Ingrify
Ingrify
Ingrify

Helping Indian shoppers understand ingredients instantly

Helping Indian shoppers understand ingredients instantly

Helping Indian shoppers understand ingredients instantly

Ingrify was created to address a growing frustration shared by everyday consumers that is ingredient labels are confusing, inconsistent, and often unreadable, making it hard to know what’s actually safe to eat.

I collaborated across teams to gather user feedback and created the app's user experience, interface with seamless access to brand values.

Ingrify was created to address a growing frustration shared by everyday consumers that is ingredient labels are confusing, inconsistent, and often unreadable, making it hard to know what’s actually safe to eat.

I collaborated across teams to gather user feedback and created the app's user experience, interface with seamless access to brand values.

Ingrify was created to address a growing frustration shared by everyday consumers that is ingredient labels are confusing, inconsistent, and often unreadable, making it hard to know what’s actually safe to eat.

I collaborated across teams to gather user feedback and created the app's user experience, interface with seamless access to brand values.

Timeline

Sep23 May 24

Role

Product Designer

Team

2 Product designers

1 Project Manager

4 developers

Skills & Tools

Figma

User interviews

Prototyping

User testing

MY ROLE & IMPACT
Research

Performed competitor analysis, created user-personas , conducted 6 user interviews and 2 rounds of user testing, and synthesized insights into actionable design ideas

10K + Downlaods

Ingrify app downloads
(August, 2025 - December, 2025).

Ingrify app downloads
(August, 2025 - December, 2025).

Design

I joined as the sole designer after the dev-prototype was build and feasibility was proven, I rebuilt the experience from the ground up. I established the products visual identity and keeping it intact throughout the design process.

5k+ MAU

Who regularly scan & engage
(August, 2025 - December, 2025).

5k+ MAU

Who regularly scan & engage
(August, 2025 - December, 2025).

10K + Downlaods

Ingrify app downloads
(August, 2025 - December, 2025).

ABOUT INGRIFY

In a market filled with food-scanning apps, most solutions quietly fail when Indian products arent in their databases. The earliest version focused on one thing using AI to read ingredient labels directly and make sense of them when barcodes fell short.
Once that feasibility was proven, I joined to help turn it into a real product shaping a scoring system that translates complex ingredients into a clear, human answer.
The goal wasnt to label food as good or bad, but to help people decide what works for them with clarity

PROBLEM

Barcode-based apps promise instant answers, but frequently break when products dont exist in local databases. When results do appear, generic health scores often ignore dietary preferences, allergies, and other eating habits. The challenge was designing a system that stayed useful even when data was incomplete, imperfect, or evolving.

CHALLENGE

How do we help everyday Indian shoppers understand ingredients instantly?

How do we help everyday Indian shoppers understand ingredients instantly?

How do we help everyday Indian shoppers understand ingredients instantly?

SOLUTIONS
SOLUTIONS

Barcode scan & Search

Barcode scan & Search

Barcode scan & Search

Personalized Onboarding customizes based on user dietary prefereneces and allergy

Personalized Onboarding customizes based on user dietary prefereneces and allergy

Personalized Onboarding customizes based on user dietary prefereneces and allergy

Personalized Onboarding

Personalized Onboarding

Personalized Onboarding

Personalized Onboarding customizes based on user dietary prefereneces and allergy

Personalized Onboarding customizes based on user dietary prefereneces and allergy

Personalized Onboarding customizes based on user dietary prefereneces and allergy

AI-powered Ingredient analysis

AI-powered Ingredient analysis

AI-powered Ingredient analysis

Ingredients scanning breaks down ingredient lists into simple, easy-to-understand insights for any product.

Ingredients scanning breaks down ingredient lists into simple, easy-to-understand insights for any product.

Ingredients scanning breaks down ingredient lists into simple, easy-to-understand insights for any product.

Lean UX approach within an agile product environment.

01
Discover

User needs through in-depth research and interviews.

02
Define

The challenge, project goals and the project scope.

03
Design

The solution based on insights and user pain points.

04
Iterate

Refining the solution through testing and constraints

05
Deliver

Shipping solutions and measuring impact through real-world usage and metrics.

Lean UX approach within an agile product environment.

01
Discover

User needs through in-depth research and interviews.

02
Define

The challenge, project goals and the project scope.

03
Design

The solution based on insights and user pain points.

04
Iterate

Refining the solution through testing and constraints

05
Deliver

Shipping solutions and measuring impact through real-world usage and metrics.

Lean UX approach within an agile product environment.

01
Discover

User needs through in-depth research and interviews.

02
Define

The challenge, project goals and the project scope.

03
Design

The solution based on insights and user pain points.

04
Iterate

Refining the solution through testing and constraints

05
Deliver

Shipping solutions and measuring impact through real-world usage and metrics.

DISCOVER
Desk Research

I conducted desk research using existing studies, industry reports, and public discussions around food labels in India. This helped validate whether the patterns observed in user surveys were isolated or reflective of a larger, systemic problem.

The findings consistently showed that ingredient labels are widely misunderstood, often ignored, and heavily influence poor food decisions especially among everyday Indian shoppers.

I conducted desk research using existing studies, industry reports, and public discussions around food labels in India. This helped validate whether the patterns observed in user surveys were isolated or reflective of a larger, systemic problem.

The findings consistently showed that ingredient labels are widely misunderstood, often ignored, and heavily influence poor food decisions especially among everyday Indian shoppers.

85%

text

20%

engage with ingredient or nutrition details

60%

find labels hard to understand

85%

text

20%

engage with ingredient or nutrition details

60%

find labels hard to understand

85%

text

20%

engage with ingredient or nutrition details

60%

find labels hard to understand

Insights from NIH

Surveys & Interviews

I ran a quick survey with 30+ participants and 4 user interviews with everyday Indian consumers. Even with a small sample size, clear patterns emerged…

I ran a quick survey with 30+ participants and 4 user interviews with everyday Indian consumers. Even with a small sample size, clear patterns emerged…

Key Insights

Key Insights
Key Insights
Clarity

Users want to read ingredients but chemical names, INS codes, and long lists make it nearly impossible.

Clarity

Users want to read ingredients but chemical names, INS codes, and long lists make it nearly impossible.

Clarity

Users want to read ingredients but chemical names, INS codes, and long lists make it nearly impossible.

Influence

When labels confuse them, users rely on branding and claims instead of actual nutrition facts.

Influence

When labels confuse them, users rely on branding and claims instead of actual nutrition facts.

Influence

When labels confuse them, users rely on branding and claims instead of actual nutrition facts.

Personalization

People with allergies and conditions want scores that match their health needs not generic ratings.

Personalization

People with allergies and conditions want scores that match their health needs not generic ratings.

Personalization

People with allergies and conditions want scores that match their health needs not generic ratings.

Simplicity

Most want quick Good vs Bad ingredient clarity first, with deeper breakdowns only when needed.

Simplicity

Most want quick Good vs Bad ingredient clarity first, with deeper breakdowns only when needed.

Simplicity

Most want quick Good vs Bad ingredient clarity first, with deeper breakdowns only when needed.

Willingness

81% said they would scan ingredients manually if barcode fails they just need a reliable, clear tool.

Willingness

81% said they would scan ingredients manually if barcode fails they just need a reliable, clear tool.

Willingness

81% said they would scan ingredients manually if barcode fails they just need a reliable, clear tool.

Relevance

People with allergies or dietary restrictions expect insights tailored to them, not one-size-fits-all ratings.

Relevance

People with allergies or dietary restrictions expect insights tailored to them, not one-size-fits-all ratings.

Relevance

People with allergies or dietary restrictions expect insights tailored to them, not one-size-fits-all ratings.

How others are doing it

I analyzed competitors like TruthIn, Yuka, TrashPanda and instead of comparing features, I focused on how these products guide users from scanning to interpretation. While strong mission messaging builds trust, most platforms fall short when labels become complex or culturally specific.


  • Combines barcode scanning with ingredient preference

  • Barcode availability and limited relevance for Indian products.

  • No Manual ingredient scanning.


  • Focuses on barcode scanning and ingredient summaries

  • Works well when products are in the database

  • Limited flexibility when barcodes are missing


  • Large product database and fast results

  • Relies heavily on health scores

  • Limited relevance for Indian products and INS-based labels

User Persona

It captures the strongest patterns from prospect users and guides initial design decisions while leaving room to evolve as more real user data comes in.

IDEATE
User flows

By prioritizing clarity and speed, the flow keeps the experience effortless and reduces decision fatigue.

DESIGN

Creating the brand:
A playful approach to healthy eating

User Testing & Iterations

Through 2 rounds of iteration, the product evolved based on real user feedback. These refinements helped improve clarity, trust, and engagement across the core scanning experience.

Through 2 rounds of iteration, the product evolved based on real user feedback. These refinements helped improve clarity, trust, and engagement across the core scanning experience.

Through 2 rounds of iteration, the product evolved based on real user feedback. These refinements helped improve clarity, trust, and engagement across the core scanning experience.

Bridging The Gap

AI ingredient analysis acted as a fallback when barcode scans failed, keeping the experience intact. However, repeated AI scans for the same products increased costs and introduced friction, prompting a more scalable solution. Thus, we introduced an Add Product flow which shifted AI analysis from a repeated, per-user cost to a shared system improvement, reducing long-term expenses while improving coverage for future users.

AI ingredient analysis acted as a fallback when barcode scans failed, keeping the experience intact. However, repeated AI scans for the same products increased costs and introduced friction, prompting a more scalable solution. Thus, we introduced an Add Product flow which shifted AI analysis from a repeated, per-user cost to a shared system improvement, reducing long-term expenses while improving coverage for future users.

AI ingredient analysis acted as a fallback when barcode scans failed, keeping the experience intact. However, repeated AI scans for the same products increased costs and introduced friction, prompting a more scalable solution. Thus, we introduced an Add Product flow which shifted AI analysis from a repeated, per-user cost to a shared system improvement, reducing long-term expenses while improving coverage for future users.

DELIVER
Success Metrics

The success of this app and it’s features were measured by:

📈 Adoption & Engagement

Growth in active users and scan frequency, indicating that users found real value in scanning products during everyday shopping.

⭐ Trust & Satisfaction

High app ratings and repeat usage, validating that ingredient insights were clear, credible, and easy to act on.

🧠 Coverage & Cost Efficiency

Improved product database coverage through user-added products, reducing repeated AI analysis costs while scaling insights for future users.

Key Learnings
Designing within real business constraints

One major learning was understanding that not every technically possible solution is sustainable. While AI-powered ingredient analysis helped bridge gaps in Indian product coverage, it also introduced recurring costs at scale. This pushed us to rethink the system and design a more sustainable solution through community-driven product additions balancing user value with business viability.

One major learning was understanding that not every technically possible solution is sustainable. While AI-powered ingredient analysis helped bridge gaps in Indian product coverage, it also introduced recurring costs at scale. This pushed us to rethink the system and design a more sustainable solution through user-driven product additions balancing user value with business viability.

Designing for imperfect data

Working with third-party databases meant scan failures, mismatches, and incomplete results were inevitable. Instead of hiding these limitations, we designed clear fallbacks ingredient scan, AI analysis, and user reporting to keep the experience intact even when the system wasn’t perfect.

I’m here to help you create smth exceptional !

I’m here to help you create smth exceptional !

I’m here to help you create smth exceptional !

DEPLOY

Success Metrics

The success of this app and it’s features were measured by:

📈 Adoption & Engagement

Growth in active users and scan frequency, indicating that users found real value in scanning products during everyday shopping.

⭐ Trust & Satisfaction

High app ratings and repeat usage, validating that ingredient insights were clear, credible, and easy to act on.

🧠 Coverage & Cost Efficiency

Improved product database coverage through user-added products, reducing repeated AI analysis costs while scaling insights for future users.

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