AI behind the scenes in Swiggy / Zomato

I was scrolling through Zomato one evening, not really sure what I wanted to eat. Within a few seconds, I found myself looking at a list of dishes that felt oddly familiar, almost like the app had read my mind. My usual comfort food, a place I had ordered from recently, and a couple of new options that were surprisingly close to my taste.

I hadn’t typed anything yet. I was just browsing.

This wasn’t just a food delivery app anymore. It was quietly making decisions for me, even before I had made up my mind.

And that’s where AI begins its work.

Food delivery apps like Zomato and Swiggy are no more just logistics platforms, they are AI-driven systems. From predicting what you’ll order to dynamically adjusting prices and optimizing delivery routes, AI operates at every layer of the experience. Let’s see how

Personalized Recommendations

Had I not been from an AI background, I would have often wondered, how does the app seem to know what I might order?

You open the app, and right there are dishes you like, restaurants you’ve ordered from, or options that feel very close to your taste.

This is not by chance.

Platforms use AI and data from your past orders, searches, and usage patterns to personalise what you see. They also look at broader patterns such as what people with similar preferences tend to order. Based on this, the app arranges your homepage in a way that feels familiar and relevant. In a way, AI is helping narrow down choices, so you don’t have to start from scratch every time.

Delivery Route Optimization & Rider Allocation

Once you place an order, the problem shifts from “what to show” to “how to deliver.”

At this point, AI takes over the logistics.

The system has to decide:

• Which delivery partner should be assigned

• What route they should take

• How to ensure the fastest possible delivery

This is not just about picking the nearest rider. The AI models evaluate multiple factors in real time such as traffic conditions, the rider’s current workload, distance, and even how quickly a rider typically completes deliveries. Based on this, the system predicts which combination will get your food to you the fastest, and assigns the order accordingly.

Think of it as an AI-powered logistics engine, continuously making and updating these decisions across thousands of orders at the same time.

Delivery Time Prediction (ETA)

The estimated delivery time you see is not a fixed number—it’s a prediction based on data.

Food delivery platforms use predictive models, built on past orders and real-time inputs, to estimate how long your order will take. These models take into account:

• The restaurant’s typical preparation time

• Current order load at the restaurant

• Traffic conditions

• Travel time to your location

As your order moves through each stage, the system keeps checking what’s actually happening, whether the food is getting ready on time, whether the rider has picked it up, and how traffic conditions are changing.

Based on this, the estimate is updated along the way.

What looks like a simple “27 minutes” is, in reality, a continuously refined estimate based on both historical patterns and real-time conditions.

Demand Forecasting

Even before you place an order, the app is already preparing for what might come next.

Food delivery platforms use predictive models that are built using past data to understand patterns like when people usually order, which areas get busy, and how demand changes over time. Based on this, they make estimates about:

• Busy hours during the day

• Weekend spikes

• Changes during rain

• Sudden jumps during events like matches or festivals

These are not exact predictions. They are educated guesses, based on what has happened earlier in similar situations. Using this, the platform can plan better by making sure there are enough delivery partners in the right areas and that restaurants are not caught off guard.

In a way, AI is helping the system learn from the past and stay a little prepared for what’s coming next.

Dynamic Pricing

Sometimes you may have noticed that the price has an element like a surge fee, or that delivery charges and offers seem to change at different times.

This is not random. Food delivery platforms use data and simple predictive models to adjust pricing based on things like:

• How many people are ordering at that time

• How many delivery partners are available

• Distance and location

• Time of day

So during busy periods, prices may go up slightly to manage the load. And during slower times, you might see more discounts or offers to encourage orders.

In a way, AI is helping the platform balance demand and supply, so that the system keeps running smoothly.

Order Batching

I remember earlier, when ordering food, there were times I would wait quite long for it to arrive. If I called the restaurant, the answer was often something like, “aapke area ke aur do orders the, sab ek saath leke nikla hai.”

It worked, but it wasn’t always efficient. Sometimes your order would just sit there, waiting for others to get ready.

That has changed quite a bit now.

Today, platforms like Swiggy and Zomato still group orders, but it’s far more structured. Behind the scenes, systems use data and optimization models to decide when it makes sense to combine orders. They look at things like:

• Which orders are from nearby restaurants

• Whether they are going in the same direction

• If they will be ready around the same time

Only when these conditions align does the system assign multiple orders to one delivery partner. This way, efficiency improves without significantly delaying any single order.

In a way, what used to be a manual, rough process is now handled more carefully, with AI helping decide when batching works, and when it doesn’t.

Customer support chatbots

I’m sure you’ve seen these everywhere, whether in banking apps, train bookings, or now in apps like Swiggy and Zomato.

This is probably one of the most visible uses of AI.

A few years ago, these chatbots mostly felt like they were repeating FAQs. They could handle only very basic queries, and anything slightly different would get stuck.

That has changed quite a bit now.

With better AI models trained on past interactions, these systems are able to understand what you are trying to say more clearly, even if you don’t phrase it perfectly.

So when something goes wrong with an order, many issues like refunds, missing items, or delays are handled much more smoothly than before.

In a way, AI is helping resolve common problems quickly in the background, without you needing to wait to speak to a person every time.

Final thoughts

This is how AI is influencing your experience inside food delivery apps like Zomato and Swiggy.

While companies don’t always go into detail about what’s happening behind the scenes, many of these features are built using data-driven systems and predictive models that learn from patterns and improve over time.

What we see is that AI is involved at almost every stage, from the time you open the app and decide what to order, to how your food is prepared, assigned, and delivered.

And yet, most of this goes unnoticed.

The experience feels simple and effortless. But behind it, there are many small decisions being made constantly.

I hope this gave you a slightly different way of looking at something we use so often.

In the next article in this series, I’ll explore how AI is working behind the scenes in Google Maps, the app most of us can’t imagine travelling without.