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Daily news and analysis about the food tech revolution

artificial intelligence

Tastewise is the Latest Startup to Use AI for B2B Flavor Recommendations

Surprisingly, my eight-year-old’s current favorite toy is one of those old Magic 8 Balls that “predict” the future. Granted, most of his questions revolve around acquiring some Lego set, but he, like so many of us, want more certainty in our future.

Tastewise is a new startup that launched yesterday with a technology that aims to help restaurants and CPG companies better predict food trends using data and artificial intelligence (AI). According to the press announcement, “The platform analyzes billions of critical food and beverage consumer touchpoints to discover people’s real-life interactions with food including over 1 billion food photos shared every month, 153K restaurant menus across the US and over 1M online recipes.”

So Tastewise is looking at all those food pictures people are Instagramming and Tweeting about to see what is hip with the kids. It can also parse different ways items are described. For example, it will know that hamburgers, burgers and sliders are all basically the same thing. It takes all of this information and runs it through its algorithms to recommend new products on both a national and local level (what’s cool in Brooklyn may not be cool in Omaha).

I spoke with Co-Founder Alon Chen by phone, and he told me that with Tastewise, his clients can simply type in a food item like “hummus” and the software will crunch all the data and report back results of not only flavor information (ingredients people are adding to hummus), but also how people are using hummus (not just as a dip, but also as a spread).

Tastewise is offered as a SaaS product, and while specific subscription plans are being worked out, Chen said that they will always offer a free tier of results and a premium version for $299 a month. Exactly what results and insights are available to premium subscribers has yet to be determined.

The flavor-prediction sector is certainly hot like sriracha (though Tastewise says Zhoug is the next sriracha), as there are a number of other B2B players already in the market. Spoonshot and Analytical Flavor Systems both use AI to help companies predict and act quickly on food trends. Even spice company McCormick enlisted IBM’s Watson to help determine what tastes are on the horizon.

When asked about his competition, Chen said that Tastewise “is not a sensory platform.” Rather, his company is looking at what people are saying and the actions they are taking around food to develop consumer insight and intelligence that reflect what is happening and predict what specific foods and flavors will become hits.

Tastewise has raised $1.5 million in funding and has five employees. With CPG companies and restaurants all looking for any kind of edge over their competition, it doesn’t take a Magic 8 Ball to see that Tastewise has picked the right sector. Now we just need to see if it can beat out all the competition.

DeepMagic Combines Computer Vision and AI to Make Mini, Unattended Amazon Gos

One of the questions that comes up when talking about Amazon Go cashierless stores is when the grab-and-go technology experience will scale up from a bodega-sized convenience store to a full-on grocery experience. But instead of thinking big, startup DeepMagic is going the other direction: developing small unattended, cashierless micro-retail outlets.

Using a combination of computer vision and artificial intelligence (AI), DeepMagic creates self-contained, cashierless walk-in “Qick Kiosks” that can be placed within existing locations. Customers use an app on their phone to unlock the kiosk doors, go inside, grab what they want and leave. Cameras in the Qick Kiosk keep track of everything taken (just like Amazon Go) and automatically charge your card when you exit the kiosk.

DeepMagic doesn’t want to own and operate its own chain of cashierless stores; rather, it wants to provide these kiosks as a way to create retail opportunities within existing high-traffic areas. Think: pop-up shops inside office building lobbies or big apartment complexes.

“It’s not about replacing existing store formats,” DeepMagic Co-Founder and CEO, Bernd Schoner told me by phone. “We want to give store owners the ability to create additional locations. Office space not big enough for a canteen? You can put a kiosk in that space.’

Schoner said DeepMagic’s approach lets retailers easily create satellite locations that can operate 24/7, without having to build a full store, or hire extra staff. For instance a bodega could run a smaller bodega inside a nearby apartment building.

DeepMagic combines a number of elements and approaches already happening in the automated, cashierless retail space. It has the Amazon grab-and-Go element. But it’s also similar to Stockwell (formerly Bodega), which creates even computer vision driven, credenza-sized containers with snacks and sundries for densely populated buildings. And Schoner’s canteen example is reminiscent of Byte Foods, which puts smart fridges stocked with food in offices.

While DeepMagic’s kiosks may add flexibility to retail locations, the company’s approach has a downside: the kiosks can only deal with one purchaser at a time. There can be multiple people in the same kiosks, but whatever they grab will be charged to the person who unlocked the store with their phone. So it seems like there could be lines that form to get into each kiosk, which kind of kills the convenience of cashierless checkout.

Having said that, DeepMagic’s turnkey kiosks could be big enough to offer a decent selection of items and branding experience for a retailer, yet small enough to create new retail opportunities within existing locations at an attractive cost. That is, if DeepMagic kiosks are at an attractive cost. Schoner wouldn’t disclose pricing on a DeepMagic kiosk, only saying that there will most likely be some combination of lease, SaaS subscription and percentage of retail sales.

DeepMagic has, however, proved its technology in public. Earlier this year, Cisco set up a DeepMagic kiosk to sell swag at its conference. Schoner says the company is working on a number of other deals right now. DeepMagic is self-funded, and has 15 employees across New York and Mexico.

While we wait and see how big cashierless stores can scale up, we’ll also have to keep an eye out to see if staying small pays off for DeepMagic.

Analytical Flavor Systems Raises $4M for its AI-Powered Flavor Prediction Platform

Analytical Flavor Systems (AFS), which uses artificial intelligence (AI) to help companies predict and personalize flavor for new food products, announced today that it has raised $4 million in Series A funding led Leawood Venture Capital and Global Brain. VentureBeat was first to report the news. AFS had previously raised an undisclosed amount of seed funding through Better Food Ventures and Techstars.

Food personalization, powered by advancements in AI and machine learning, is a big trend we’re following at The Spoon. Earlier this year we named AFS as one of our FoodTech 25 companies changing the way we eat, writing the following:

Analytical Flavor Systems’ AI-driven Gastrograph platform helps packaged food companies achieve greater success in a saturated food industry that has an over 80% failure rate. Gastograph moves CPG brands’ development process beyond traditional tasting panels; it surveys each product with a flavor profile engine that is predictive, anticipating how new foods will perform in different markets, over a long time horizon, and against various demographic archetypes. Food companies are struggling to launch new products in an era of rapidly shifting consumer tastes, and an AI-driven platform like Gastrograph gives big food a more accurate map with which to navigate into the future.

Think of Gastrograph almost like Flavor as a Service. Using data from “regular” people and professional tasters to power its analytical engine, Gastrograph can help food companies determine which flavors will be popular with different people or in different regions etc.

AFS Co-Founder and CEO, Jason Cohen spoke at our Smart Kitchen Summit in Seattle last month, and gave a presentation where he talked about personalization of food versus customization, and also provided a nice walkthrough of how AFS and Gastrograph works.

AI and Personalized Flavor

AFS told VentureBeat that it will use the money to build out the team and further develop its technology. The company’s fundraise comes at a good time, as it is among a raft of startups using AI to power flavor and food recommendations. Other players in the space include Spoonshot (formerly Dishq), Plantjammer, FoodPairing, and Flavorwiki.

Hitachi to Use AI to Analyze Hospital Food Leftovers and Improve Patient Recovery

We often write about artificial intelligence (AI) being used on food before it gets to you: inspecting the supply chain, making sure your burgers are cooked, etc. But a new unit of Japanese company Hitachi is applying AI to food leftover on the plate after people are done with it.

The Japan Times reports that Hitachi is partnering with hospitals to use AI to analyze food not eaten by inpatients. Hospitals prepare meals with specific nutrients for each patient to assist in the recovery during their stay, and uneaten food can result in recovery delays.

Hitachi has already starting testing its tech with a major hospital in Japan. The system works by using a camera mounted on a trolley that collects trays, taking pictures of the leftovers. The company’s deep learning algorithms then examine the images to provide analysis.

By doing this post-meal analysis, Hitachi’s systems can recognize patterns in the leftovers that humans otherwise could not see. Japan Times writes that nurses often check leftovers now, but the task adds to their workloads and they are not trained nutritionists.

Hitachi plans to use this system even for remote patients, who could take pictures of their food with their smartphone. If this system works as promised, it’s not hard to envision this technology moving further into the consumer category. Being able to quickly analyze your (or your kid’s) uneaten food and establish a nutritional pattern for what’s left would go a long way to help identify personalized food profiles, or help customize your own personal 3D printed food.

Of course, it seems like the end goal for this technology is self-obsolescence. The better the AI works at identifying food you haven’t eaten, and the better you’ll (hopefully) get at eating all of your food — the less you need the leftover scanner.

Report: Microsoft Working on Amazon Go-like Cashierless Tech

Microsoft is reportedly working on its own cashierless checkout technology in a bid to take on cross-town rival, Amazon, according to a story in Reuters.

The reported technology is similar to the Amazon Go store experience, where what you put in your cart is automatically tracked and charged to you without the need to go through a checkout line or cashier. Reuters goes on to report that Microsoft has engaged in talks with Walmart about the technology.

If true, the news isn’t that surprising for a number of reasons. First, Amazon Go uses technology like computer vision and artificial intelligence to know what you put (and keep) in your bag. Computer vision and AI are two areas of focus for Microsoft research. Second, we’ve known since December that Walmart is exploring its own computer vision-based cashierless store experience (and last month, the retailer killed its Scan and Go approach to cashierless shopping).

Finally, and most obvious, nobody wants to cede even more of the future of shopping to Amazon, and grocery shopping is no exception. Amazon already owns Whole Foods and is expanding discounts and two-hour delivery for its 100 million-strong Prime members. Plus, the first Amazon Go store is very impressive, is expanding into Chicago and San Francisco, and absolutely should be replicated elsewhere.

Moves like these have sent grocery retailers scrambling to compete. Target and Walmart are expanding their two-hour delivery service. Albertsons partnered with Instacart, and Kroger just invested more heavily in Ocado to build out twenty rapid-delivery robot warehouses here in the U.S.. Not to mention Walmart experimenting with its own fridge-to-fridge delivery service similar to Amazon Key.

Plus, other smaller players are working on their own versions of cashierless tech. All_ebt has Amazon Go-like ambitions for those on food stamps. Caper has its own computer vision and deep learning smart checkout cart. And AI Poly, whose CEO is speaking at our Smart Kitchen Summit in Seattle, has its own autonomous market in the works.

So while Microsoft provided a big “no comment” for Reuters, the idea of the Redmond giant working on such technology shouldn’t come as a news flash to anyone following the industry.

Podcast: Using VR to Train AI to Kill Weeds (Among Other Things)

Using virtual reality to train artificial intelligence to better interact with the real world almost sounds like what you’d get if Inception and Westworld had a synthetic baby. But there’s no deep mystery or ambiguous ending here; it’s the work of a company called AI.Reverie, and it has applications in agriculture technology.

This week on The Automat (our weekly podcast about food-related robots and AI), we sit down with Daeil Kim, Founder and CEO of AI.Reverie to talk about the power of virtual worlds for training AI, applications for the technology in agriculture (weed killing robots!), and how it can help identify and sort food in the supply chain.

It’s a fascinating deep dive into really the cutting edge of both AI and VR. Listen to it here and subscribe to all our Spoon Feed podcasts in iTunes.

Intello Labs Uses AI to Help Farmers Get a Fair Price for Their Crops

When we talk about artificial intelligence (AI), we often speak in giant, world-shifting terms about revolutionizing a certain industry. But AI can also benefit a single person at a time. In the case of Intello Labs, its AI can be used to help prevent a poor farmer from getting screwed.

Food inspection is often still done manually. One person’s perfect tomato may be another’s piece of trash, and these basic biases can lead to an imbalance of power. A poor, rural, farmer may not be educated on price points or what “fresh” produce means to a buyer. As a result, they may want to sell tomatoes at a dollar per tomato, but buyers may scoff, refuting the quality of those tomatoes, and only offer fifty cents. How are they to know how much the literal fruits of their labor are actually worth?

Intello Labs is working to help balance these scales through a combination of computer vision and artificial intelligence. Using their mobile phone app, the tomato farmer could take a picture of a bushel of tomatoes and upload it into Intello’s system. The company’s algorithms would examine the photo of the tomatoes and gives it a rating based on a set of government (i.e. USDA) or other criteria. With this objective, algorithmic rating in place, each party in the negotiation now knows the quality of the tomatoes being sold — and they can be priced accordingly.

The company started with commodities like tomatoes and potatoes, but according to Sreevidya Ghantasala, Intello Labs Head of U.S. Operations, the company’s core technology can be customized for almost any food. It could be used to rate products like seafood and chicken, or even as a tool for plant disease identification. “We have a pest and disease application for six or seven different crops,” said Ghantasala, “Our system is highly customizeable. If there’s something we don’t see on our library, we can update it in 2 to 3 months.”

Intello, which is headquartered in Bengaluru, India, has already gone live elsewhere in that country at the farmer’s market in Rajasthan to work with 10,000 farmers there for wheat and grain analysis. The company has also worked with the Reliance Foundation in India to help 100,000 farmers with pest and disease detection for crops.

Pricing for Intello’s software is subscription based, and Ghantasala wouldn’t provide specific numbers. She said that cost was dependent on what was being analyzed, and what users want to use it to detect. The company was founded in May 2016, and has raised money through friends, families and various different accelerator programs. It now has 30 employees across offices in Bengaluru, Stockholm, Sweden and Plano, Texas.

Intello isn’t the only one using computer vision and AI to generate objective food ratings. Here in the U.S., AgShift is using a similar mobile phone app to provide better data for food buyers in the supply chain to help reduce food waste. And grocery giant Walmart has implemented its own machine learning-based Eden technology to assess food freshness.

But according to Ghantasala, Intello’s ambitions go beyond food altogether. The company is working with gas and oil companies in Sweden to apply its computer vision to parts identification, and they want to expand its vision into hyperspectral imaging for more in-depth analysis.

Intello, it seems, wants to use its AI to change the world. But for now, it’s changing the world for one farmer at a time.

Sony and Carnegie Mellon Team Up to Work on Food Robots

Sony Corporation announced yesterday that it has hooked up with Carnegie Mellon University (CMU) to collaborate on artificial intelligence (AI) and robotics research that will initially focus on “optimizing food preparation, cooking and delivery.”

In a press release, Sony said they were starting off with food-related robots because the complexities involved with food could later be applied to a wider range of industries. Specifically, it cited the ability to work with fragile and odd-shaped materials, as well as the ability to operate a robot in small spaces.

The research will happen mostly at CMU’s School of Computer Science in Pittsburgh. Partnering with a big tech company isn’t new for CMU; the school has previously worked with Uber on self-driving car technology.

Despite, or perhaps because of, the complexities involved with its preparation, food robotics is a hot area right now. Miso Robotics has Flippy, a robot which uses a series of cameras, thermal imaging and AI to properly cook a hamburger. Cafe X launched its second generation robot barista-in-a-box. Meanwhile, 6d bytes and Alberts have both launched smoothie-making robots.

More difficult than actually building useful robots may be tackling the issues surrounding human/robot interaction. Flippy was “retired” after just one day because human workers just couldn’t keep up with the fast robot. Cafe X and 6d bytes’ bots are self-contained units that have pretty much taken humans out of the equation altogether.

The delivery aspect of this partnership is also intriguing. Companies like Marble and Starship have started rolling out pilot projects for robot food delivery in cities across the country. Leveraging CMU’s experience with autonomous driving could rapidly advance the viability of small, grocery-carrying robots scurrying around city sidewalks.

We’ll keep tabs on this project to see what comes of it. Who knows, maybe Sony can develop a delivery robot that’s as cute as its Aibo robot dog.

Ingest.AI Unifies Disparate Data to Run Restaurants more Efficiently

If you learn one thing while covering restaurant software companies, it’s that there are a lot of restaurant software companies. Payment systems, HR, inventory management. Not to mention all of the software applications built on top of those like GrubHub, OpenTable, and a host of others.

The problem is that none of these systems talk to one another, so useful data sits in silos, unable to integrate and deliver holistic, business-wide insights for restaurants. The result can be inefficiencies that cause wastes of human capital and food.

To solve this, Kenneth Kuo founded and is CEO of Ingest.AI, a software layer that plugs into all these disparate restaurant systems, uses machine learning to extract data from all of them, and unifies them into a single platform.

“We clean, classify and aggregate all the data to prep it for our second set of machine learning,” said Kuo.

Because Ingest.AI accesses and combines data from every part of the store, it can then tell a manager what will happen at a restaurant at any given time slice with “upwards of 90 percent accuracy.” This allows the manager to properly order the right amount of food and schedule staff accordingly.

And proper staffing can be a huge headache for a manager, especially in states that have high minimum wage and punitive overtime laws. Ingest.AI can make dynamic staffing suggestions to deliver alerts when workers are nearing overtime, and it can schedule around that or ensure there aren’t too many or too little servers at any given time.

With its predictive analytics, Ingest.AI can also help in the back of the house with proper ordering. It knows when a particular ingredient is running low as well as how long it takes a vendor to make deliveries. With this info, Ingest.AI can automate the ordering so restaurants have enough inventory on hand in anticipation of busy times.

Ingest.AI can also make smaller tweaks throughout the dining experience to increase incremental revenue. It will know, for example, that when parties of six or more people sit down, the first thing they do is order beer. The software will send out a notification to servers to suggest that the first thing they say to customers is “Hello, what kind of beer can we get for you, here’s what we have on tap…”

Restaurant managers aren’t typically data scientists, and connecting data from every aspect of the house all at once could quickly set them adrift in a sea of numbers. But Kuo is cognizant of this, and says he basically wants to answer two questions for the restaurant manager: “1. Did I make money? 2. Am I going to go under in the next week?”

You’d be forgiven for thinking was all too good to be true. A magical AI layer that can talk to and predict just about anything in your restaurant saving you time and money. It has a whiff of software snake oil. But Kuo has bona fides when it comes to artificial intelligence: Prior to his startup, he worked on IBM Watson using natural language to deliver personality insights.

There are two things that stand out for me when thinking about Ingest.AI. First, it has the capacity to replace a lot of other restaurant software startups out there. Ordermark unifies orders from different delivery services and Gebni provides dynamic pricing on menu items — but that’s all they do. Ingest.AI does those bits plus a lot more.

And second, honestly the food industry could be just the beginning for Ingest.AI. Every company I’ve worked for uses multiple software applications (Slack, Salesforce, Braintree, Workday, etc.) that don’t talk directly with one another. If Ingest.AI works as promised, there’s no reason it couldn’t expand beyond restaurants into any vertical.

But that is further down the road. Right now Ingest.AI is bootstrapped, based in Manhattan and was just inducted into the latest Food-X cohort. The company has nearly twenty customers paying anywhere from $150 – $250 per month for the service. Kuo says that it has a few deals in the pipeline and after that they will begin looking for a $1 – $1.5 million round of funding around November of this year.

So sure, there are a lot of restaurant software startups out there, but Ingest.AI seems like one to watch.

Farmstead Raises $2M, Releases FreshAI for Retail Food Management

It’s only Tuesday and already it has been a busy week for Farmstead, the startup that uses a combination of machine learning and small food hubs to enable home grocery delivery. On Monday, the company announced it had raised a $2 million seed round, and today, Farmstead opened up its FreshAI predictive analytics software platform to external food companies.

Farmstead is trying to create an entirely new model for grocery stores by limiting its physical footprint and using AI to intelligently curate its inventory. If successful, Farmstead Co-Founder and CEO, Pradeep Elankumaran, believes his company can drastically reduce the huge food waste problem in this country (where 40 percent of our food is never eaten).

Traditional grocery stores are massive buildings stocked to the gills to provide shoppers with a ton of options. Farmstead, on the other hand, uses food “microhubs” that are only about two to three thousand square feet and carry only about 1,000 items across all shopping categories.

It can get away with carrying such a small inventory because its AI platform predicts the items customers will want and orders just the right amount. To do this, Farmstead combines historical sales data and current trend data with consumer recommendations and external factors, such as holidays and product sell-by dates. Its machine learning processes these data points to assess which items to carry and how many it should stock at any given time.

“The system gives us a very fine prediction for stock,” Farmstead Co-Founder and CEO, Pradeep Elankumaran told me. “We want to have just enough on the shelves so no new customers walk away disappointed, but not so much that product gets wasted.”

Because Farmstead microhubs are small, they can be implemented closer to residential neighborhoods rather than warehouse districts to facilitate faster home delivery. Farmstead currently operates in the Bay Area and offers one-hour delivery from 8:00 a.m. to 9:30 p.m. for people in San Francisco, as well as rolling delivery hours during the morning, afternoon and evening for those in the elsewhere in the Bay. You can also drive to the Farmstead hub in San Francisco or San Mateo and a worker will run out and put your groceries in the car.

Elankumaran said that Farmstead is now making 1,500 – 1,600 deliveries per week. With this $2 million round, Farmstead has raised a total of $4.8 million so far. It will use the new money to scale its delivery service and expand beyond the Bay Area and will build out its core AI technology.

Which brings us back to today’s news about FreshAI. Farmstead is opening up its predictive analytics platform to supermarkets, cafeterias, restaurant chains and other companies that work with food at scale, allowing them to use Farmstead’s automated inventory management system. Farmstead says that using FreshAI has reduced its perishable food waste to sub ten percent as opposed to “35 – 40 percent average perishable food waste for typical supermarkets.”

For now, FreshAI is a pilot program, and companies can apply to take part on Farmstead’s site. If accepted, companies will upload operations data, and Fresh AI will provide daily and weekly order recommendations.

Farmstead is part of a new generation of startups such as Spoiler Alert, Ovie, and LeanPath that are using technology to combat food waste. It’s also reminiscent of CommonSense Robotics, which is using a combination of robots and micro-fulfillment centers. But Elankumaran said Farmstead is still a ways from using robots in their hubs or to fulfill deliveries. Instead, he said that they’re focusing on getting the best possible customer experience.

If Farmstead can deliver on that experience as it scales out, the busy weeks won’t be ending anytime soon for Elankumaran or his company.

SomaDetect Uses AI to Help Dairy Farmers Improve their Milk

There is a milk glut in the U.S.. Technology has allowed dairy farmers to produce more milk than ever, but all this abundance has caused milk prices to plummet. It’s getting so bad that some farmers face selling off their cows.

While technology helped create this crisis, perhaps SomaDetect‘s technology can help struggling dairy farmers get out of it.

SomaDetect uses a combination of optical sensors and machine learning to help dairy farmers analyze the milk each cow produces to determine its quality. As SomaDetect CEO Bethany Deshpande explained it to me, farmers attach a small sensor box to the milking hose, which shines a light through the milk as it flows. Based on the scatter pattern of that light coming through, SomaDetect’s software can analyze what’s in the milk.

The company measures fat and protein as well the reproductive status of a cow and any residual antibiotics. More importantly, SomaDetect can look at somatic cells to detect Mastitis, a serious bacterial infection of the udder that is the most common disease among dairy cows and the number one cause of their early death. By quickly identifying cows with high somatic cell counts, farmers can better target treatment and to help prevent the spread of Mastitis.

Additionally, one cow can throw off somatic counts for an entire batch of product. By removing high-somatic cows, the farmer can lower the overall somatic counts of their milk and earn more money: “Farmers with low somatic cell counts get a bonus from the milk processor,” said Deshpande.

This type of deep inspection of milk has only recently become possible. According to Deshpande, the optical technology has “been around for a hundred years,” but advances in computer vision and machine learning means SomaDetect’s software can analyze vast sums of information in ways that were not possible even five years ago.

SomaDetect isn’t the only company using light to help dairy farmers. EIO Diagnostics uses multispectral imaging for Somatic cell counting, and Consumer Physics is putting its handheld SCiO device to use on farms to detect levels of dry matter in cow feed, which can also impact milk production.

SomaDetect was founded in 2016 in New Brunswick, Canada. The company won a 43North startup competition, earning them $1 million in funding and office space in Buffalo, NY. SomaDetect makes money by selling the equipment to farmers and charging $5 per month per cow for the software. The company is currently running pilot programs and is searching for seed funding as it looks to expand into New York state.

Deshpande says she came from the dairy side of the industry and wants SomaDetect to work closely with farmers. With milk prices expected to stay low throughout this year, dairy farmers could use all the help they can get.

You can hear about SomaDetect in our daily spoon podcast.  You can also subscribe in Apple podcasts or through our Amazon Alexa skill. 

Dishq Uses Machine Learning for Bespoke Food Recommendations

This is embarrassing to admit, but whenever I go to a Thai restaurant, I just order tofu phad thai. Always. Yes, that is totally generic, but I don’t get to eat out that often, so I don’t want to take a chance on something that I might really dislike. I know what I’m getting with the phad thai, so I settle.

It’s that settling for the same-ole that Dishq is looking to improve by using artificial intelligence. Based in Bangalore, India, Dishq provides APIs for food service companies like restaurants, corporate cafeterias or food delivery services, so those companies can implement AI-powered, customized food suggestions for their customers.

During an interview, Dishq Co-Founder and CEO Kishan Vasani told me there are four parts of his company’s offering:

  • A database of more than 100,000 dishes that are broken down into 26 different attributes including ingredients, cuisine style and cuisine origin
  • Anonymized customer behavior analytics data
  • A machine learning algorithm
  • Food science research from around the world that feeds into Dishq’s algorithm

Vasani said that the collaborative filtering used by Amazon or Netflix to make suggestions won’t work for food because meals are such a personal experience. “Too many things go into it,” said Vasani, “What you like, where you grew up, who you’re with.” He says that Dishq’s deep data-driven approach allows for truly bespoke recommendations because it understands food at the flavor compound level as well as transaction history.

Where the customer encounters those recommendations are up to the company using Dishq’s API. It can be used at the menu level to surface suggestions or email notifications. It depends on whether that client is looking for more conversions, increased average order value, or just creating a better customer experience.

Right now Vasani says that Dishq has 6 clients with two million recommendations generated every month, and that clients see an 11 percent uplift in revenue with Dishq.

Sadly, Dishq can’t offer a universal taste passport that travels from restaurant to restaurant. So what you like at Domino’s would help them determine what you might like at Dunkin’ Donuts. The reason for that, Vasani says is data protection and restaurants not wanting to play nice with one another.

Founded in 2015, Dishq has 13 employees and has raised $160,000 to date, with $120,000 of that coming from the Hong Kong-based AI accelerator Zeroth.ai. Vasani says he plans to start looking for new funds in Q2 of this year.

Vasani is also looking to expand beyond food service clientele and into the consumer packaged goods. He refers to the forthcoming product as “Taste analytics as a service,” and would allow CPG companies to react more quickly to food trends as they are happening. For example, if avocados were suddenly appearing everywhere on Instagram and social media mentions around Nashville, Dishq’s data could help a CPG company spot and understand those trends to quickly ramp up some avocado-related product for that location.

Until then, however, Dishq just wants to make eating out more pleasurable. Vasani wants Dishq’s recommendations to “shift people’s experience from a 6 out of 10 to an 8 or 9,” said Vasani. If Dishq works as promised, that could mean a lot less phad thai for me in the future.