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What do I eat at this restaurant? Confused? Ask Ketchupp

Sindhu Kashyaap
29th Jan 2016
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Foodtech, food recommendations, and food delivery seem all the rage these days. Even with the number of players in this space, there always seems to be room for more. The market appears to be shifting towards recommendations made via social and chat platforms like Siri and Google Now. Pune-based Quinto and the latest entrant – Gurgaon-based Ketchupp are focussed on giving recommendations.

Both startups believe that while there are several platforms that focus on aggregating different restaurants, chefs, curation of menus and food delivery, few actually help the consumers get the food they need at the restaurants.

Everyone has faced confusion when deciding what to order or what a restaurant is famous for. Ketchupp aims to help solve this problem. Ketchupp says it enables its users to have the best meals every time anywhere. Based on the same principle as most recommendation apps, Ketchupp uses natural language programming and consumer recommendations to offer insights.

The team says their vision is to be a personalised recommendation platform for dishes where users are recommended dishes basis their personal preferences, behaviours, climate and demographics.

Yourstory-Ketchupp
The core team @ Ketchupp

Starting by the book

Narender Kumar and Chirag Taneja had been friends for a while, and like most friends, they liked exploring the best places to eat around Delhi. Chirag went through a surgery, which reduced his appetite. Being food buddies, the duo would end up eating together most of the time and almost always found a blind spot when it came to what to order at different restaurants.

While most platforms like Zomato would recommend or give them options on the best restaurant to go to, the duo didn't know what to eat there. They decided to write a book on where to find the best food in the city.

However, they found the task time-consuming and un-scalable. It was then that they decided to invite people from across the city to explore different restaurants and give their insights based on their dining experiences. They formed a Facebook group called the KetchuppGang.

“On the group, people shared their experiences, opinions and views on different dishes at different restaurants. We thought why not automate the process and get real-time results; thus the idea of building a mobile app came into being,” says 30-year-old Chirag.

Rahul Makkar, a hotel management graduate with a cult social media following, and deep insights about food joined the team early on. Bipin Taneja came onboard to handle restaurant alliances and events.

Knowing that they needed to build a strong tech team, they brought Abdul Khalid an IIT Delhi alumnus on board. He decided to take the plunge from Housing to join in as the co-founder of Ketchupp.

“All of us share a common theme of discovering #YahanBestKyaMiltaHai. Most of all, we believe one can find the best dishes only if one has the drive to cook themselves. So one of our key requirements in the job description is that you should know how to cook or should have a chef in you,” adds Chirag.

Workings of the product

Their MVP aggregated the views of different people, which helped consumers find the best dishes around them at different restaurants. So when someone asks the app what they can eat at any place, the best recommendations come up.

Citing an example, Chirag says, if someone wants to have the best masala dosa in their area, they can ask for it and instantly get results for masala dosa in their location based on people’s recommendations.

Ketchupp aggregates all social media feeds and social blogs, analyses them and makes instant recommendations. These recommendations are in turn validated by your trusted friend network, bringing credibility to the recommendations.

“For instance, Berry Pulao is a “Must Try” at Soda Bottle Opener Wala is an outcome of our algorithm, which is also validated by your friend network who use the app and recommend it,” says Chirag.

So like in the example, if the Berry Pulao has been recommended by the algorithm, the user also gets a list of friends in his or her social network who recommend the same dish.

Growth and traction

The team launched their Android app in November 2015, which has since been downloaded more than 1,000 times in the Gurgaon area. Ketchupp will be launching their iOS app soon.

Ketchupp claims to have partnered with all food festivals in Delhi like the Asian Hawkers Market, Palate Fest 2015 and Delhi Cocktail Week, where they claim to have given the fest a mobile outlook and helped visitors decide #YahanBestKyaMiltaHain.

The team is currently bootstrapped and has raised a small round of investment from family and friends. They are planning to raise funds from investors who can also offer mentorship and guidance.

The team is planning to increase the number of dishes they recommend from 3,000 to 15,000 soon and then further increase this number exponentially to 2,00,000 dishes in a year’s time. In the next three months, they will cover more cities in North India focussing on delicacies from Amritsar, Ludhiana, Chandigarh, Jammu and Kashmir, Lucknow, Shimla, and Manali. The team also plans to be a lead-generation platform for the likes of Zomato and Swiggy.

YourStory take

The Siri for X, Google Now for X, chat based and other recommendation engines seem to be ruling the roost. And they seem to have hit that most popular of all startup ideas – food.

In October last year, the delivery startup MagicTiger acquired AI-based startup Zoyo to help users shop over chat. With this acquisition, MagicTiger will now answer consumers’ questions on food and other food-related products.

Pune-based Quinto raised an undisclosed amount from Faasos Founder Jaydeep Burman. Ketchupp follows the a similar recommendation based model, and this too is yet to see revenues; app downloads and Facebook page members cannot be considered a mode of traction and growth. With investors and the market increasingly looking for revenue models, it will become important for Ketchupp to focus on growth and traction.

Also, with players like Swiggy raising more funding, the possibility of them entering the personalised chat recommendation model is high. They are already venturing into different modules of social recommendations of their own. Not only do these players have deeper pockets, they already have a strong base of restaurant tie ups and partnerships.

While the demand is there – food services is estimated to be a $50 billion market – issues of scale plague this sector. Companies like TinyOwl have borne the brunt of scaling up too soon. Also, these young startups are burning up a lot of investor cash in their quest for growth, as evidenced by the many rounds of funding companies have raised in a short period of time.

The question is whether Ketchupp can fight its competition and the bigger players or not.

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