Businesses today face an overwhelming “data tsunami” — a surge of data that, while valuable, often hampers application performance and strains architectural scalability. The more data an organization manages, the more challenging these issues become. With AI workloads adding to data management complexities, enterprises are looking for ways to achieve scalability, maintain performance, and meet evolving data demands.

CIO Dimension spoke with Bhanu Jamwal, Head of Presales & Solution Engineering, APAC, TiDB, to explore how a modern and innovative approach to databases is helping businesses overcome these challenges and innovate. TiDB – an open source distributed SQL database developed by PingCAP – tries to provide some answers. 

How is PingCAP’s TiDB positioning itself in the competitive database market, particularly considering the rapid growth of digital native businesses in India?

We’ve witnessed a remarkable surge in digital native businesses across India, spanning e-commerce, logistics, fintech, gaming, SaaS, and more recently, the emerging healthcare and Gen AI sectors. This explosive growth, however, comes at a cost – a massive influx of data.

Their increasing reliance on data creates a greater demand for scalable and reliable data solutions. Traditional databases often struggle to scale to meet this kind of growth and volume. Imagine a scenario where an e-commerce platform, during a peak sales event experiencing a huge surge in transactions. Traditional databases, such as MySQL or PostgreSQL, may encounter performance bottlenecks under such pressure.

Businesses are therefore compelled to either implement workarounds or adopt modern database solutions that can address these challenges. And these challenges now go beyond just scale. High availability is paramount; downtime is simply not an option today. Real-time analytics is another critical factor businesses. For instance, a bank needs to be able to detect fraudulent transactions in real-time to safeguard customer funds. So, it needs, scalability, availability and analytics- all at the same time. AI introduces further complexities and challenges to this mix. 

Recognizing these evolving needs, TiDB was born in 2015. We strive to address these challenges with our distributed architecture, which allows businesses to scale as their data grows. Distributed SQL databases have in fact emerged as an effective answer to modern data challenges. This approach has been gaining traction, as evidenced by Amazon’s recent entry into the distributed SQL market, acknowledging the growing demand for such solutions. 

How do you view the SQL vs. NoSQL debate, and how does TiDB position itself in this context?

The SQL vs. NoSQL debate has been ongoing for some time with both playing pivotal roles in enterprise environments. While NoSQL databases offer flexibility for specific use cases, they often require significant development effort to maintain data structures. SQL, on the other hand, remains the de facto standard for data access, with a vast pool of skilled developers. 

It’s interesting how many NoSQL databases, which originally distanced themselves from SQL, are now adding SQL layers to their platforms. It shows that SQL remains a simple and effective way to access data. 

TiDB’s aim is to bring best of both by offering a distributed SQL solution. Our Hybrid Transactional/Analytical Processing (HTAP) capabilities are designed to bridge the gap between SQL and NoSQL databases and handle both transactional and analytical workloads. 

How is AI and ML, particularly the rise of LLMs, changing the game for distributed databases, and how is TiDB capitalizing on these trends?

The rise of vector database is a trend worth mentioning in this context. Vector is becoming a powerful engine for today’s AI workloads. Though it has been around for a while, the adoption has rapidly scaled in recent years. These databases are becoming increasingly crucial for storing and processing the data generated by AI/ML models. 

But, this also makes the environment quite complex for enterprises. They often manage multiple databases for different workloads—a transactional database, a separate one for analytics, and yet another for vector storage. Managing databases is a complex process. 

At TiDB, we see an opportunity to simplify this process by offering a unified solution. Our vision is to provide a single database that handles all these workloads, eliminating the need for multiple systems. TiDB supports vector as a form of storage with TiDB serverless, enabling organizations to store and process both traditional data and vector data within a unified platform. We believe the integration of vector search capabilities into a traditional database will be a game-changer for businesses looking to leverage the power of AI and ML. 

Can you share some successful case studies of TiDB implementations in India and globally?

Absolutely! We have a number of success stories across various industries. In India, a leading e-commerce giant was struggling to manage the increasing complexity of their database infrastructure as they scaled, relying on a multitude of single-node databases. By migrating to TiDB, they consolidated their infrastructure, significantly improved performance, and gained greater operational efficiency. 

Delhivery, a leading logistics company that delivers a million packages a day, migrated to TiDB to power their real-time analytics and data marts use case, enabling them to handle thousands of requests per second with less than 100ms latency. 

Pinterest is another flagship customer that leveraged TiDB to modernize their data architecture. Globally, we’ve seen tremendous success in the fintech sector. A major Japanese fintech company, for example, experienced a 10x increase in throughput and a 30% reduction in latency after adopting TiDB for their high-volume wallet application. 

We’re also seeing significant growth in the SaaS market, where TiDB helps companies manage the complexities of multi-tenant environments and scale their databases to support growing user bases.

What are the key market opportunities for TiDB in India?

India is a dynamic and rapidly growing market with a thriving startup ecosystem. We see immense potential here. The sheer volume of data generated by India’s digital-native businesses and startups, coupled with their ambitious growth plans, creates a strong demand for distributed database solutions. India’s thriving Web3 community, its vast base of skilled developers are all opportunities that TiDB will be actively tapping into. 

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By Saurabh Rai

Reflecting on 2024, the geospatial sector has witnessed transformative advancements, driven by innovations that integrate GIS, AI, and IoT into critical domains. The development of the Ayodhya Sustainability Index as a milestone in leveraging geospatial intelligence for sustainable urban planning, environmental conservation, and heritage preservation. This initiative has set a global benchmark, showcasing how technology can balance growth with cultural preservation. 

The year also saw India embracing national initiatives like Naksha, Bhuvan, and SVAMITVA, which propelled sustainable development, urban planning, and disaster management using geospatial solutions. These programs reflect the sector’s growing influence in addressing complex challenges.

Looking ahead to 2025, the integration of geospatial technologies with AI, IoT, and big data is poised to transform industries such as agriculture, logistics, and renewable energy. 

The focus will be on leveraging these technologies to tackle global issues like climate change, rapid urbanization, and resource management. With data-driven tools at the core, the industry aims to drive impactful and sustainable change, fostering innovation and supporting national priorities. 

With precise data-driven insights, industries can make informed decisions, optimise resource allocation and implement sustainable practices.

In agriculture, for instance, geospatial tools combined with AI can enhance crop monitoring, predict yield and manage water resources effectively. In logistics, they streamline operations by providing real-time data on traffic, weather and optimal routes. Renewable energy sectors can benefit by identifying ideal locations for solar and wind energy projects, thus accelerating India’s transition to cleaner energy sources.

The integration of geospatial technologies aligns with India’s national priorities, such as building resilient infrastructure and achieving sustainable development goals. 

As geospatial intelligence continues to evolve, it promises to play a critical role in shaping a more sustainable, resilient, and technologically empowered future.

Saurabh Rai is the CEO of Arahas, a provider of Geospatial, AI, and Sustainability Technology services. The opinions expressed by the author do not necessarily reflect the views of CIO Dimension.

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For CIOs and CTOs, AI can automate routine tasks like network monitoring, system maintenance, and cybersecurity, reducing the need for hands-on management. This gives them more time to focus on innovation and developing long-term technology strategies. AI also helps them make data-driven decisions by providing insights from vast amounts of data, which allows companies to anticipate customer needs, improve operations, and create new products or services.

However, AI also presents new challenges. CIOs and CTOs will need to navigate ethical concerns around AI, such as data privacy and the impact of automation on jobs. They’ll also need to continuously update their skills to keep up with rapidly advancing AI technologies.

Moreover, AI is driving the need for strong collaboration between CIOs/CTOs and other departments like human resources, marketing, and finance. For instance, AI systems that monitor employee performance or improve customer experience require cross-functional alignment. This means CIOs and CTOs must become more integrated with the business side, ensuring that AI initiatives are aligned with overall company goals.

As companies continue to adopt AI-driven solutions, the demand for CIOs and CTOs who understand both technology and business strategy will grow. Embracing this shift will help them not only lead but also innovate in today’s competitive landscape. Those who stay ahead will be the driving force behind their organization’s success.

References:

• Forbes: How AI is Reshaping the Role of the CIO
• Gartner: Gartner Says Three-Fourths of CIO Job Description Will Be Rewritten by 2024

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MongoDB announced a partnership with All India Council for Technical Education (AICTE), Ministry of Education, Government of India to give more than 150,000 Indian students access to virtual internships and to gain the skills required to use MongoDB Atlas—the leading multi-cloud developer data platform. 

This partnership with AICTE, the Indian government’s authority for the management of technical education, is part of the company’s ‘MongoDB for Academia’ initiative and will be supported by SmartBridge’s SmartInternz learning platform. Each internship will include 60 hours of experiential learning, hands-on bootcamps, courses, and project work, as well as simulated corporate environments where students can apply their learned skills, collaborate with peers, and receive mentorship. 

Launched in September 2023, the MongoDB for Academia in India program provides student training, curriculum resources for educators, credits to use MongoDB technology at no cost, and certifications to help individuals start careers in the technology industry. The skills and training provided through the program are particularly important, as many Indian organizations struggle to find developers who have the skills to build modern applications and take advantage of emerging technologies like generative AI, the company maintained. 

According to a report from NASSCOM, India’s technology sector will demand more than one million engineers with advanced skills in artificial intelligence and other capabilities over the next three years. Overall, the industry body expects there will be a need for around six million digital roles by 2028—and the available talent pool is forecast to be 4.7 million workers. This gap underscores the need for increased collaboration between industry and academia to upskill students and educators in India to meet the demands of the country’s large and growing economy. 

“In India, we have a massive opportunity with the current wave of AI and modern technologies that will transform our lives and economy in the coming years. But to take advantage of that opportunity, it is vital our developers have the right skills. We’re excited to partner with MongoDB to help make that possible,” said Dr. Buddha Chandrasekhar CEO, Anuvadini AI, Ministry of Education and Chief Coordinating Officer, AICTE, Ministry of Education, Government of India.

“We’ve seen great appetite and interest on our platform for modern database technologies like MongoDB. We want to equip students with knowledge of in-demand technologies so they have skills they need to become the job-ready candidates India’s organizations are looking for,” said Amarender Katkam, Founder and CEO at SmartBridge and SmartInternz. 

“India loves developers and so does MongoDB. I’m so proud of the work our MongoDB for Academia team is doing to empower Indian developers and to support the next generation of tech talent in this country,” said Sachin Chawla, Area Vice President, India at MongoDB.

As part of the program’s expansion, MongoDB also announced a new partnership with GeeksforGeeks, a platform for computer science resources in India. The collaboration will make the MongoDB Developer Learning Path available to all of GeeksforGeeks’ 25 million registered users.

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Indian CEOs place great importance on AI governance, with a significant 71% believing that trusted AI is impossible without effective AI governance in organizations. A global study of 3,000 CEOs conducted by IBM Institute for Business Value, in cooperation with Oxford Economics, reveals a number of interesting aspects about workforce, culture and governance challenges faced by Indian CEOs while navigating AI journeys. 

According to the study, 75% Indian CEO respondents say governance for generative AI must be established as solutions are designed, rather than after they are deployed. At the same time, the study also noted a contrast in actual adoption of AI governance policies with only 42% Indian CEO respondents saying they have good generative AI governance in place today.

The survey also highlights other key challenges around workforce. 75% of Indian CEO respondents say that inspiring their team with a common vision produces better outcomes than providing precise standards and targets. Yet 31% acknowledge that their employees don’t fully understand how strategic decisions impact them.

Sandip Patel, Managing Director, IBM India & South Asia said, “As Indian CEOs navigate AI-led transformations within their organizations, they recognize the need for AI guardrails so that they derive real business value responsibly for growth and competitive success. However, our study reveals a gap between their intention and actual implementation. This scenario highlights the complexity of implementing AI governance, hence making a strong case for partnering with trusted experts to develop and execute effective practices and policies.”

Other key study findings include:

People and skills are at the heart of successful generative AI adoption

• 71% of Indian CEOs surveyed say that succeeding with AI will depend more on people’s adoption than the technology itself
• 49% of Indian CEO respondents says they are hiring for Gen AI roles that didn’t exist last year
• CEOs surveyed from India say 34% of their workforce will require retraining and reskilling over the next three years – up from just 6% globally in 2021

Indian CEOs recognize it takes a cultural shift to successfully scale AI, but face organizational collaboration and adoption challenges.

• 70% of Indian CEOs surveyed say their organization’s success is directly tied to the quality of collaboration between finance and technology, yet nearly half (48%) say competition among their C-Suite executives sometimes impedes collaboration.
• Nearly half (48%) of those surveyed from India acknowledge that cultural change is more important to becoming a data-driven organization than overcoming technical challenges.
• 58% of Indian CEO respondents say they are pushing their organization to adopt generative AI more quickly than some people are comfortable with

Customer experience and product & service innovation are top priorities, regulatory constraints might be hindering long-term progress

• Indian CEOs surveyed ranked customer experience and product & service innovation as their highest priorities for the next three years
• 59% of respondents say they are willing to sacrifice operational efficiency for greater innovation.
• However, nearly half (48%) of Indian CEOs surveyed point to regulatory constraints as their top barrier to innovation.
• Today, only 32% of the Indian CEO respondents are primarily funding their generative AI investments with net new IT spend, with the remaining 68% reducing other technology spend.

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As Wimbledon fever grips the world, a Generative AI-powered tool will help fans across the globe to stay up to date with personalized player stories. The All England Lawn Tennis Club, also known as the All England Club where the Wimbledon championship is held, has expanded its three-decade long partnership with IBM to further leverage GenAI. 

The tech giant’s partnership with the All-England Lawn Tennis Club (AELTC) has driven digital transformation at Wimbledon for more than 30 years. This year, we will witness a new feature for the Wimbledon digital experience that will leverage match data with generative AI from IBM’s AI and data platform, watsonx, to keep fans updated on the world’s leading players as they advance through The Championships.

The new ‘Catch Me Up’ feature displays pre and post-match player cards with AI-generated player stories and analysis via wimbledon.com and the Wimbledon 2024 App. Player cards will be personalised based on user preferences and data such as their location and myWimbledon profile, starting with their favourite players. Pre-match content will include analysis of recent performance and likelihood to win predictions, and post-match it will include key statistics and highlights. The feature will also create longer-form daily summaries of play.

‘Catch Me Up’ was built using IBM’s Granite large language model (LLM) to provide AI-generated text using the capabilities of the watsonx platform. The model has been trained on the Wimbledon editorial style and will be monitored by the All England Club. The new feature is designed to help Wimbledon scale its content to both new and existing tennis fans globally, as well as provide fans access to timelier, curated coverage across ladies’ and gentlemen’s singles matches which are often happening simultaneously.

This year, Wimbledon will also use generative AI to provide coverage of a broader range of matches than was previously available, including wheelchair events, to offer more to Wimbledon’s diverse international audience. This will form part of a redesigned digital match centre, known as IBM Slamtracker, available on the Wimbledon App and wimbledon.com. Using generative AI built from watsonx, IBM Slamtracker has been enhanced to  provide bullet point-based match previews and post-match reviews for ladies’ and gentlemen’s singles matches.

“We are committed to offering fans the highest quality Wimbledon experience, whether it’s in person or digitally. Generative AI allows us to scale our ability to provide different types of content for fans wherever they are in the world in a way that’s personalised for them. This year’s new Catch Me Up feature will make it easier for fans to follow the key storylines as they emerge throughout The Championships.”

Chris Clements, Digital Products Lead at the All England Club, 

“The new Catch Me Up feature is an exciting example of how we can use the power of generative AI to deliver compelling, insight-driven storytelling at scale. For 35 years, IBM and Wimbledon have been co-creating solutions that make fans feel more connected to all the on-court action, and our new research confirms they are beginning to understand the positive impact technologies such as generative AI can have on their digital experiences…”

Jonathan Adashek, Senior Vice President of Marketing and Communications for IBM

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Cisco Investments, the global corporate venture investment arm of Cisco launched a $1B AI investment fund to bolster the startup ecosystem and expand the development of secure and reliable AI solutions. As part of the new AI fund, Cisco is making strategic investments in Cohere, Mistral AI and Scale AI among others. Cisco has already committed nearly $200M of the $1B investment fund to date.

Over the past several years, Cisco has made over 20 AI-focused acquisitions and investments, furthering GenAI and Machine Learning capabilities and integration of AI across the Cisco portfolio. In addition to these investments, Cisco will also be working with AI companies on product collaborations that allow Cisco to co-innovate, while also being an agnostic provider and platform player in AI, offering options and coverage for Cisco’s global customer base.

The committed investments in the new AI fund include:

• Cohere: Headquartered in Toronto and San Francisco, Cohere provides security-focused frontier large language models (LLMs) and industry-leading Retrieval-Augmented Generation (RAG) capabilities tailored to meet the needs of enterprises.
• Mistral AI: Mistral AI is a global company headquartered in Paris specializing in generative artificial intelligence. The company develops new models of generative artificial intelligence for businesses, combining scientific excellence, an open approach and a responsible vision of technology.
• Scale AI: Based in the U.S., Scale AI provides a data-centric, end-to-end platform providing training and validation for AI applications. Cisco recently participated in Scale AI’s Series F funding as the round’s largest strategic investor.

“The establishment of our $1 billion AI investment fund is a testament to our commitment to the transformative power of artificial intelligence and its potential to redefine industries globally,” said Derek Idemoto, SVP, Corporate Development and Cisco Investments. “This fund represents our unwavering commitment to AI and innovation, and to the entrepreneurs and visionaries who are defining the next wave of technology.”

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Deloitte has released its “2024 Financial Services Industry Predictions,” which outlines some of the most significant emerging trends across the financial services industry over the next three to five years. Among the most significant trends, the report highlights the impact AI could have on transforming retail investing, enabling deepfakes and fraud in banking and driving new insurance offerings. Climate change is also predicted to continue to increase insurance costs for commercial real estate property owners and impact profitability of insurers.

“The seismic shifts we will see in financial services as a result of emerging technology and innovation will likely be transformative and provide new opportunities for growth,” said Jim Eckenrode, managing director, Deloitte Center for Financial Services, Deloitte Services LP. “At the same time, financial services organizations will face new risks and resiliency challenges, including market volatility, impact from climate change, talent gaps and new regulations, and organizations should plan now for what’s on the horizon.”

Highlights of Deloitte’s 2024 Financial Services Industry Predictions include:

Retail investors may soon rely on Generative AI for financial investment advice: Deloitte predicts GenAI-enabled applications will likely become the leader in advice mind-space for retail investors, growing from its current nascent stage to 78% usage in 2028 and could become the leading source of retail investment advice by 2027ⁱ. 

Generative AI expected to magnify the risk of deepfakes and other fraud in banking: Hyper-realistic fake content has never been easier to create—or harder to catch. As threats grow, banks should invest in AI and other technologies to help detect fraud and prevent losses. Deloitte predicts GenAI could enable fraud losses to reach $40 billion in the US by 2027, up from $12.3 billion in 2023ⁱⁱ.

Providing insurance coverage for AI may be a blue ocean opportunity: Regulatory guidance, large losses and market demand may drive the need for AI insurance, and some insurers and insurtech startups are already developing products in this space. While the opportunity is massive, insurers will likely also face difficulties in assessing and pricing the complex and evolving risks of AI. Deloitte predicts that by 2032, insurers can potentially write ~$4.7 billion in annual global AI insurance premiums, at a compounded annual growth rate of around 80%.

Climate change to increase commercial real estate insurance costs: The links between climate change, extreme weather and insurance costs will likely remain for the foreseeable future. Deloitte predicts that the average monthly cost of insurance for a commercial building in the US could increase from $2,726 per month in 2023 to $4,890 per month in 2030, at an 8.7% compound annual growth rateⁱⁱⁱ. For states with the greatest extreme weather risk, current monthly costs could more than double.

Climate change and homeowners’ insurance: Similarly, climate change and severe weather events are also dramatically affecting the profitability and sustainability of the US homeowners’ insurance sector. Deloitte predicts that if insurers, in partnership with government agencies and policyholders, invest $3.35 billion in residential dwelling resiliency measures, the two thirds of US homes that are not currently built to code could be made resistant enough to reduce many weather-related claims losses. These actions could, in fact, save insurers as much as $37 billion by 2030^iv.

Additional trends included in the report that are expected to shape the financial services industry:

In-app payments expected to take social commerce to the next level: Social media platforms are transforming into the new “storefronts” for consumers, who can discover and buy products within the apps. Deloitte predicts that in-app payments on social media sites could grow at a compound annual growth rate of about 30% from $19.1 billion in 2024 to $94.5 billion in 2030^v.

AI can accelerate the portfolio valuation process: AI can help private investment firms increase the frequency and transparency of their portfolio valuations and attract more retail investors. There will be benefits and challenges of using AI for portfolio valuations, such as mitigating the denominator effect, satisfying limited partners’ information needs and complying with regulatory requirements. Deloitte predicts as many as 25% of private equity firms will be using AI to augment their portfolio valuations^vii.

“As financial services firms face an ever-changing landscape, they should think about what’s on the horizon,” said Monica O’Reilly, Vice Chair, US Financial Services Industry Leader, Deloitte & Touche LLP. “Market pressures and emerging risks as well as new growth opportunities will shape business strategies, and financial services firms should prepare for that now.”

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UST, a digital transformation solutions company, has launched a massive new initiative to train over 25,000 of its employees globally in Generative AI (GenAI) and provide opportunities for career advancement. This ambitious program underscores UST’s ongoing efforts to comprehensive workforce training and upskilling, keeping its talented personnel future-ready, the company stated. 

The program comes just months after the unveiling of UST AlphaAI, which consolidates the company’s AI offerings to enhance business agility, streamline operations, and accelerate digital transformation journeys.

UST will offer a range of GenAI training programs that are specifically tailored to its employees and their different roles and responsibilities, improving efficiency while helping each employee reach their own unique potential. 

The training program will build upon insights gained through UST’s collaboration and partnership with AI researchers from renowned academic institutions such as the MIT Computer Science & Artificial Intelligence Laboratory and the Stanford Artificial Intelligence Laboratory. This legacy of close collaboration with leading researchers will help UST expand on its culture of continuous learning by bringing the latest academic breakthroughs to its workforce, according to a press release issued by the company. 

“Our comprehensive GenAI training initiative will set a new industry standard and bring over 25,000 employees up to speed on everything from foundational GenAI concepts to cutting-edge applications. Adaptable and customizable for each participant, the flexible training program provides hands-on experience with industry-leading tools and platforms like GitHub Copilot, setting our employees up for future success by ensuring that they can apply GenAI capabilities across diverse domains, said Sunil Balakrishnan, Chief Values Officer and Global Head – Development Canter Operations, UST.

“At UST, we recognize that the key to staying competitive in today’s fast-paced digital landscape lies in the continuous upskilling of our workforce. As GenAI continues to revolutionize industries and transform business operations, we remain dedicated to equipping our employees with the tools and knowledge they need to leverage these cutting-edge technologies effectively,” said Dr. Adnan Masood, Chief AI Architect, UST.

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The Generative AI (GenAI) wave is upon us. Every enterprise is buzzing about its potential, and investments are skyrocketing. But amidst the excitement, a crucial question remains: What truly separates successful GenAI projects from the rest?

In a recent visit to India, Jihad Dannawi, Regional VP – APAC, DataStax (a leading real-time vector database company) sheds light on the hidden complexities of AI projects. This exclusive interview with CIO Dimension delves into:

• The power of the right database in unlocking the true potential of Large Language Models (LLMs)
• Why developers are the backbone of GenAI and how to empower them
• India’s unique advantage in leading the GenAI revolution

Edited excerpts: 

DataStax positions Astra DB as the ‘go-to’ database for GenAI applications. Can you explain why Astra DB stands out as the best choice for GenAI applications compared to your competition?

For starters, we are a 14-year-old company that has built the most widely used enterprise-grade database. It has been delivering high scale, high performance, and high availability — even before Generative AI came in. 

We are at a stage where a lot of organizations are scaling GenAI from PoC to production. At PoC level, organizations are not looking at scale or performance. But when they move to production, they suddenly start to have performance issues. They need to be able to pull in massive amounts of data to increase the relevancy and accuracy of the models, which means that the database must be bigger.

But volume often comes in the way of performance at the production stage. When organizations move beyond entry level scenarios to more mature use cases, they need to have a database that can handle their speed, scale, and performance requirements      – all at a reasonable TCO. A lot of databases in the market struggle to balance these aspects. 

An industry benchmark clearly demonstrates that DataStax’s Astra DB Serverless (the cloud-native database service built on Apache Cassandra) significantly outperforms the competition in throughput, latency, relevance, and TCO.  

Another key offering from DataStax is RAGStack, which claims to reduce the complexity associated with Gen AI for developers? Could you elaborate? 

When it comes to generative AI, one of the key issues that the developer community faces – especially when they start building the big stuff for production – is that they don’t know where to start. Because there are so many components and tools available in the market, it’s hard to decide what to use –  especially when dealing with vector databases.   

That’s why DataStax built a layer for the developers called RAGStack. This helps them build production-grade retrieval augmented generation (RAG) Gen AI applications in a simpler and faster way – without the developers having to learn everything from      scratch. It’s fully open source, connected and improves developer productivity significantly. It drastically simplifies the process of implementing RAG – the process of providing context from outside data sources to deliver more accurate LLM query responses. 

RAGStack is part of our vision is to enable and empower developers to build GenAI applications in production more efficiently. We are helping developers and companies to quickly innovate.  Developers do not come with AI skills; they don’t know how to build LLMs. We firmly believe that the GenAI journey of an organization starts from the developer, and not the AI team. With RAGstack, they can start right away, without waiting to acquire new skills. 

Another major concern that organizations must deal with is the AI hallucination. How is DataStax working to ensure fewer hallucinations? 

Hallucination, or in other words the “context” for an LLM, is the tricky bit. Now technology allows you to launch GenAI very quickly, but GenAI is not always accurate. This is why retrieval augmented generation (RAG) is so important for enterprises building GenAI applications. They must be able to augment an LLM with relevant, up-to-date data so it can provide relevant, factual responses.

More data means better context. And this will also bring in the big competitive differentiator into the models and the eventual outcomes for businesses. This further emphasizes the point that many databases in the market that claim to be vector do not have the capacity to perform high volume data. 

Can you share some examples of how Astra DB has helped customers achieve significant business outcomes?

Through Astra, we have started to discover a lot of customers with smaller needs, who can move very quickly through the GenAI journey. When customers begin to deploy impactful GenAI in their business processes, we believe that we stand to gain.  

A great case in point is Skypoint, the Portland-based Gen AI healthcare company. They leverage Astra DB’s vector search to enhance their services, offering users real-time, contextual, and personalized AI agent solutions. 

Another key customer for us is India’s leading edtech player, Physics Wallah. This unicorn’s app has over 15 million downloads as of January 2024. Its personalized AI bot is built on Astra DB and assists students with their academic and support queries in a personalized manner. The database’s stability helped the company handle 50x traffic increase with zero downtime during a live demo with 1 million students.  

A lot of organizations are still in the experimentation stage. Our study shows that only 4% of GenAI projects have moved to production, so we are in the absolute beginning of the wave, a lot of the     projects in PoC will move in     to production this year.      

India’s GenAI market is predicted to reach a massive $17B by 2030 and India is reportedly ahead of the US in AI adoption by businesses. What will be the catalysts for this growth and what are DataStax’s plans to capitalize on this opportunity?

AI will impact the top line and bottom line of every business out there. India has a strong community of developers and GenAI starts with developers. So, India has the skills and the mass to innovate and drive more use cases. They      also have the right mindset – the ecosystem here loves to build and that’s a very positive mindset to have when it comes to AI. Because AI is not something you buy and turn on, it’s something you build. And with the right mindset, India will be faster to innovate. The GenAI wave is poised to trigger a paradigm shift, comparable to the impact of mobile and web technologies. India has the skills, capacity and the mindset to really benefit.  

From a DataStax point of view, India is a big market and we are definitely investing here. We are expanding our team to meet to the growing needs.  

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