Your personal AI powered travel planning assistant. Say farewell to the stress of travel planning.

Problem Space


In today's digital era, the abundance of travel opportunities has made trip planning a time-consuming and overwhelming task for many travellers.  The sheer volume of travel information available online often leads to information overload, making it challenging to find relevant and reliable details for travel planning. 

A study by Expedia has shown that travellers spend an average of 30 hours planning their vacations and visit numerous websites during the research process. Decision-making difficulties arise when trying to select a destination, understand and compare different attractions or experiences, and create an itinerary that aligns with individual preferences and budget. 

Furthermore, a survey conducted by Phocuswright revealed that 42% of survey respondents felt overwhelmed by the amount of travel information available, and around ~40% of the survey participants found it challenging to create an itinerary that met their preferences and budget. These statistics illustrate the significant impact of information overload on travellers, leading to increased anxiety and indecisiveness.

User Research Insights and Pain Points

To gain deeper insights into the trip planning process and validate the problem, we conducted customer interviews and surveys with approximately 20 users. The generative research revealed valuable information about how travelers plan their trips and shed light on the pain points they experience:

  • 90% of surveyed users spend upwards of 15 hours going through multiple sources (blogs, vlogs, social media, travel websites) to create a high-level plan for their desired destination.
  • 50% of users prefer having a detailed itinerary, which takes them multiple days to create after extensive online research.
  • 40% of users want to explore new and unique destinations beyond popular tourist hotspots but struggle to find such destinations that match their desired vacation type due to lack of  time and means to discover such places.
  • 80% of surveyed users responded that having a ready-made detailed itinerary to be the most valuable feature in a travel planning app.
  • 40% of users would appreciate an app that suggests vacation ideas based on their stated preferences.

These insights highlight the time-consuming nature of trip planning, the desire for both high-level and detailed itineraries, the need for discovering unique destinations, and the value users place on ready-made itineraries and personalized vacation suggestions.

Competitive Analysis

Before coming up with our own solution, we also conducted some preliminary analysis of the competitive landscape to understand how crowded the solution space for this product is, and to get an idea for what features our closest competitors have or lack.

From our analysis, we discovered that there are no offerings from our closest competitors that reduce the burden of travel planning by automating itinerary generation. Our existing competitor’s solutions only allow users to manually create an itinerary and save it for future reference. 

Moreover, these existing products fail to provide another useful feature: suggesting potential travel destinations based on specific constraints set by users, such as the month of travel or the number of vacation days available. Therefore, we strongly believe that there is a vast untapped potential in this domain, presenting an opportunity for us to be pioneers in delivering a solution that not only saves users' time but also enhances their vacation planning experience.

Market Sizing

Our travel planning app is designed to cater to individuals who enjoy travelling and seek an efficient and personalized itinerary planning experience. The target audience includes frequent travellers, adventure enthusiasts, and vacationers who value convenience and time-saving solutions. 

Total Addressable Market 

  • Population between 18-65: As of 2023, there are approximately 6.2 billion people in the world within the age range of 18 to 65. 
  • Proportion with middle-to-high income level: According to data from the World Bank, around 32% of the population within this age range have a middle-to-high income level, indicating their potential to afford multiple trips a year. 6.2 billion x 0.32 = 1.984 billion people.
  • Percentage of frequent travellers: Based on various surveys from TripAdvisor and other independent agencies, it is estimated that around 30% of the global population within the age range of 18 to 65 takes frequent trips. 1.984 billion x 0.3 = 595.2 million people.
  • Group size and trip planning: Assuming an average group size of 4 people travelling together, with one person taking charge of planning the itinerary, we can estimate the number of potential users who would be interested in making an itinerary. 595.2 million / 4 = 148.8 million potential users.
  • Preference for well-planned itineraries: Combining data from surveys and user research, 80-90% of people who take trips prefer to have a properly planned itinerary. Taking the conservative estimate, we consider that 80% of the potential users would value a well-planned itinerary. 148.8 million x 0.8 = 119.04 million people.

Therefore, based on our estimation, we estimate the Total Addressable Market (TAM) for our travel planning app to be approximately 119.04 million people.

Serviceable Available Market and Share of Market

Taking into account our marketing capabilities and geographic reach at this point, we estimate that we can effectively target and serve 20% of the TAM. Therefore, our SAM would be 22.4 million potential customers.

Analyzing the competitive landscape and our unique features, we anticipate capturing 15% of the SAM as our market share. Thus, our estimated Share of Market (SO

Solution Space

Explanation of the Solution

Our proposed solution is to develop an intuitive AI-powered web application that simplifies the trip planning process and provides personalized recommendations to users. The web app will feature a user-friendly interface accessible from desktop devices, enabling users to effortlessly input their desired destination and preferences.

Utilizing the power AI, the app will automatically generate a detailed day-wise itinerary customized to each user's preferences. By doing so, it eliminates the need for users to spend countless hours conducting online research and planning. The app will automatically curate a selection of attractions, activities, and dining options that align with the user's interests, budget, and travel style.

To foster a sense of adventure and discovery, the app will also incorporate a recommendation engine that suggests unique and lesser-known vacation destinations based on the user's preferences. By presenting these off-the-beaten-path options, users can explore new and exciting places beyond the typical tourist hotspots, expanding their travel horizons.

Based on user pain points collected from our initial research, we prioritized the features we want to deliver for our MVP based on the following parameters:

  • Impact - how useful it is to an individual user? 
  • Reach - how many users would it benefit?
  • Effort - how long would it take to build?
  • Dependency - does one feature depend on another?

MVP Goals

In the MVP for our product, we aim to help users:

  1. Automatically create itineraries: The app will generate personalized itineraries for users based on their specified destination and preferences for trip length, pace, and the type of trip they desire (sightseeing, adventure activities/hiking, cultural, balanced). This feature aims to save users time and effort by automating the itinerary creation process.
  1. Suggest potential destinations: The app will suggest potential destinations for users based on their preferences, such as the number of vacation days, month of travel, their city of residence, and the desired vacation destination type. This feature aims to inspire and assist users in discovering new and suitable travel destinations.
  1. Save and customize auto-suggested itinerary: Users will have the ability to save the auto-generated itinerary and customize it according to their preferences. This feature empowers users to tailor the itinerary to their specific needs and preferences, providing flexibility and personalization.

Future Goals

Beyond the MVP, we would be doing more user testing on what features deliver the most value to the end customer. At a high level, we have the following goals in mind for the post-MVP version of our product:

  1. Showing a map view for the places chosen on the itinerary. 
  2. Ability to forward existing flight/hotel reservation emails to the app so that all trip details can be saved in one place. 
  3. Auto-adjust the itinerary based on the flight timings, and exact location of the hotels for each night’s stay.
  4. Integration with GPT plugins such as Expedia and Kayak to automatically recommend hotel options for each night’s stay based on budget, price and the planned itinerary, and provide affiliate links to book these hotels.
  5. Android/iOS mobile app. Saved trips and maps can be retrieved offline.  
  6. Monetize the product using an affordable yet profitable pricing model. 

Lo-fi Mockups

Hi-fi Mockups

Iterative Design Learnings

We came up with the user flow for the app after completing our initial user research regarding what features users valued the most in an app that would help them plan their travel.  

For our low-fidelity wireframes, we collected user feedback from within our team, and from our pod mentor. Our learnings at this stage were how to capture user preferences in the simplest manner possible, and how to display  a detailed itinerary in a manner that is easy to understand and navigate around. 

After coming up with the high-fi wireframes and the clickable prototype, we opened our product up for more thorough user testing from external users. This user testing revealed a number of UI/UX improvements that needed to be made. Some of the feedback we chose to act on:

  • A lot of users said that they didn’t fully understand the purpose and difference between the two core features (Trip Planner/Explore Destinations). So, we decided to add screens that guide the user to select between the two core features (Trip Planner/Explore Destinations) based on what they wanted to do.
  • Some users said that they didn’t understand what the number (2.6 miles) between two places meant. To remedy this, we decided to add dotted lines between the places, and some additional text to indicate that this is the driving/walking distance between two points on the itinerary.
  • A lot of users also mentioned that they would like to have the ability to add, delete and move places on the itinerary so that they can completely customize it before saving it. We decided to add the necessary UI elements on the Trip Planner page to support this request.
  • Users also requested the ability to select multiple options when selecting certain preferences (for instance, selecting both ‘domestic/international’ instead of just 1). 

Implementation Details

Where is it hosted? 

Our website is hosted on AWS Amplify because AWS is more reliable than other third party hosting sites and it also provides a free tier which was good enough for our MVP.

What is your tech stack?

We choose to use Next.js as it is the preferred way now to create react projects. Also Next is a full stack framework, so the backend is also implemented in the same project. For the database we are using firebase because it makes the job very easy to store data using their serverless functions.

What was the hardest part of development?

The hardest part of development was to work with Openai api. We are fetching the response and showing it to the frontend. Openai gives responses in string or character format and we need to structure the data before showing it in our frontend. But sometimes gpt api does not give the response in the manner in which we have structured the data. So the response comes weird and random quite often.

Does your app have any scaling issues?

So our website uses openai api for generating the response. We are using their free tier 3.5 gpt model with a limitation of request up to  5 dollars. So we are not able to get the latest gpt-4 model which gives a more refined and u pto-to-date response as it is a paid feature.
We are using AWS free tier which sometimes cuts off the response coming from openai.

Also we are using Google maps api in our website but we are limited to only 200 dollars.

 So in order to make this a production grade app we need to upgrade to their paid plans respectively.

What are some key takeaways?

Never underestimate the power of teamwork and consistency. Teamwork is very important when it comes to building a quality product. I would like to thank my teammates for being consistent, cooperative, and responsive.


Product Manager Learnings:

Abhinav Kothiala

Colab offered me an exceptional opportunity to work with a cross functional team of software developers and designers. It emulated an actual company’s product pod in many ways - we collected user feedback at every step of the process to iterate on our product, prioritized and de-scoped features as needed, and followed scrum guidelines to aid timely execution. 

Through my experience I learnt:

  • The right mindset to have as a PM. I learnt that it is critical to spend more time on exploring the problem space very well, and understand the whats and whys of what we’re trying to build.
  • How to empathize with users and conduct meaningful, unbiased customer interviews. 
  • How to prioritize which features to build and make difficult tradeoffs where necessary.
  • Lastly, I learnt how to lead and influence a team of developers and designers to ship a product! This was the most satisfying and enjoyable part of my journey as a product manager. 

Designer Learnings:

Nina Nguyen

I'm grateful for this opportunity from Design Buddies x Co.Lab for a chance to experience cross-functional collaboration. I also got to learn more about product manager and developer roles and their perspectives during the design process. 

Some important takeaways include:

  • Communicating early is encouraged for ongoing feedback and better understanding between team members.
  • Figma file organization is necessary for a smooth design handoff.
  • Learning and deciding what tradeoffs will need to be made to deliver on time.

Developer Learnings:

Shuvam Santra

During the 8-week colab program, I have had the opportunity to work as a software developer along with designer and product manager which was completely a new experience for me. To sum up my learnings from this program, I will like to add: 

  • Communication: I strengthened my communication skills by effectively conveying technical concepts and  progress updates with all my team-mates. This helped foster a collaborative environment and ensured everyone was on the same page throughout the project.
  • Collaboration: I actively participated in brainstorming sessions with the project manager, designer,mentor and the other developer, sharing and exchanging ideas to shape project’s direction and goals.
  • Adaptability: I learned to adapt to changing requirements and priorities by embracing an agile development approach. This involved being flexible and responsive to feedback and incorporating necessary iterations to improve the product.
  • Bug fixing: Throughout the development process, I delivered skills in identifying and resolving software-related bugs and errors. This involved throughout debugging and troubleshooting to ensure a stable and reliable product
  • Time management: Throughout the project, I honed my time management skills by setting small goals and milestones, prioritising tasks effectively, and delivering work within given timelines.

Developers Learnings:

Akash Kishore


Throughout my experience at CO.LAB, I gained valuable experience in building an itinerary website with optimised place suggestions and distance calculation. I developed strong communication and collaboration skills, which were crucial in transforming initial ideas into a functional MVP.

Through my experience I learnt:

  • Sharing and exchanging ideas, in brainstorming sessions with the product manager,        designer, mentor, and developer.
  • Utilised acquired knowledge to effectively implement the essential features.
  • Throughout the development process, identifying and resolving software-related bugs and errors.

Full Team Learning

Finding a meeting time that worked for everyone was a challenge for the entire team, as every member is located in a different timezone. We quickly learned how to work around this challenge by communicating more asynchronously, and setting up 1:1s instead of full team meetings in the middle of the week. We also learnt how to adapt and be agile in changing the direction for the features to implement for our app taking into account the user feedback, technical strengths of the developers in the team, as well as the schedule.