Service As A Software: How AI Redefines Business?
agination. Professional expertise is now just a click away, faster, smarter, and more affordable than ever. This shift has redefined SaaS not just Software as a Service, but Service as a Software. In this blog, we will learn what is Service as a Software and How AI can Redefine Business? Need For Service as a Software – Traditional SaaS Challenges It’s been years that businesses depend on traditional Software as a Service to make operations efficient but there is a gap that remains. Saas that businesses use still require human expertise to operate. From marketing campaigns to analyzing financial data and optimizing supply chains, businesses need skilled professionals to drive results and make informed decisions. It opens up many challenges for businesses like: How Service as a Software (SaaS 2.0) Solves These Challenges Saas 2.0 or Service as a Software represents the new era of AI based digital transformation for businesses. It offers autonomous, intelligent and scalable solutions to replace manual efforts of humans and decision making. Traditional Software as a Service which provides cloud-based tools always requires user input. In contrast, Saas 2.0 functions as an expert system as it has underlying technologies like AI and machine learning to complete all tasks independently. If businesses adopt Service as a Software, industries can achieve: Industry-Specific Benefits of Service as a Software Boxed software can be according to basic business needs but it sometimes falls short in addressing the unique challenges of specific industries. Industry-specific service as a software offers greater customization, efficiency, and support, helping businesses streamline operations and gain a competitive edge. Below are the key benefits for dealers looking to maximize their software investment. Finance & Banking: Conventionally financial analysis and risk assessments were only done by teams of experts but it was prone to delays and high operational costs. But, AI integration in fintech platforms can provide real-time credit scoring, fraud detection, and automated investment advisory without human intervention. On the other hand, banks and hedge funds must integrate AI-powered risk assessment and algorithmic trading to stay competitive. Healthcare & Pharma: In the healthcare industry and pharma, there is limited scalability as the tasks like medical diagnostics, drug discovery, and patient management have to be dependent on highly skilled professionals. The challenges can be solved by automated diagnostic software. It can help detect diseases from imaging scans, predict patient deterioration, and suggest treatment plans faster than doctors. That’s why hospitals and pharmaceutical companies should pay attention and decide to integrate service as a software like AI predictive analytics and RPA (Robotic Process Automation). It will not only improve patient care but also help with drug discovery. Legal & Compliance: The major problems of compliance sectors are manually done tasks like legal research, contract analysis, and compliance audits. These tasks are time-intensive and require costly legal professionals. But the solution lies in AI-based legal platforms that can draft contracts, conduct due diligence, and monitor regulatory compliance instantly. This is why, law firms and corporate legal teams should adopt AI-powered contract lifecycle management to reduce costs and improve efficiency. Marketing & Advertising: Marketing and advertising companies traditionally rely on manual A/B testing when running ad campaigns. It is just a guesswork which can lead to suboptimal performance. AI in marketing and advertising can help predict real-time consumer behaviour tracking and content creation that in result will maximize ROI. Brands and agencies can transition their business to clients by implementing AI driven marketing automation for hyper-personalized customer engagement. E-commerce & Retail: Inventory management, pricing optimization, and customer personalization require vast human resources. AI in ecommerce and in retail can automate demand forecasting, dynamic pricing, and chatbot-based customer service. It will also enhance sales performance. Retailers must integrate service as a software with AI-powered recommendation engines and automated logistics for seamless scalability. Manufacturing & Supply Chain: The major drawback that supply chain and manufacturing industry face is inefficiency with inventory management, demand prediction and logistics. AI in the supply chain can track real-time inventory with predictive maintenance and route optimization in logistics. However, if manufacturing industries and supply chain companies get AI based predictive analytics in service as a software, they can automate and minimize disruptions to enhance overall productivity. Why Businesses Need to Implement Service as a Software Now! It’s now or never. Because the transition from Software as a Service to Service as a Software is a necessity to remain competitive in an era where automation, efficiency, and scalability define success. There are successful examples like salesforce, slack, mailchimp and zoom. Seeing them, businesses must invest in AI-powered SaaS solutions to: Transform Your Business with AI-Powered Service as a Software OptimusFox is a web3 development company providing AI development services to transform companies worldwide. Our team is expert in AI app development, mobile solutions, and robotic process automation (RPA). We strive to help businesses move beyond traditional SaaS by integrating intelligent automation solutions like service as a software and white label software solutions that scales effortlessly. No matter, if you are looking to streamline your enterprise operations or to enhance your customer engagement or automate complex workflows in an organization, our cutting-edge solutions will help you to reduce costs, boost efficiency, and drive growth. Wrapping Up It is evident that AI is now the consultant, designer, and researcher and it can automate almost every task that once required human effort. Additionally, it does not eliminate the whole human intervention but human AI collaboration can make a transition. As you know, traditional SaaS offered cloud-based software on demand; now, AI is taking it further, delivering professional services as intelligent, automated solutions available anytime, anywhere. This means, Service as a Software (SaaS 2.0) doesn’t just offer tools but it also assists companies with AI-driven solutions to act as the expert, making decisions, analyzing data, and delivering real-time solutions without human intervention. Additionally, industries that delay adoption risk falling behind as AI-driven SaaS solutions become the standard for efficiency, innovation, and profitability. The question is no longer if AI will take over
Why Companies Should Use Big Data Analytics In Retail?
Introduction: Traditionally it was simple to sell products and today in 2025, retailers should understand their customer persona to drive ultimate growth and profitability. Data centric selling is one the best tricks these days. In essence, every click, swipe and purchase tells a story to retailers about customer mindset. It is important to leverage big data analytics in retail to get a clear understanding of customers interest, optimize operations, and ultimately boost their bottom line. According to Dataforest, As a result, Retail makes $26 trillion every year and provides jobs for 15% of the world’s workers. Moreover, studies show that every time we swipe a credit card, tap our phone to pay, or click “buy now” online, we’re creating valuable data bits. In other words, this data is used by businesses later to understand customer interests, demographics which in result improve sales. This means, Big data analytics are necessary for retailers. In this blog, we will learn the how traditionally retailers used to get sales and how big data analytics help them accelerate sales AKA benefits of big data analytics in retail: Role of Big Data Analytics in Retail According to Mordor Intelligence, global big data analytics in retail market size was valued at $6,3 billion in 2024, and is projected to reach $16,7 billion by 2029. These are not just numbers but it shows the significant role of big data analytics in retail to achieve retail goals. This image tells how big data management enhances the retail industry by integrating various data sources to provide a 360-degree view of the customer. There is a flow of high-volume data from different sources like shopper data, market data, supplier data, and retailer data. These factors are integrated and transformed into actionable insights. However, these insights support demand-based forecasts and analytics. As a result, businesses get support in optimizing on-shelf availability, promotional effectiveness, budget planning, category management, and competitive awareness. Lastly, this approach allows retailers to make data-driven decisions which can later enhance customer satisfaction and overall business performance. Retail Before Big Data: In the past, retail system relied on manual tracking and guesswork. Back then, store managers counted inventory with clipboards, track sales in notebooks and make informed decisions based on the past trends only. Customers review products with casual chats and comment cards. Moreover, marketing was based on general assumptions rather than precise data. Furthermore, Retail lacked many things as planning for sales and promotions was slow and often inaccurate. It has no insights that retailers now get from analytics. Big Data Analytics: How Retail Got Smarter Unlike before, now retailers can use big data analytics. Instead of just looking at last month’s sales, stores now collect huge amounts of data from social media posts and weather forecasts to how long you spent in aisle seven last week. For instance, big companies use powerful technologies for storing data, fast calculations, and predict what customers will buy next. This helps them personalize shopping, keep the right items in stock, and change prices quickly. Benefits of Big Data Analytics in Retail Big data analytics is a game-changer for retail businesses to boost efficiency, increase profits, and create better shopping experiences. Let’s see some of the benefits: 1. Improved Demand Forecasting Big data analytics helps retailers predict what customers will buy and when, allowing them to stock the right products at the right time. This reduces stock shortages and prevents overstocking, leading to better inventory management and higher profits. 2. Better Customer Segmentation Instead of broad categories, retailers can create highly detailed customer groups based on shopping habits, preferences, and behaviors. This leads to personalized marketing that resonates with individual shoppers, increasing customer loyalty and sales. 3. Real-Time Dynamic Pricing Retailers can adjust prices instantly based on demand using big data analytics for competitor pricing, and customer behavior. a=Additionally, this ensures they remain competitive while maximizing profit margins. 4. Optimized Inventory Management By analyzing past sales trends and seasonal demands, big data helps stores stock exactly what customers want, reducing waste and avoiding unsold inventory. 5. Enhanced Customer Experience With AI-powered big data analytics, retailers can get recommendations and personalized offers, shoppers feel valued and understood. Retailers like Amazon and Sephora use big data to tailor product recommendations, leading to higher engagement and satisfaction. 6. Big Data Analytics For Supply Chain Efficiency Big data analytics helps track supplier performance, delivery times, and warehouse efficiency, ensuring that products reach stores and customers without delays or extra costs. Consequently, fewer stock outs and faster deliveries. 7. Identifying Underperforming Products and Stores Retailers use data analytics to spot which products or locations aren’t performing well. They can then replace slow-moving items with high-demand products or make changes to boost store performance. 8. Boosted Sales with Predictive Analytics Retailers can anticipate shopping trends before they happen. Big data analytics helps analyze past sales, weather patterns, and online behavior, they launch better promotions and stock the right products ahead of time. 9. More Effective Marketing Campaigns Big data analytics in retail enables hyper-targeted marketing, ensuring that ads and promotions reach the right audience. Personalized ads and offers increase engagement and drive sales. 10. Competitive Advantage of Big Data Analytics The retailers who leverage data effectively stay ahead of their competition by offering better pricing, a smoother shopping experience, and the right products when customers need them. Therefore, those who don’t keep up risk falling behind. Conclusion To sumup, Big data and AI-driven solutions provide real-time insights to improve inventory management, optimize pricing strategies, and enhance customer experiences. With advanced analytics, predictive modeling, and intelligent automation, retailers can make data-driven decisions that boost efficiency and profitability. Ultimately, businesses need to leverage AI-powered big data solutions to stay ahead of market trends, personalize customer interactions, and streamline operations for long-term success. Solve Retail Problems with AI-Powered Big Data Solutions Optimusfox is a pioneer in AI development services providing big data solutions for enterprises and startups. Our big data experts leverage AI-powered big data solutions to help retailers make smarter,
Kimi AI: China’s Another AI Drop To Redefine AI Reasoning
China is advancing AI at a breakneck pace. After Deepseek r1 headlines, another company named Moonshoot AI dropped Kimi AI 1.5. It is a model that is routing superior to Open AI GPT-4o and DeepSeek AI r1 model. The best part of Kimi AI is that it shows advancements in multimodal reasoning, long-context understanding, and real-time data processing, raising questions about the future of AI dominance. For the record, there’s a long-standing cliché: the U.S. innovates, China replicates, and Europe regulates. But we’re not here to dwell on geographic stereotypes. Instead, we’re looking beyond them to assess how Kimi AI k1.5 is disrupting the AI industry and what its rise means for the future of artificial intelligence: The Startup Behind Kimi AI – Moonshot AI Moonshot AI was founded in 2023 by the youngest CEO Yang Zhilin and is now one of the top AI companies. The company may be new but its rapid growth in AI is remarkable. According to stats, the company secured major funding from Alibaba, Tencent, and other investors, raising its valuation to $3 billion in just one year. What Is Kimi AI? Kimi AI is introduced by a company named Moonshot AI which is a Beijing-based startup. Kimi AI is a large language model (LLM) that understands and generates human-like text responses, particularly in Chinese. Amazingly, this AI tool can handle up to 2 million Chinese characters in a single prompt. It is a highly effective model to analyze lengthy documents and handle complex tasks. Moreover, Moonshot AI is positioning Kimi as a cost-effective yet powerful alternative to the frontier models. It can surpass models in performance like OpenAI’s GPT-4 and DeepSeek’s latest iterations. How Is It Different From Other Frontier AI Models? OpenAI is designed to solve complex problems by breaking them into small pieces. But Kimi k1.5 is better at handling math and coding problems while working with multiple types of data such as text, images and videos. It is setting new records in multiple areas like in advanced reasoning it scored 77.5% which means its surpassing other models. In complex mathematical problem solving it achieved an impressive 96.2 which is exceptional accuracy. Moreover, in visual understanding tests it scored 74.9% which means it has advanced abilities to process images and graphics. This means, Kimi k1.5 is faster and more versatile than any other. It can handle a variety of tasks, like math, coding, and processing text, images, and videos, more efficiently. Unlike DeepSeek-R1, which mainly focuses on text, Kimi k1.5 is more powerful and flexible. Moreover, there is another important fact that Kimi k1.5 costs less to develop than similar AI models in the U.S. The creators of Kimi believe it can compete directly with OpenAI’s O1, and its strong test results support this claim. What Sets Kimi AI 1.5 Apart? Kimi AI is not less than GPT like models. It has advanced AI model capabilities that are pushing the boundaries of reasoning, multimodal intelligence and real time data retrieval. Let’s see some of the features that sets Kimi from the competition in AI industry: Extended Context Memory: Kimi AI can handle 128k tokens at once. It makes it an ideal AI model for processing long-form documents and conversations without losing context. Existing models struggle with memory limitations so when you work with extensive research papers, tech documentations and in-depth research, Kimi AI k1.5 can be your go-to to get continuity and accuracy. Free and Unlimited Access: Existing AI tools come with subscription fees but Kimi AI is free and provides unlimited access to users which makes it an attractive option for users. However businesses and AI enthusiasts can use Kimi AI without any upfront costs. Real-Time Web Browsing: AI models rely on pre-trained data but Kimi AI 1.5 features real-time web browsing capabilities. It has the capability to scan over 1,000 websites instantly. It can pull up-to-date information to provide more accurate and relevant responses. Means that its prowess in financial analysis is already demonstrated by users. Kimi can assess stock trends and news in real time and this is something GPT-4 and DeepSeek currently struggle with. Multimodal Reasoning: Kimi is not text-based only but it can process multiple forms of data, including text, images, and charts. It has the ability to generate insights that consider multiple input sources. This feature makes it far more sophisticated than standard chatbots. AI Benchmark Performance: As mentioned earlier, Kimi AI 1.5 has outperformed GPT-4 and Claude 3.5 Sonnet in various technical benchmarks. This includes coding and mathematics. In the MATH 500, Kimi achieved an outstanding 96.2% accuracy rate proving that it is a high-level problem solver. The Future of AI: Rapid Expansion Moonshot AI’s Kimi model has surged from handling 200K Chinese characters in October 2023 to an astonishing 2 million by March 2024. This tenfold increase in just six months signifies a transformative shift in AI capabilities. This shows Kimi AI k1.5 is definitely showing a major shift in AI dominance. After deepseek AI launch and then kimi and qwen, China emerges itself as a serious contender in the race for artificial general intelligence (AGI). What This Means for AI’s Future and the Industry? Exponentially, AI models are becoming better at retaining and processing vast amounts of information within a single interaction. Kimi AI has revolutionized how AI handles long documents, research papers, coding tasks, and creative writing by enabling deeper comprehension and more nuanced responses. We don’t know about the future yet but since OpenAI, Google, and Anthropic are major players, Moonshot AI’s advancements suggest that China is positioning itself at the forefront of AI development. Sum and Substance – A New Wave of AI Development Competition After all the research and this article, we can say that Kimi AI stands out with its high reasoning power, long-context handling, and free unlimited access. It represents a significant leap in artificial intelligence reasoning, accessibility, and real-time processing. With backing from China’s biggest tech giants and a pricing model that undercuts its competitors,
DeepSeek vs ChatGPT: Can China’s AI Disrupt U.S. Tech Giants?
The recent launch of DeepSeek AI R1 model has turned heads in the AI Industry. According to China, they have spent only $6 million per training run on their model, compared to the tens of millions required for U.S. competitors. This is amazing right, the social is full of the buzz Deepseek vs Chatgpt? Moreover, Its commercial pricing is also impressively low. According to DocsBots Website mentioned by Statistica, with 1 million tokens costing only 55 cents to upload. This rapid success raises important questions: can a Chinese AI model truly challenge the U.S. AI dominators without sacrificing quality and security? In this post, we’ll compare cost and performance between top U.S. and Chinese AI infrastructures, to find out best open-source LLM mainly focusing on DeepSeek vs ChatGpt and others like Qwen, Gemini and Llama. We will also explore if China’s AI disruptors can truly outperform their U.S. counterparts. Understanding AI Infrastructure and LLM Costs AI infrastructure is a combination of hardware, software, and cloud services required to train and deploy AI models. When developing cutting-edge AI models like ChatGPT, Gemini, or DeepSeek, they require massive computational power which often involves specialized chips, vast datasets, and advanced training techniques. Typically, training a large language model (LLM) involves millions of dollars in computational costs. According to analysis, running ChatGPT costs approximately $700,000 a day. That breaks down to 36 cents for each question. The US models also demand extensive datasets, advanced algorithms, and constant tuning to ensure they perform at the highest level. Technical Components LLMs Require: The Evolution of AI Training Costs (2017-2023) The evolution of AI training costs has seen an astonishing rise over the years. It reflects the growing sophistication and scale of large language models (LLMs). AI training costs have soared from modest beginnings to reach hundreds of millions today. This rise reflects the growing complexity of large language models (LLMs). Let’s examine how the increasing sophistication of AI models has led to this sharp escalation in development expenses. The above image presents a fascinating timeline of AI model training costs from 2017 to 2023. It shows a dramatic increase in investment over the years. If you see the visualization, it notes that these figures are adjusted for inflation and were calculated based on training duration, hardware requirements, and cloud computing costs, according to The AI Index 2024 Annual Report. US AI Models – The Pioneers The U.S. has long been the leader in artificial intelligence development. Here are several tech giants that are driving innovation in tech space: It was developed by OpenAI and has revolutionized as conversational AI. With iterations like GPT-3 and GPT-4, it remains one of the most advanced models on the market. Training a model like ChatGPT costs upwards of $78 million, reflecting its complexity and the computational power required. ChatGPT app development costs can range anywhere between $100,000 to $500,000. The factors that affect the cost are the dataset’s size, the chatbot’s end-use case, the services, the features required, etc. Claude AI is created by Anthropic. The ai model has emerged as a leading conversational agent as it provides an alternative to ChatGPT with a focus on safety and alignment. The development costs are significant but vary depending on deployment and specific business use cases. Meta’s Llama series is a key competitor in the open-source AI space. While the models are cheaper to access for businesses, developing applications using Llama models still incurs considerable costs mainly for larger-scale integrations. Google’s Gemini is the most expensive AI model in terms of training costs, requiring $191 million for development. It’s designed to handle more complex datasets, including multimedia formats. Despite its higher costs, Gemini is known for its reliability and performance across various tasks. China’s AI Models: A Low-Cost Revolution Recently, China has begun making waves with its innovative, cost-effective alternatives. Chinese companies are challenging the traditional AI ecosystem by introducing similar or better performance at a fraction of the price. Here are some of the newest models of AI: DeepSeek AI launch of its R1 model has sent shockwaves through the AI industry. With a development cost of just $6 million, DeepSeek has proven that cutting-edge AI can be achieved on a lean budget. Its pricing structure is also far more accessible, with 1 million tokens costing only 55 cents to upload. Despite the lower costs, DeepSeek’s model has earned strong performance reviews, often outperforming U.S. models in key benchmarks. Last night, Alibaba launched their AI offerings, including the Qwen series. It quickly gained traction as a viable alternative to expensive models like GPT-4. With a heavy focus on cloud-based AI solutions, Alibaba provides highly competitive pricing, ensuring that businesses can scale AI-powered applications affordably. Moonshot’s Kimi series is a rising star in China’s AI scene. But, it is a less-known AI architecture. However, the Kimi K1.5 has been praised for its efficiency and cost-effectiveness. As it is giving companies an affordable way to implement AI without compromising on quality. The Chinese AI model, ByteDance is known for revolutionizing social media through TikTok, ByteDance is also making strides in AI. Doubao 1.5 Pro is one of their leading LLMs, offering impressive capabilities at a significantly lower cost compared to its Western counterparts. Estimating AI Development Costs The cost of AI development varies greatly depending on the scale, complexity, and project requirements. From infrastructure to labor, software, and training, each component contributes to the overall cost. On average, businesses can expect to invest between $10,000 to $50,000 or more in AI projects. Key Cost Components: Cost Breakdown: Is DeepSeek-R1 Really a Threat? In particular, DeepSeek-R1 has been disruptive due to its low costs and strong performance. But longevity is controversial. However, that model only spends $6 million per training run, far less than models like ChatGPT or Google’s Gemini, which can cost tens of millions. Its commercial use pricing also reflects this, with 1 million tokens costing only 55 cents to upload and $2.19 to download, which is significantly cheaper than U.S.-based