AWS&#8217; Love for Startups in Tier 2 &#038; Tier 3 Cities
As of August 29, 2022, there were around 77,000 DPIIT-recognised startups in India, making it the third largest startup ecosystem globally. Kumara Raghavan, Head, Startups at AWS India (Amazon Web Services India Pvt Ltd), said, “With over 200 products and services, we have the broadest and deepest set of capabilities of any cloud provider, ranging from basic compute and storage to advanced database and architecture options, to pre-built solutions.” In this exclusive interview with Analytics India Magazine, Raghavan discusses how AWS is helping startups in India leverage AI/ML capabilities. AIM: Tell us a bit about how AWS Startups India is helping businesses in the country to scale and grow? Raghavan: AWS has a long history of supporting startups to succeed at every stage of growth, from inception to maturity. At the earliest stages, our AWS Activate programme provides qualified startups with a host of benefits, including AWS credits, technical support, and training. Amazon has provided more than USD 2 billion in AWS credits since 2020 to help early-stage startups launch their businesses and accelerate their growth. With this help, startups are using scalable, reliable, and secure cloud services like compute, storage, database, analytics, Internet of Things, machine learning, and many others from AWS to scale their businesses. We offer extensive cost optimisation services to our startup customers, for whom every dollar counts, especially in the initial launch and growth phases of building a business. For example, SaaS startup ‘Atlan’ was able to scale their deployment seamlessly across 100+ customers globally by leveraging AWS Global Infrastructure, and simultaneously managed to reduce its computing costs by 50–70% by using Amazon EKS on-spot instances, as compared to On-Demand instances. The cost-effective nature of the cloud means that startups can experiment at a greater pace, fail fast with low financial impact, and recover easily. In fact, our team members are tasked with reducing a startup customer’s cloud bill. This may seem counterintuitive, but it is closely aligned with our customer-obsessed culture. Since our inception, AWS has actually reduced its prices 119 times. For startups entering more mature stages of their life cycle, AWS leverages our network to facilitate connections in the startup and investor community. Let me share an example of how that looks right here in India—we’ve worked with cloud-based marketing automation startup ‘MoEngage’ to support their expansion from India and into the ASEAN region by connecting them with more than 230 Chief Marketing Officers of enterprise clients from six countries, resulting in seven new deals. Our global network of startup specialists ensures that we are well placed to assist startups as they pursue their expansion plans, even international ones, which is a critical milestone for any fast-growing startup. For instance, we recently organised a mentorship programme in the US for a delegation of 23 early-stage hyper growth SaaS startups. The programme was designed to provide the selected SaaS startups with insights on topics such as product market fit, go-to-market strategies, team building, marketing opportunities, among others, to build and scale in the US market. At the same time, we connected the startups with US-based SaaS leaders to provide mentorship and networking opportunities to the cohort. To ensure that startups can find the skills they need to develop as they grow, we offer a range of training courses designed by the experts at AWS, including 11 AWS Certifications to help startups cultivate new skills as they scale their businesses—often very quickly. AIM: How is AWS helping startups in India better leverage AI and ML? Raghavan: AWS offers a wide range of pre-trained AI services for tasks such as language interpretation, image recognition and product recommendations. For example, fintech startup RING aims to provide digital financial services to underserved millennials through its transactional credit app. AWS machine learning services help RING make quick credit decisions, underwriting, fraud detection, and document processing. The startup uses Amazon SageMaker and Amazon SageMaker Notebooks to reduce the overheads associated with setting up complex workflows to build machine learning models. This has helped RING’s credit engine on over 14 million loans and has helped verify 20 million customers using their fraud detection engine. RING also uses Amazon Rekognition and Amazon Textract to process documents uploaded by the customers for classification, blur detection, OCR data extraction, photo extraction, face comparison, among other use cases. Using AWS services, RING reduced its non-performing assets (NPA’s) by 20–25%, and delivered a customer retention rate of over 90% for the past 16–18 months. The startup has also successfully reduced the customer wait time by 50%, collection delinquency by 25%, and cost of collection by 30%, by using AWS to power smart analytics and drive high efficiency through appropriate allocation of resources. Another example is Observe.ai, a voice AI platform which helps call centre employees perform better. This rapid growth was driven by Observe.ai’s AI/ML-led innovative customer offerings. Using solutions like Amazon SageMaker, Observe.ai derives actionable insights and analytics from over 50 million calls and over 500 million inferences monthly. This has saved 50% of development effort for every deep learning model deployment and has increased the number of calls processed by 10x in the last two years. Observe.ai has grown 2.5x in the last two years without massive technology spends with AWS and are using AI/ML solutions to drive 10x better outcomes. Our customer, ‘Blend’ is a deep-learning-powered photo and design editing app that democratises access to studio-quality photography and generative AI. SMB sellers use Blend around the world to create professional product visuals in just three clicks to stand out, and sell more online. Blend identifies the product and its pose in the photo, instantly removes the background, and automatically generates thousands of designs optimised for 16 e-commerce marketplaces and eight social media platforms within minutes. The startup is built on AWS from day one. Using AWS services, Blend’s deep learning algorithms generate more than a million designs every day, supporting thousands of e-commerce businesses to create ready-to-use product listings and marketing images. We have also launched initiatives like ML Elevate, an intensive six-week programme to nurture and empower early-stage machine learning (AI-first or ML-core) startups. As a part of the ML Elevate programme, these startups go through a series of sessions that help them focus on evolving their product-market fit, setting up a strong technical foundation for a scalable product and developing capabilities to improve prospects of getting funded. AIM: Today, many startups are mushrooming in Tier 2 and Tier 3 cities in India. Are you working with startups in these regions? Raghavan: At AWS, we work with startups across India and support them with cloud programmes. To give an example, we work with MyTeam11, a startup based in Jaipur. They migrated to AWS in 2019 and since then AWS has worked with them consistently to enable them to optimise costs while managing their scale efficiently. They have also benefited from the AWS Jumpstart programme which helped them set up a data lake and analytics engine in a short time with help of an APN partner specialising in Data Analytics. Through these initiatives, MyTeam11 was able to reduce technology costs by 50% and grow by 220%. Another example is iMerit from Kolkata, which provides digital services like high-quality data across computer vision, natural language processing and content services that power machine learning and artificial intelligence applications for large enterprises. iMerit leveraged AWS’ Cloud Financial Management, a set of tools that can help optimise cloud spend, to understand past spend analysis and savings scope to address their concern on increasing cost. Based on this analysis, AWS identified commercial and technical levers to help reduce their cloud cost. With such initiatives, iMerit was able to save approximately 20% on their monthly bills.
Amazon has provided more than USD2 billion in AWS credits since 2020 to help early-stage startups launch their businesses and accelerate their growth.
Amazon has provided more than USD2 billion in AWS credits since 2020 to help early-stage startups launch their businesses and accelerate their growth.
["AWS", "AWS cloud"]
["Amazon SageMaker", "artificial intelligence", "machine learning", "AWS", "AI", "ML", "computer vision", "Aim", "deep learning", "analytics", "generative AI", "AWS cloud"]
["AI", "artificial intelligence", "machine learning", "ML", "deep learning", "computer vision", "analytics", "generative AI", "Aim", "Amazon SageMaker"]
I have a keen interest in creative writing and artificial intelligence. As a journalist, I deep dive into the world of technology and analyse how it’s restructuring business models and reshaping society.