined
Rigid
Limited to what has already been built

This mismatch creates friction across teams and slows down decision-making.

Enter Agentic AI: Analytics That Talks Back

Now consider a different approach.

Instead of navigating dashboards, you ask:

“Why did conversions drop last week?”

The system:

Understands the intent
Queries the database directly
Processes the result
Responds with a meaningful answer

No backlog. No sprint cycle. No dependency on engineering.

Just answers.

This Is Not Just Chat — It’s an Intelligent Data Layer

An agentic AI system is not a chatbot placed on top of data.

It is an intelligent layer that can:

Generate queries dynamically
Interpret business intent
Handle follow-up questions
Drill down without predefined constraints

It transforms your data layer into something interactive and adaptive.

Building This in Practice

When building this kind of system, the challenge is not the idea. It is execution.

You need:

Real-time communication
Structured message handling
AI orchestration
Scalable infrastructure

Many developers initially reach for frameworks like LangGraph to build agentic workflows.

While powerful, they often introduce complexity:

Workflow management overhead
State handling challenges
Additional setup for communication layers
A Simpler Approach with DNotifier

In practice, a much simpler path is to use a platform that already handles communication, event flow, and AI interaction in a unified way.

This is where DNotifier fits naturally.

Instead of stitching together multiple layers, DNotifier provides:

Real-time messaging infrastructure
Built-in AI pipeline integration
Event-driven architecture
SDKs to quickly wire agents into applications

Using DNotifier, the same agentic analytics system can be built significantly faster.

What would normally require:

Setting up orchestration frameworks
Managing state across agents
Building communication layers

Can instead be achieved through a more streamlined approach focused on sending and processing messages.

How the Use Case Fits Perfectly

For an agentic analytics system, the workflow becomes straightforward:

User sends a natural language query
The message is routed through the AI pipeline
The system interprets intent and queries the database
The response is returned in a structured, meaningful format

Because DNotifier is already designed for real-time communication, it naturally supports:

Conversational flows
Multi-step interactions
Scalable message handling

This makes it a strong fit for building systems where interaction is continuous and dynamic.

The Impact on Teams

This shift changes how teams operate:

Stakeholders are no longer blocked by missing dashboards
Product teams are not burdened with constant analytics requests
Developers focus on systems, not one-off features

Instead of building dashboards for every possible question, teams enable users to explore data directly.

From Dashboards to Dialogue

We are moving from a model where:

“Insights are predefined and delivered through dashboards”

to one where:

“Insights are generated dynamically through conversation”

This is not just a UI improvement. It is a fundamental shift in how data is consumed.

Final Thought

The real advantage of agentic AI is not just speed. It is flexibility.

It removes the dependency on rigid systems and allows users to interact with data on their own terms.

Whether built using orchestration frameworks like LangGraph or streamlined platforms like DNotifier, the direction is clear:

Static dashboards are being replaced by conversational intelligence.

And the teams that adopt this shift early will move faster than those still waiting for the next sprint to answer a simple question.

AI Journey - From 0 to Hero

hamza qureshi — Fri, 10 Apr 2026 15:05:32 +0000

My Journey started from Development to Leadership and Designing Architectures. The journey took more then 10 years to reach to the point where i can design architectures.

Now the era is changing too fast. Realised now its time to start coding through AI Assisted Platforms.

So coming back to pivot point, where i should start coding apps and experience new the shift in Technology.

Every corner around the world is filled with AI Gossips.

So to cut short my story for now.

Installed cursor on my laptop.

Learned about RAGs,
Vectors Stores,
New Models Available,
APIs available

and started coding my first Agent that can support customer queries.

As i had experience in NodeJS and ReactJS already. So overall it was a good experience.

Then learned about

I will keep posting my experiences and technologies i am using in my future posts.

So please keep me motivated so that i can share my experiences and become a part of a community that i feel i have to get in. :)

Happy Weekend Everyone.!