The SaaS paradigm has revolutionized software distribution by offering flexible pricing options to meet diverse customer needs. However, the rapid expansion of the SaaS market has introduced significant complexity for DevOps teams, who must manually manage and evolve pricing structures, an approach that is both time-consuming and prone to errors. The absence of automated tools for pricing analysis restricts the ability to efficiently evaluate, optimize, and scale these models. This paper proposes leveraging intelligent pricing (iPricing), dynamic, machine-readable pricing models, as a solution to these challenges. Intelligent pricing enables competitive analysis, streamlines operational decision-making, and supports continuous pricing evolution in response to market dynamics, leading to improved efficiency and accuracy. We present an LLM-driven approach that automates the transformation of static HTML pricing into iPricing, significantly improving efficiency and consistency while minimizing human error. Our implementation, AI4Pricing2Yaml, features a basic Information Extractor that uses web scraping and LLMs technologies to extract essential pricing components, plans, features, usage limits, and add-ons, from SaaS websites. Validation against a dataset of 30 distinct commercial SaaS, encompassing over 150 intelligent pricings, demonstrates the system's effectiveness in extracting the desired elements across all steps. However, challenges remain in addressing hallucinations, complex structures, and dynamic content. This work highlights the potential of automating intelligent pricing transformation to streamline SaaS pricing management, offering implications for improved consistency and scalability in an increasingly intricate pricing landscape. Future research will focus on refining extraction capabilities and enhancing the system's adaptability to a wider range of SaaS websites.

This study presents a novel learning approach designed to enhance both mathematical reasoning and problem-solving abilities of Large Language Models (LLMs). We focus on integrating the Chain-of-Thought (CoT) and the Program-of-Thought (PoT) learning, hypothesizing that prioritizing the learning of mathematical reasoning ability is helpful for the amplification of problem-solving ability. Thus, the initial learning with CoT is essential for solving challenging mathematical problems. To this end, we propose a sequential learning approach, named SAAS (Solving Ability Amplification Strategy), which strategically transitions from CoT learning to PoT learning. Our empirical study, involving an extensive performance comparison using several benchmarks, demonstrates that our SAAS achieves state-of-the-art (SOTA) performance. The results underscore the effectiveness of our sequential learning approach, marking a significant advancement in the field of mathematical reasoning in LLMs.

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I am a postgraduate in Computer Science (Cyber Security) from IIT Bombay and hold a keen interest in Cyber Security, Blockchain, AI & IoT, Electric Vehicle (EV) among other deeptech areas. At the application level, I love SaaS applications. Currently, I am the Founder & CEO of Tosh Innovations Private Limited, a Gurugram based Bootstrapped, Cash positive EdTech Company. Tosh Innovations offers online educational certifications in deeptech areas. Tosh Innovations is a Bootstrapped company of 30 young people who create exceptionally informative content for a global community.

When Steve jobs held the iPhone on Jan 9, 2007, for the first time at the mac conference, millions gazed with a great wow. Very few saw this as a huge opportunity in alternate directions on how to catalyze this innovation. By 2008, he demonstrated his location-tracking app, Loopt at the Apple developer conference and got a "cool" tag congrats message from Steve Jobs. Whatever we have to do with day today's digital interaction with smartphones, it could be credited back to the innovation that was triggered by iPhone. I bet Sam himself would give his innovation credit back to this marvelous piece.

The fintech industry is constantly evolving and improving, and 2023 is no exception. It is predicted that the fintech space will multiply and reach $174 billion in 2023. The banking industry has recently experienced a massive surge in adopting fintech solutions as users become more tech-savvy. There are several emerging technologies in Fintech, and I have highlighted some of the most predominant ones that will be witnessed in 2023 below. AI and ML can transform investments, payments, banking, risk management, and related industries.

As the world leader in SaaS Management and Optimization, Zylo enables companies to organize, optimize, and orchestrate SaaS. Organizations large and small trust Zylo's enterprise-proven technology and unparalleled SaaS Management expertise to optimize more than 30 million SaaS licenses and $23 billion in SaaS spend. Zylo's patent-pending, AI-powered Discovery Engine provides continual, frictionless monitoring of SaaS spend, licenses, and usage to create the industry's most trusted SaaS system of record. Fueled by more data than any other provider, Zylo delivers insights that allow you to take action quickly to optimize growing SaaS portfolios. We are looking for a Machine Learning Engineer who will help bring Zylo's discovery engine into the next phase of growth.

The Internet of Things (IoT) defines the network of physical objects "things" embedded with software, sensors, and other technologies to connect and trade data with different gadgets and systems over the internet. These devices vary from standard household objects to sophisticated industrial tools. More than 7 billion are connected to IoT devices today, and specialists expect this number to expand upto 22 billion by 2025. We can combine everyday objects, thermostats, kitchen appliances, cars, baby monitors to the internet via entrenched devices; seamless communication is feasible between people, processes, and things. By Utilizing low-cost computing, big data, the cloud, analytics, and mobile technologies, material things can transfer and compile data with the tiniest human intervention. In this hyperconnected world, digital systems record, monitor, and adjust each interaction between related items.

Over the last ten years, SaaS has changed the way we work -- for the better. SaaS is helping tech companies move so much faster, but it's also introducing a new surface area of risk they've never seen before. As a result, companies are faced with answering the difficult question of how to manage compounding security risks as they grow without introducing more red tape that slows their team members down. At Metomic, we help tech companies save time & reduce costs by minimising their sensitive data footprint in cloud applications like Slack, Google Drive and Zendesk. Our technology includes native integrations with dozens of cloud apps, AI to detect thousands of sensitive data types, and advanced functionality to manipulate data at scale (redaction, retention, access controls, etc).

AI is becoming a powerful disruptive technology in recent years. One poll showed that 86% of CEOs feel that AI is a mainstream technology that their companies heavily depend on. According to a recent report by SaaS purchasing platform Vertice, 90% of buyers are overpaying for their SaaS, by an average of 20-30%. Many of these buyers are enamored with the idea of using AI to improve their business models, but don't make sure that they are getting the right value from it. And with these tools responsible for so many indispensable business operations, this figure should be ringing alarm bells for companies of all sizes.