Jio Bharat

Designing a simpler way to access India’s digital ecosystem

Jio Bharat is an exploration of how an ONDC-based consumer experience could bring shopping, food, news, and AI into a single app.

The idea was to make everyday digital services easier to discover and use, particularly for people who may not be familiar with digital commerce platforms. Instead of building separate experiences for every use case, I explored how a common AI layer could connect different intents and help users move from discovery to action.

The product

The experience brings together:

• Shopping across multiple merchants

• Food discovery based on location

• News based on interests and location

• AI-powered search and conversation

• Image-based product discovery

• Voice interaction

The underlying idea was simple:

Every intent should have a clear path to an action.

The challenge

ONDC creates a fundamentally different commerce model from a traditional marketplace.

Products can come from different sellers, restaurants can be discovered through different networks, and users don’t necessarily need to know which platform provides the service they are looking for.

That creates a new UX challenge.

A user shouldn’t have to understand the underlying network to use it.

The experience therefore needed to hide much of that complexity and make the interface feel familiar, regardless of where the underlying product or service came from.

One interface, multiple intents

I explored the experience around four primary areas:

Search → News → Food → Shopping

Rather than treating these as completely separate products, they share a common interaction model.

The AI layer sits across the experience and gives users another way to express what they want.

A user can search traditionally, browse through the interface, or start a conversation.

Smart Search

The search experience combines traditional search with AI-assisted discovery.

The starting point remains familiar: users can enter a search query normally.

From there, the system can:

• Suggest more specific queries

• Understand the user’s intent

• Surface relevant products

• Generate contextual results

• Start an AI conversation

For example, a search such as:

“Checked shirts for men under 1500”

can move beyond a list of search results.

The experience understands the intent and surfaces relevant products, while still allowing the user to refine the request.

Moving from search to conversation

The AI conversation creates another layer of discovery.

A user can ask for similar products, refine what they’re looking for, or continue the interaction without starting another search.

This creates a more fluid path:

Search → Understand intent → Discover → Refine → Select

Contextual product selection

Product discovery doesn’t stop at finding an item.

Once users find something they like, the interface surfaces the information needed to make a decision.

The AI experience can present:

• Similar products

• Product variations

• Sizes

• Colours

• Quantity

The user can then make their selection directly within the conversation.

For example, after choosing a shirt, the interface presents available sizes and confirms the selected product before adding it to the cart.

The goal was to reduce the number of separate screens involved in a relatively simple shopping decision.

Food

The food experience takes a location-first approach.

Instead of asking users to search through restaurants without context, the experience starts with where they are.

Users can:

• Explore nearby restaurants

• Search for dishes

• Filter by distance

• Filter by rating

• See restaurants that are currently open

• Browse by cuisine

• View restaurants on an interactive map

The map and restaurant results work together, allowing users to understand both what is available and where it is.

From discovery to delivery

The interaction is designed around a simple sequence:

Location → Discover → Compare → Choose → Order

AI remains available throughout the experience so users can switch between browsing and conversation without leaving the food journey.

News

The news experience follows the same principle, but the primary signal changes from location to relevance.

The concept combines:

• Global news

• Local news

• Breaking news

• Topic-based content

• News agencies

• Personalized recommendations

A location such as Kondavita, MIDC, Andheri East is used to surface local information alongside broader stories.

The experience also introduces a voice-based news assistant, giving users another way to consume news without manually browsing through multiple stories.

The aim is to make the news feed more relevant without overwhelming the user with everything available.

Shopping

Shopping was designed around the idea that users shouldn’t need to understand how ONDC works before they can shop.

The interface brings products from different merchants into one experience.

Users can:

• Browse categories

• Discover products

• Search

• Compare products

• Find similar products

• Add products to the cart

The AI layer adds another route into discovery.

A user can share an image and ask for similar products, then move directly into product selection.

Image → Product discovery

This creates a useful alternative to text-based search:

See something → Share the image → Find similar products → Choose → Add to cart

This is particularly useful when the user knows what something looks like but doesn’t know what to search for.

One connected interaction model

Across shopping, food and news, I kept the interaction model consistent.

The user can move between:

Browse

↓

Search

↓

Ask AI

↓

Refine

↓

Take action

The specific content changes depending on the user’s intent, but the underlying interaction remains familiar.

That consistency becomes important when multiple services live inside the same product.

Designing the AI layer

AI wasn’t treated as a separate chatbot sitting beside the product.

Instead, I explored it as an interaction layer that could sit across different intents.

For shopping, it helps users discover and refine products.

For food, it can help users explore nearby options.

For news, it can summarize and surface relevant information.

The design therefore treats AI as part of the product flow rather than as a destination users have to deliberately enter.

Visual language

The visual system was designed to make the product feel approachable and consistent across very different categories.

The interface uses:

• Warm orange tones

• Soft gradients

• Rounded cards

• Large imagery

• Simple navigation

• A consistent AI interaction element

The same visual language carries across shopping, food, news, and AI so the product feels like one experience rather than a collection of services.

My role

I worked across the product experience, defining the interaction model and exploring how ONDC, AI, search, content, and commerce could work together in a single consumer interface.

My focus covered:

• Product experience

• Information architecture

• User flows

• AI interaction patterns

• Search and discovery

• Shopping experience

• Food discovery

• News experience

• Visual design

• Interaction design

• Design system direction

The outcome

The project explored a different model for digital services:

Users bring the intent. The product figures out the path.

Instead of forcing users to understand whether they are searching a marketplace, restaurant network, news platform, or AI assistant, the experience connects those capabilities behind a common interface.

For me, the most important design decision was making the complexity of the underlying ecosystem invisible to the user. The experience stays focused on what someone is trying to accomplish, whether that’s finding a shirt, ordering dinner, catching up on the news, or simply asking a question.