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With 40+ Retail Clients, 200+ Employees And A $5M Funding, This Startup Has Ambitious Growth Plans For Its ML-Driven Tech Platform

With 40+ Retail Clients, 200+ Employees And A $5M Funding, This Startup Has Ambitious Growth Plans For Its ML-Driven Tech Platform

Srishti Deoras

The adoption of artificial intelligence in the retail sector has increased significantly. From inventory management, brand marketing and order fulfilment, artificial intelligence and machine learning are making a headway in this area. As the retailers are increasingly looking at developing ML capabilities to personalise product recommendations and optimise inventory across sales channels, companies like Ace Turtle are helping make progress in this area.

Analytics India Magazine caught with Kapil Bhatia, CTO of Ace Turtle, a Bengaluru-based company founded in 2013. Ace Turtle provides a platform to retail players that integrates sales channels for brands, enables a single view of inventory across channels, optimises and automates order management process and last mile delivery. In doing so, the platform gathers tons of data from various channels which is then used to build deep ML capabilities to help brands capitalise on omnichannel opportunities.



Ace Turtle has grown eight times in the current fiscal, with leading brands such as Puma, Max, US Polo, Fossil, Rayban and Arrow, using their platform. Having raised $5 million in Series A funding from investors such as Vertex venture, CapitaLand and Innoven Capital, Bhatia shares their ambitious plans.   

How Ace Turtle Is Revolutionising Retail Ecosystem

The company believes that retail space is ripe with data and faces the challenge of having a siloed infrastructure where online and offline sales channels are operating separately. “However, with the customers evolving to become omnichannel, where they expect to interact with the brand at a channel of their convenience (online or offline), it requires, brands to ensure that they have a mechanism that supports the ‘order from anywhere, fulfil from anywhere’ concept. This is where Ace Turtle comes into play,” explains Bhatia.

Ace Turtle, with its fully-integrated omnichannel platform Rubicon, has helped brands integrate all their sales channels to provide a single view of inventory and orders, enabling them to get orders from any channel and fulfil from any stock point.

Prominence Of Analytics And Machine Learning

As the company is taking away the complexity of omnichannel retail and transforming it into a seamlessly orchestrated customer experience, their platform deals with a huge chunk of transactional data. Bhatia shares that this data is used to build ML algorithms to allow brands to optimise their inventory and find the right logistics partner for the fulfilment of a particular order.

“The objective is to make our platform take control over the process from the time the order is placed by the customer till the time the delivery is done without any or minimum human intervention,” says Bhatia.

Bhatia also believes that ML can go a long way in determining the success of an omnichannel platform. He points out that:



  • It can help retailers understand the needs of customers and personalise the assortment
  • Optimise the delivery
  • Provide personalised offers that match their needs
  • Enrich their overall shopping experience

“The benefits of ML for customers extends beyond the point of purchase. It can be used by brands or retailers to forecast sales more accurately at a local store level. Inventory can be stocked based on an intelligent forecast, thus ensuring that there is the right breadth and depth of inventory for orders”, he said.

The availability of data has allowed Ace Turtle to use ML algorithms to complement the strength of their Rubicon platform, helping brands and retailers in better decision making in the real time.

Use Cases And Clients

Bhatia shares that they have partnered with over 40 enterprise brands including the likes of Puma, Fossil, US Polo, Max, Arrow and Ed Hardy, among others.

Explaining how their clients have benefited, he said, “For Puma, our platform has helped in improving product availability and inventory utilisation by 21 times apart from enabling other benefits in terms of reducing logistic costs and also helped in increasing sales by six times.

For Arrow, their platform has solved the problem of limited representation of their inventory in leading marketplaces, and increase the quarterly sale by three times.

Attributing the success of the company to their highly-driven team, Bhatia shares that more than 70 percent of their current team of 200 are in the technology domain, which consists of a mix of senior, mid and entry-level members with varied experiences in engineering, product development and tech support.

Roadmap And Challenges

Sharing the company’s roadmap for the year 2018, Bhatia says that their primary focus is to make their platform futuristic and scalable, and ML will play a critical role in this endeavour. “We had invested a lot in enhancing our ML capabilities and would further build on it to enable faster and smarter business decision for our customers,” he said.

Though ML has a critical role in the success of their platform, it is not entirely an easy road for them. Bhatia explains the challenges by adding, “It is not only about the interpretation of data, but also in the execution of decision that ML is used, which requires a simultaneous evolution of the retail system. For our platform to evolve rapidly and efficiently, the ecosystem also needs to evolve at the same pace”. Here lies the challenge as the tech agility of the ecosystem is not uniform throughout. Moreover, the stakeholders are sometimes limited in terms of having a strong tech infrastructure which builds constraints in adding productive features that can contribute to scale.”

On A Concluding Note

Bhatia believes that recent advances in AI and deep learning have enabled businesses to master many challenges. A continued roll out of new technologies based on ML algorithms has also helped retailers take informed business decision based on data.

“Having said that, the retail industry is still at an early stage of implementing ML, but as with all technology, the speed of adoption will continue to increase, allowing retailers to build their business strategy based on data, real-time and execute through smarter application of AI,” he said.

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