The Biggest Women AI Hackathon by ADA

To build the world’s most intelligent growth platform for every client

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To build the world’s most intelligent growth platform for every client

Lorem Epsom

We build the systems that think, react, and evolve, helping brands put their business exactly where their customers are. Our technology combines data, AI, and experience design to drive precision across every stage of ecommerce growth.

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We’re focused on building something real: tools and services that make growth smarter, faster, and more predictable.

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To build the world’s most intelligent growth platform for every client

Connect

Meet other women in tech, collaborate, and grow your network.

Create

Build and showcase your AI ideas in a real-world challenge.

Shine

Get visibility and recognition for your skills and innovation.

GirlCode 2025 •

the Biggest Women AI Hackathon by ADA •

What can you win?

USD 2,000

Grand Prize

Take home the top spot and a cash prize of USD 2,000 for your winning innovation.

USD 800

1st Runner-Up

The next best team walks away with USD 800 and major recognition.

Top 5 Finalists

Each finalist team will also have the opportunity to kickstart their career with an internship at ADA.

Hackathon Timeline

30 October –
13 November

Applications closed

Registration Period

Submit your entry and get ready to show your ideas.

18 November

Shortlisted Participants Announced

Selected participants will be contacted via WhatsApp.

20 November

Briefing Call & Q&A

Join ADA and our expert judges to learn all about the challenge.

24 – 28 November

5-Day Hackathon Period

Develop and refine your AI-powered solution.

28 November

Submission Deadline

Submit your presentation deck (PPT) and short video (MP4).

28 November –
15 December

Evaluation Round

Judges review all submissions to shortlist the top 5 finalists.

15 December

Finalist Announcement

Top 5 teams revealed.

18 December

winner announcement

Final Pitch & Winner Announcement

Finalists pitch live at ADA India office — travel and accommodation covered by ADA.

Ready to Make Your Mark?

Show the world what women in tech can do.

Submission closed

FAQ

Sample Dataset Schema

Dataset 1 (Insights of Device IDs / Consumers) fields

Attribute Group	Attribute	Data type	Sample Data	Description
Data Period	quarter	String	2024Q1	The quarter period when the device IDs are captured
geoBoundaries	adm0	String	Malaysia	The geoBoundaries of an area where device IDs are captured at total country level
	adm1	String	Kelantan	The geoBoundaries of an area where device IDs are captured at first-level subnational boundaries
	adm2	String	Kota Bharu	The geoBoundaries of an area where device IDs are captured at second-level subnational boundaries
	adm3	String	Pengkalan Chepa	The geoBoundaries of an area where device IDs are captured at subdivsion of second-level boundaries
Identifier	device_id	String	d6f65fb0-0fc8-xxxx-xxxx-ed128ab73809	Anonymous but unique Mobile Advertising ID (MAID)
Location	Home and Work	Number (Percentage)	74.3%	The device IDS observed to work and live in a particular coverage area
	Home not Work	Number (Percentage)	14.8%	The device IDS observed to live in a particular coverage area but work out of the area
	Work not Home	Number (Percentage)	10.8%	The device IDS observed to work in a particular coverage area but live out of the area
Online Interests / Persona	Bookworms	Number (Percentage)	16.3%	The device IDs categorised as bookworm from inferred interests based on reading apps used, including books, news & magazines, comics, etc.
	BudgetManager	Number (Percentage)	19.0%	The device IDs categorised as budget manager from inferred interests based on budgeting apps used to manage spending
	CreativeCrowd	Number (Percentage)	17.5%	The device IDs categorised as creative crowd from inferred interests based on graphics, art & design, photo and video editing apps used
	FitnessWarrior	Number (Percentage)	0.5%	The device IDs categorised as fitness warrior from inferred interests based on fitness apps used, such as step counters or home training programs
	Gamers	Number (Percentage)	55.5%	The device IDs categorised as gamer from inferred interests based on gaming apps used, such as strategy, role play, action, word, puzzle, etc.
	HealthJunkie	Number (Percentage)	9.2%	The device IDs categorised as health junkie from inferred interests based on health management or medical apps used apps for monitoring of health
	PassiveEntertainer	Number (Percentage)	39.4%	The device IDs categorised as passive entertainer from inferred interests based on music or video streaming apps used
	PhoneEnthusiast	Number (Percentage)	38.4%	The device IDs categorised as phone enthusiast from inferred interests based on apps used for themes or personalisation, such as ringtone, keyboard, wallpaper, emojis, etc.
	SocialButterfly	Number (Percentage)	26.8%	The device IDs categorised as social butterfly from inferred interests based on social apps used, such as communication, sharing & dating apps
	WealthManager	Number (Percentage)	1.7%	The device IDs categorised as wealth manager from inferred interests based on financial, business, stock market or investment apps used
Places of Interest	Education POIs	Number (Percentage)	12.9%	The device IDs seen located at educational POIs such as school, college, university, etc.
	Healthcare POIs	Number (Percentage)	11.6%	The device IDs seen located at healthcare POIs such as hospital, clinic, etc.
	Mall POIs	Number (Percentage)	7.9%	The device IDs seen located at mall POIs
	Sports POIs	Number (Percentage)	9.5%	The device IDs seen located at sports POIs such as golf course, stadium, etc.
	Travel POIs	Number (Percentage)	10.9%	The device IDs seen located at travel POIs such as airport, apron, etc.
Places of Interest - Coffee	CoffeeBean	Number (Percentage)	69.2%	The device IDs seen located at Coffee Bean coffee shop
	SanFrancisco	Number (Percentage)	1.7%	The device IDs seen located at San Francisco coffee shop
	Starbucks	Number (Percentage)	11.6%	The device IDs seen located at Starbucks coffee shop
	ZusCoffee	Number (Percentage)	17.5%	The device IDs seen located at Zus Coffee coffee shop
Places of Interest - Convenience Store	7Eleven	Number (Percentage)	65.1%	The device IDs seen located at 7 Eleven convenience store
	99Speedmart	Number (Percentage)	18.2%	The device IDs seen located at 99 Speedmart convenience store
	FamilyMart	Number (Percentage)	4.0%	The device IDs seen located at Family Mart convenience store
	KKMart	Number (Percentage)	6.6%	The device IDs seen located at KK Mart convenience store
	MyNews	Number (Percentage)	6.1%	The device IDs seen located at My News convenience store
Places of Interest - Petrol Station	Petron	Number (Percentage)	29.8%	The device IDs seen located at Petron petrol station
	Petronas	Number (Percentage)	38.5%	The device IDs seen located at Petronas petrol station
	Shell	Number (Percentage)	31.7%	The device IDs seen located at Shell petrol station
Socio-economic Demographics - Affluence	AffluenceHigh	Number (Percentage)	45.0%	The device IDs which are categorised into high affluence level group based on top 30%
	AffluenceLow	Number (Percentage)	24.3%	The device IDs which are categorised into low affluence level group based on bottom 30%
	AffluenceMid	Number (Percentage)	30.7%	The device IDs which are categorised into mid affluence level group based on mid 40%
Socio-economic Demographics - Age Group	Age18-24	Number (Percentage)	26.5%	The device IDs which are categorised into age group of 18-24 years old
	Age25-34	Number (Percentage)	16.7%	The device IDs which are categorised into age group of 25-34 years old
	Age35-49	Number (Percentage)	45.8%	The device IDs which are categorised into age group of 35-49 years old
	Age50+	Number (Percentage)	11.0%	The device IDs which are categorised into age group of 50 years old & above
Socio-economic Demographics - Gender	GenderF	Number (Percentage)	47.0%	The device IDs which are categorised into female gender group
	GenderM	Number (Percentage)	53.0%	The device IDs which are categorised into male gender group
Telco Profile - Carrier	Telco1	Number (Percentage)	37.2%	The device IDs captured to be using cellular mobile carrier brand of Telco1
	Telco2	Number (Percentage)	22.0%	The device IDs captured to be using cellular mobile carrier brand of Telco2
	Telco3	Number (Percentage)	20.9%	The device IDs captured to be using cellular mobile carrier brand of Telco3
	Telco4	Number (Percentage)	19.9%	The device IDs captured to be using cellular mobile carrier brand of Telco4
Telco Profile - Device Brand	Apple	Number (Percentage)	13.5%	The device IDs captured to be using device brand of Vivo
	Huawei	Number (Percentage)	5.1%	The device IDs captured to be using device brand of Vivo
	Oppo	Number (Percentage)	21.4%	The device IDs captured to be using device brand of Vivo
	Samsung	Number (Percentage)	16.7%	The device IDs captured to be using device brand of Vivo
	Vivo	Number (Percentage)	24.3%	The device IDs captured to be using device brand of Vivo
	Xiaomi	Number (Percentage)	19.0%	The device IDs captured to be using device brand of Vivo
Telco Profile - Device Price Tier	PriceHigh	Number (Percentage)	17.8%	The device IDs captured to be using device categorised under high price tier based on top 30%
	PriceLow	Number (Percentage)	57.9%	The device IDs captured to be using device categorised under low price tier based on bottom 30%
	PriceMid	Number (Percentage)	24.3%	The device IDs captured to be using device categorised under mid price tier based on mid 40%
Update Date	shapefile	String	MY030404	The polygonal geofencing of each individual shape on the dashboard map where device IDs are captured

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Dataset 2 (Attributes of Device IDs / Consumers) fields

Attribute Group	Attribute	Data type	Sample Data	Description
Data Period	quarter	String	2024Q1	The quarter period when the device IDs are captured
Identifier	adaid	String	d6f65fb0-0fc8-xxxx-xxxx-ea4d07873d21	Anonymous but unique Mobile Advertising ID (MAID)
Location	country	String	Malaysia	The device IDs captured at a particular country - Malaysia, Thailand, Indonesia or Philippines
	home_latitude	Number (Coordinate)	2.71	The predicted home location coordinate of latitude for device IDs captured during nighttime if applicable
	home_longitude	Number (Coordinate)	101.93	The predicted home location coordinate of longitude for device IDs captured during nighttime if applicable
	office_latitude	Number (Coordinate)	3.14	The predicted work location coordinate of latitude for device IDs captured during weekdays daytime if applicable
	office_longitude	Number (Coordinate)	101.69	The predicted work location coordinate of longitude for device IDs captured during weekdays daytime if applicable
	shapefile	String	MY090101;MY090601;MY090606;MY090802	The polygonal geofencing of each individual shape on the dashboard map where device IDs are captured
Online Interests / Persona	persona_group	String	Entertainment Junkies;Gamers;Social Butterflies	The device IDs categorised for personas derived from inferred interests based on apps usage, including Bookworms, Budget Manager, Creative Crowd, Fitness Warrior, Gamers, Health Junkie, Passive Entertainer, Phone Enthusiast, Social Butterfly or WealthManager
	primary_persona	String	Entertainment Junkies	The primary persona of device IDs derived from the blend of personas
Places of Interest	coffee_brand_group	String	Coffee Bean;Starbucks	The device IDs seen located at coffee shop of Starbucks, Coffee Bean, Zus Coffee, San Francisco, etc.
	convenience_store_brand_group	String	7Eleven;99 Speedmart	The device IDs seen located at convenience store of KK Mart, My News, 99 Speedmart, 7 Eleven, Family Mart,etc.
	offline_category_group	String	Education;Healthcare;Sports;Travel	The device IDs seen located at educational POIs cateogories of education, healthcare, mall, sports, travel, etc.
	petrol_brand_group	String	Petron;Petronas;Shell	The device IDs seen located at petrol station of Petronas, Shell, Petron, etc.
Socio-economic Demographics	affluence	String	Mid	The device IDs which are categorised into low, mid & high affluence level group based on bottom 30% : mid 40% : top 30%
	age	String	35-49	The device IDs which are categorised into age group of 18-24, 25-34, 35-49 & 50 years old & above
	gender	String	F	The device IDs which are categorised into male / female gender group
Telco Profile	carrier	String	Celcom	The device IDs captured to be using cellular mobile carrier brand of Celcom, Digi, Maxis, U Mobile, etc.
	device_brand	String	Xiaomi	The device IDs captured to be using device brand of Vivo, Samsung, Xiaomi, Oppo, Huawei, Apple, etc.
	device_name	String	Xiaomi Poco X4 Pro 5G	The device IDs captured to be using device of a particular model
	device_pricetier	String	Mid	The device IDs captured to be using device categorised under low, mid & high price tier based on bottom 30%, mid 40% & top 30%
	platform	String	Android	The device IDs captured to be using device of a particular platform (operating system)

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Dataset 3 (Location Pings of Device IDs / Consumers) fields

Attribute Group	Attribute	Data type	Sample Data	Description
Data Period	quarter	String	2024Q1	The quarter period when the device IDs are captured
Identifier	adaid	String	d6f65fb0-0fc8-xxxx-xxxx-ea4d07873d21	Anonymous but unique Mobile Advertising ID (MAID)
Location	country	String	Malaysia	The device IDs captured at a particular country - Malaysia, Thailand, Indonesia or Philippines
Location Ping	connection_type	Number (Label)	0	The device IDs captured to be using device with connection type of 0=Unknown; 1=Ethernet ; 2=WiFi, 3=Cellular network but unknown generation ; 4=Cellular 2G ; 5=Cellular 3G ; 6=Cellular 4G or 7=Cellualr 5G
	latitude	Number (Coordinate)	4.54127	The location coordinate of latitude when device IDs captured at that time
	longitude	Number (Coordinate)	101.065498	The location coordinate of longitude when device IDs captured at that time
	timestamp	Number (UNIX timestamp)	1706068120	The timestamp when device IDs captured at that time (in the format of UNIX timestamp - Epoch time)
	bundle	String	com.litatom.app	The app bundle used used by device IDs at the time
	asn	String	Litmatch - Make new friends	The name of app being used by device IDs at the time
	ip	String	180.75.254.92	The IP address of device IDs captured at that time
	location_tag	String	ip_location	The source of location info which is derived from either GPS (gps_location) or IP address (ip_location)

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Values Of Brand Preferences In Sample Datasets By Country

	Malaysia	Thailand	Indonesia	Philippines
The top telco brands in the market	CelcomDigi, Maxis, UMobile	TrueMove, AIS, TOT	Telkomsel, Indosat, XL, Smartfren	Smart, Globe, Dito
The top coffee shop brands in the market	Starbucks, Coffee Bean, Zus Coffee, San Francisco Coffee	Starbucks, Café Amazon, True Coffee	Starbucks, Kopi Kenangan, Excelso	Starbucks, Seattles Best, Coffee Beanery
The top convenience store brands in the market	KK Mart, MyNews, 99 Speedmart, 7 Eleven, Family Mart	CP Freshmart, 7 Eleven, Family Mart	Indomaret, Alfamart, CircleK	AllDay, Ministop, 7 Eleven
The top petrol stations brands in the market	Petronas, Shell, Petron	PTT, Shell, Bangchak	Pertamina, Shell, SPBUvivo	Caltex, Shell, Petron

Who can participate in this hackathon?

GirlCode is open exclusively to female university students in India from selected partner universities who are passionate about data, AI, and technology.

How does the selection process work?

All interested participants must register through our official form. ADA will review applications based on the answers provided, and only shortlisted participants will be contacted via WhatsApp with further details.

Is there a registration fee?

No, GirlCode is completely free to join.

How do I register?

You must register through our official registration form [here]. Every participant, whether in a team or solo, must complete the form individually.

How many team members are allowed per team?

You can form a team of 2 to 5 people. If you prefer, you can also register and participate as a solo participant (team of 1).

Do all team members need to register?

Yes. Every single member of your team must complete the registration form themselves.

I don't have a team. Can I still participate?

Absolutely! You can register as a solo participant.

How will I know if I’ve been shortlisted?

If you’re shortlisted, you’ll receive a WhatsApp message from the GirlCode team with the official information deck, submission details, and briefing call invite.

What happens after I’m shortlisted?

Shortlisted participants will receive a calendar invite for a briefing call, where they’ll learn everything about the hackathon and can ask questions directly to our expert judges.

What if I miss the briefing call?

Don’t worry! You’ll receive the contact number of our person-in-charge (PIC) so you can ask questions anytime throughout the 5-day hackathon.

How do I submit my solution?

Submissions must include:

A presentation deck (PPT) outlining your idea and solution.
A short MP4 video explaining your concept.

Submission links will only be shared with shortlisted participants.

What are the judging criteria?

Solutions will be evaluated based on creativity, impact, technical execution, and how effectively they address the chosen problem statement.

What are the prizes?

There will be one Grand Prize winner and one Runner-Up. The top 5 teams will be invited to the ADA India office for a face-to-face pitch session with our judges — ADA will cover travel and accommodation costs.

Are there any career opportunities?

Yes! All 5 finalist teams will have the chance to kickstart their career at ADA through an internship opportunity.

Where can I find the timeline?

You can find the full hackathon timeline on our website under the “Hackathon Timeline” section.

What will the problem statements be about?

Problem statements will focus on real-world AI and data challenges, and will be shared with shortlisted participants in the official hackathon deck via WhatsApp.

Solutions

About

Resources

The Biggest Women AI Hackathon by ADA

To build the world’s most intelligent growth platform for every client

To build the world’s most intelligent growth platform for every client

Lorem Epsom

To build the world’s most intelligent growth platform for every client

Connect

Create

Shine

What can you win?

Hackathon Timeline

FAQ

Sample Dataset Schema

Dataset 1 (Insights of Device IDs / Consumers) fields

Dataset 2 (Attributes of Device IDs / Consumers) fields

Dataset 3 (Location Pings of Device IDs / Consumers) fields

Values Of Brand Preferences In Sample Datasets By Country

Sample Queries

Who can participate in this hackathon?

How does the selection process work?

Is there a registration fee?

How do I register?

How many team members are allowed per team?

Do all team members need to register?

I don't have a team. Can I still participate?

How will I know if I’ve been shortlisted?

What happens after I’m shortlisted?

What if I miss the briefing call?

How do I submit my solution?

What are the judging criteria?

What are the prizes?

Are there any career opportunities?

Where can I find the timeline?

What will the problem statements be about?