Off-the-Shelf or Custom? Choosing the Best LLM for Your Enterprise
As AI continues to transform industries at an exponential pace, large enterprises are facing a critical decision: Should they opt for off-the-shelf LLMs or invest in custom-built solutions? The rapid growth of artificial intelligence offers companies the opportunity to streamline operations, enhance customer experiences, and unlock new insights. For instance, about 67% of organizations worldwide are using generative AI powered by LLMs to improve operations and processes across different industries. Off-the-shelf models, while offering speed and cost-effectiveness, often fall short when it comes to handling specific, industry-related challenges or ensuring the control and privacy of proprietary data. Custom LLMs, on the other hand, are built to meet the unique needs of each business, providing more flexibility and control but requiring a more considerable investment in time and resources. At Intellivon, we specialize in guiding enterprises through this decision-making process. Our hands-on expertise in custom LLM development empowers businesses to choose the best AI solution tailored to their long-term goals. With a focus on scalability, data privacy, and future-proofing, we help enterprises make data-driven decisions that support both immediate needs and future growth. In this blog, we will walk you through which LLM version you should pick for your enterprise needs and how we build and deploy custom LLMs from scratch. Understanding the Need For LLMs in Modern Enterprises According to a report by Research and Markets , the LLM market was valued at $5.72 billion in late 2024 and could grow to over $123 billion by 2034, with an annual growth rate of around 36%. LLMs are dominating almost every sphere of business operations and will continue to escalate in the coming years, which makes their adoption in large enterprises vital. Key Market Takeaways: About 67% of businesses worldwide are using large language models (LLMs) or generative AI to improve their operations. 72% of companies plan to increase their investment in LLMs this year, showing a strong commitment to the technology. Nearly 40% of companies are already spending over $250,000 annually on LLM adoption. By 2026, more than 80% of companies are expected to use generative AI or related APIs, a huge jump from just 5% in 2023. Over 70% of global businesses are using AI in at least one part of their operations, with generative AI playing a key role in areas like marketing, sales, IT, and customer service. Google’s LLMs are used by 69% of companies, while OpenAI’s models are used by 55%, according to recent surveys. The Role of LLMs in Enterprise AI As businesses adopt AI, LLMs are becoming essential tools for innovation and efficiency. These models can understand and generate human-like text, making them powerful for various enterprise functions. From automating customer support to analyzing data, LLMs help businesses improve processes and scale operations. As AI adoption grows, LLMs are becoming key to staying competitive. 1. Automating Customer Support LLMs are transforming customer support by automating responses and offering real-time assistance. AI-driven chatbots and virtual assistants can handle both simple and complex customer queries, reducing the workload on human agents. For example, Salesforce uses LLM-powered chatbots to manage customer interactions, speeding up response times and boosting customer satisfaction. 2. Enhancing Data Analysis and Insights LLMs can analyze massive amounts of unstructured data, such as reports and social media posts, to provide valuable insights. For example, a retail company can use LLMs to analyze customer feedback and identify emerging product trends. This helps businesses make quicker, more informed decisions and improves their data-driven strategies. 3. Scaling Operations Efficiently with LLMs LLMs help businesses scale by automating tasks that traditionally require human effort. For instance, large enterprises can use LLMs to automate data entry, organize documents, and manage internal communications. This leads to fewer errors and increased productivity, making it easier to scale operations without significantly raising costs. 4. LLMs in Predictive Analytics LLMs enhance decision-making by analyzing past data and providing predictions based on identified patterns. For example, financial institutions use LLMs for fraud detection, identifying suspicious activity by analyzing transaction trends. In healthcare, LLMs can predict patient outcomes, allowing providers to optimize treatment plans and improve care. Understanding Off-the-Shelf LLMs Off-the-shelf LLMs are pre-trained, general-purpose AI models like GPT, BERT, and Llama. These models are widely used because they are readily available and have been trained on massive amounts of data. They can generate human-like text, making them suitable for many basic tasks, such as chatbots, content generation, and customer support automation. Their popularity comes from their quick deployment and the ability to handle general use cases without needing specialized training. Advantages of Off-The-Shelf LLMs 1. Quick Deployment: Off-the-shelf LLMs are designed to be ready-to-use right out of the box. This means businesses can deploy them quickly with minimal setup time, allowing them to start benefiting from AI immediately. 2. Cost-Efficiency: Compared to building a custom model from scratch, using an off-the-shelf LLM is more affordable. These models offer a low-cost entry point for businesses experimenting with AI, without requiring a large upfront investment. 3. Proven Technology: Since these models have been used by countless organizations and tested in various scenarios, they are reliable for standard applications. Many well-documented resources and communities exist to help enterprises integrate and troubleshoot these tools. Limitations of Off-the-Shelf LLMs for Enterprises 1. Lack of Customization: While off-the-shelf models are powerful, they are general-purpose and not tailored to specific industries or business needs. For example, a legal firm using a general LLM might face challenges with context-specific language or regulations, leading to inaccurate responses. This can result in higher error rates and increased reliance on human oversight to correct mistakes. 2. Data Privacy and Security Concerns: Using third-party APIs for these models means sending sensitive, proprietary data to external providers. This can create significant security risks, especially if the provider doesn’t meet your organization’s privacy standards. For industries governed by strict regulations, such as finance (GDPR), healthcare (HIPAA), or consumer protection (CCPA), this can lead to non-compliance issues and expose your business to legal risks. 3. Scalability Issues: As
Why Enterprises Need Private AI Systems
What if your enterprise’s confidential data became the training data for your competitor’s AI? This is a very real risk in today’s business world. As AI grows more powerful, competitors can gain access to your data and quickly launch products or solutions based on it. The speed at which AI can process and learn from data means that the advantage you once had can vanish in the blink of an eye. About one-third (32%) of cyber incidents in 2024 involved data theft and leaks, solidifying the need for systems that can help you scale AI, yet keep it private and compliant. Public AI solutions often put businesses at risk. They can expose sensitive information and make compliance harder. This is why enterprises need private AI systems that offer more control over their data. Around 60-70% of enterprises are now using or building private AI systems to ensure data privacy and maintain a competitive edge. Intellivon specializes in designing scalable private AI systems for large enterprises, keeping security and compliance at its core. Our AI experts leverage our suite of cutting-edge tools dedicated to crafting 100% compliant and secure AI systems. We build and implement private AI systems specifically customized to fit your unique enterprise needs. Our solutions give you full control over your data while ensuring security, privacy, and compliance. In this blog, we walk you through why your enterprise needs these private AI systems and how we develop them from the ground up. What is Private AI and Why Enterprises Need It The global generative AI market was worth $25.86 billion in 2024 and is expected to grow to $1005.07 billion by 2034, with an annual growth rate of 44.20% from 2025 to 2034, according to Precedence Research. Around 60-70% of businesses are adopting or building private AI systems to have better control over data privacy and security, and to stay competitive. Many companies are investing heavily in AI, with 88% of mid-to-large organizations spending more than 5% of their IT budgets on AI, particularly on private AI. North America is leading in private AI investments, with over $109 billion spent on private AI in 2024 alone. What is Private AI? Private AI refers to artificial intelligence systems that are designed and deployed within a private, secure environment, specifically for a single organization. Unlike public AI, which relies on cloud services and shared data, private AI ensures that the enterprise retains full control over its data. This means that sensitive business data never leaves the company’s secure infrastructure, minimizing risks associated with data exposure, security breaches, or misuse. Private AI systems are custom-built to meet the specific needs of an enterprise. They can be tailored to work with the company’s existing infrastructure and data while providing the necessary level of security. This makes private AI a preferred choice for large organizations that deal with sensitive customer information, intellectual property, and proprietary business algorithms. Private vs Public AI – Which is Better For Enterprises When choosing an AI solution for your enterprise, it’s crucial to decide whether a private or public AI system is the best fit for your needs. While both have their advantages, understanding the key differences can help you make a more informed decision. Private AI gives enterprises full control over their data, security, and AI models. It operates within the company’s secure infrastructure, ensuring that sensitive information stays protected. This level of control is especially important for businesses that handle proprietary data or must comply with strict data protection regulations. Public AI, on the other hand, is hosted on third-party cloud services and often involves shared infrastructure. It offers convenience, cost-effectiveness, and scalability, but can expose enterprises to security risks and potential data misuse. Public AI is generally easier to implement but may not offer the level of customization or security that large organizations require. Key Differences Between Private AI and Public AI Feature Private AI Public AI Data Security Full control over data, ensuring privacy and protection Data may be stored and processed off-site, exposing it to security risks Compliance Easier to comply with strict regulations (GDPR, CCPA, etc.) Compliance can be more difficult due to reliance on third-party services Customization Highly customizable to business needs Limited customization options Cost Higher upfront cost for infrastructure and maintenance Generally more affordable, with pay-as-you-go pricing models Scalability Scalable, but may require more resources to grow Easily scalable due to cloud-based infrastructure Implementation Time Longer implementation, requiring more setup Faster implementation with ready-to-use solutions In summary, private AI is often the better choice for enterprises that require complete control over their data, need to comply with regulations, or work with sensitive information. Benefits of Private AI Systems for Large Enterprises Private AI systems provide large enterprises with essential advantages, including enhanced security, full data control, tailored solutions, and scalability. These benefits help businesses protect sensitive information and drive operational efficiency. 1. Data Privacy and Security Private AI ensures that sensitive data, such as customer information or proprietary algorithms, stays within the organization’s controlled environment. By eliminating reliance on third-party cloud services, private AI reduces the risk of data leaks or breaches. 2. Compliance with Regulations With growing concerns about data privacy, adhering to regulations is crucial. Private AI allows enterprises to fully control how data is stored, processed, and used, ensuring compliance with strict data protection laws. 3. Competitive Advantage When data is securely housed within a private AI system, an enterprise can maintain a competitive edge by leveraging its unique data without risking exposure to competitors. Public AI solutions often use data from multiple sources, potentially giving competitors insights into an enterprise’s proprietary data. 4. Customization and Scalability Private AI solutions can be tailored to an enterprise’s specific needs. Whether it’s adjusting AI models for unique business goals or scaling AI systems as the business grows, private AI systems provide the flexibility needed for long-term success. By adopting private AI, enterprises can ensure their data is secure, their operations are compliant, and they can maintain their
Custom AI Chatbots VS Pre-Made Chatbots for Enterprise
As businesses grow and customer expectations rise, AI chatbots are becoming a must-have tool for improving efficiency and customer service. In fact, 80% of businesses plan to implement or expand their chatbot use by this year to boost engagement and ROI. But with so many options available, businesses face a dilemma: should they go with a pre-built chatbot or invest in a custom solution? While pre-built bots offer quick setup and cost efficiency, they often fall short when handling complex tasks and integrating with existing systems. The key is choosing a solution that scales with your business needs over time. We’ve helped several enterprises build custom AI chatbot solutions that streamline workflows, enhance customer service, and drive efficiency. Intellivon’s expertise in developing AI-powered chatbots ensures that businesses can automate complex tasks, provide personalized customer experiences, and seamlessly integrate with existing systems. Our solutions not only improve customer engagement but also scale with your business as it grows. That’s why we’ve created this blog, to guide you to choose the right chatbot and help build a custom AI chatbot tailored to your enterprise’s unique needs, ensuring long-term success and operational excellence. Key Market Takeaways of Enterprise AI Chatbots According to a Grand View Research report, the global chatbot market size was valued at USD 7.76 billion in 2024 and is projected to reach USD 27.29 billion by 2030, growing at a CAGR of 23.3% from 2025 to 2030. A key driver in this market is the surge of AI and machine learning trends. Key Market Takeaways: 88% of people had at least one conversation with a chatbot in the past year. 87% rate their chatbot interactions as neutral or positive. 62% would rather use a chatbot than wait for a human agent. 59% expect a chatbot reply within 5 seconds. 74% prefer chatbots for getting quick, simple answers. 80% of financial institutions see chatbots as a major opportunity. Healthcare: 68% of healthcare organizations now use AI chatbots. Global customer service cost savings: $11 billion annually by 2025 after using AI Chatbots. Average annual savings per company after using AI Chatbots: $300,000. Routine questions handled without humans: Up to 80%. By 2027, 25% of businesses will use chatbots as their main customer service tool. As enterprises continue to embrace digital transformation, chatbots have become an essential tool for managing customer interactions at scale. However, as the market expands, businesses must consider their unique needs to choose the right chatbot solution, whether pre-built or custom, to ensure long-term success and scalability. The Role of AI Chatbots in Enterprise Growth AI chatbots play a multifaceted and crucial role in the growth of large enterprises. They have evolved into strategic assets that help businesses drive efficiency, strengthen customer relationships, and gather valuable business insights. Here are at least five key roles AI chatbots play in the growth of enterprises: 1. Improving Customer Service and Engagement AI chatbots bring a fundamental shift in how enterprises handle customer service. According to a Zendesk report, 69% of customers prefer self-service over speaking with an agent, and chatbots can deliver just that by offering 24/7 availability. This ensures customers receive instant support and answers to common questions at any time, from anywhere. The immediate, round-the-clock access to assistance eliminates long wait times, offering a consistent, reliable point of contact. By handling high volumes of routine inquiries, chatbots free up human agents to focus on more complex and high-value customer issues, ultimately improving service quality and increasing overall customer satisfaction. This shift not only enhances customer experience but also boosts brand loyalty. 2. Boosting Sales and Lead Generation AI chatbots are a powerful tool for driving sales and enhancing lead generation. According to Statista, chatbots are expected to generate $142 billion in cost savings by 2024 across various industries, including sales and marketing. Chatbots can engage proactively with website visitors, answer product-related questions, and offer personalized recommendations based on browsing history. More importantly, they play a critical role in qualifying leads by asking targeted questions to assess potential customers’ needs before seamlessly passing them off to human sales representatives. This automation shortens the sales cycle, improves lead conversion rates, and ultimately contributes to higher revenue. 3. Enhancing Operational Efficiency For large enterprises, maintaining operational efficiency is crucial to staying ahead in a competitive market. AI chatbots can streamline many internal processes by providing employees with quick and easy access to information. For instance, in HR departments, chatbots can answer frequently asked questions about company policies, benefits, or leave requests, while in IT departments, they can assist with password resets or troubleshoot technical issues. By automating these tasks, chatbots reduce the workload on internal support teams, leading to higher productivity and faster decision-making. According to a PwC survey, 34% of businesses have already seen improved operational efficiency by integrating AI chatbots into their workflows. 4. Gathering Valuable Business Insights Every interaction an AI chatbot has is a potential source of data that can help shape business strategies. AI chatbots are designed to collect and analyze conversational data, offering enterprises valuable insights into customer behavior, pain points, feedback, and emerging trends. For example, Customer Support chatbots can track recurring issues, while Sales chatbots gather insights into what customers are asking or interested in. Analyzing sentiment and content helps businesses fine-tune products, marketing strategies, and operational processes. 5. Reducing Operational Costs The adoption of AI chatbots can significantly reduce operational costs for large enterprises. By automating routine tasks in customer service, HR, and IT support, businesses can cut down on the need for extensive human resources in these areas. This is especially important for providing 24/7 support, which would otherwise require expensive after-hours staffing. The cost savings from reduced labor needs, combined with improved productivity, lead to a strong return on investment (ROI). According to a Juniper Research study, chatbots are expected to reduce business costs by over $8 billion annually by 2022 through the automation of customer service tasks alone. These key roles show how AI chatbots are transforming enterprises by
Top Enterprise AI Trends in 2025
In the current context, AI has moved beyond automating mundane tasks to include influence over the way enterprises think, decide, and compete. The most advanced organizations are moving past surface-level automation into intelligent systems that learn, adapt, and act with precision. This evolution is shifting workflows from reactive problem-solving to proactive strategy-making, giving leaders the power to anticipate opportunities and risks before they appear. For enterprise decision-makers, that means fewer blind spots, faster pivots, and strategies that outpace the market. Those who embed AI into the very fabric of their operations today will define the competitive benchmarks of tomorrow. With AI adoption accelerating across data-driven industries, the urgency for quick, seamless, and scalable integration has never been higher. At Intellivon, we help enterprises design, build, and integrate AI solutions that align with their strategic goals from day one. In this blog, we’ll unpack the top Enterprise AI trends of 2025 and share how we can help you bring them to life in your workflows, effectively, securely, and at scale. Key Market Takeaways for AI in Enterprise Operations The enterprise AI market is expected to grow from USD 97.2 billion in 2025 to USD 229.3 billion by 2030, with an annual growth rate of 18.9%, according to a Mordor Intelligence report. The fuel behind this growth is attributed to the rapid adoption of generative AI, agentic systems that automate complex tasks, and increasing demand for specialized silicon to reduce inference times. By 2025, more than 78% of companies are expected to use AI in various areas like customer experience, operations, finance, and HR. AI is becoming a key feature in enterprise SaaS products, with over 60% of them expected to include AI capabilities by 2025. The growing need for automation, AI-driven solutions, and the ability to process large enterprise data are major factors fueling this growth. Specialized hardware for AI, such as GPUs and TPUs, is also seeing rapid growth, with demand for AI chips expected to grow at over 23% annually. Hybrid and edge AI models are outpacing traditional public cloud deployments, as businesses prioritize lower latency and data sovereignty. Healthcare is leading AI investment, with a projected annual growth rate of 22.17%. AI is predicted to significantly boost global economic growth, with some estimates suggesting it could add more than $15 trillion in revenue by 2030 across various sectors. Role of AI in Revolutionizing Enterprise Operations AI has already become a core operational engine for enterprises across industries. It is transforming processes with unmatched efficiency, agility, and innovation. Here are some ways in which AI transforms enterprise operations. 1. Streamlining Operations Through Automation AI-powered automation now handles both routine and complex workflows with minimal human input. From inventory management and compliance monitoring to customer service, AI systems process large datasets in real time, flagging issues instantly and making proactive adjustments. This reduces manual work, cuts errors, and speeds up delivery. For example, leading retailers use AI to predict demand, optimize supply chains, and reduce costs, all while ensuring faster, more accurate deliveries. The result is smoother operations that scale without adding overhead. 2. Driving Data-Driven Decision-Making Enterprises are harnessing AI analytics to turn vast amounts of structured and unstructured data into actionable insights. Predictive models powered by machine learning forecast market shifts, guide resource allocation, and refine product design. These insights allow leaders to make faster, smarter decisions, keeping strategies aligned with real-time market intelligence. This agility translates directly into competitive advantage. 3. Enhancing Customer Experience and Personalization AI is elevating customer service from reactive support to proactive engagement. AI chatbots and virtual assistants handle large volumes of queries instantly, providing tailored responses and freeing human agents to solve complex cases. By analyzing behavior patterns, AI tools can recommend products, anticipate customer needs, and personalize offers, deepening loyalty and boosting retention. 4. Empowering Innovation with Gen AI Generative AI is pushing enterprises beyond data analysis into content creation, code generation, and automated workflow design. Autonomous AI systems, combined with hybrid deployments that use both cloud and edge computing, ensure high speed, reliability, and security, which is critical for latency-sensitive operations like manufacturing or financial trading. This opens new opportunities for innovation without increasing operational risk. 5. Ensuring Responsible AI and Governance Enterprises are embedding governance frameworks to ensure fairness, transparency, and compliance with global regulations. Ethical AI practices not only reduce risk but also build trust with customers, investors, and partners. In a world where AI decisions shape business outcomes, governance is now a cornerstone of sustainable growth. Top 15 Enterprise AI Trends in 2025 The AI landscape in 2025 is richer, faster, and more deeply integrated into enterprise operations than ever before. These 15 trends are shaping how global organizations innovate, compete, and grow. Each represents both an opportunity and a call to action for leaders ready to embrace change. Trend What It Is Enterprise Benefit Agentic AI Systems Autonomous AI agents that execute complex workflows with minimal human input. Faster execution, reduced costs, and proactive operations. AI-Augmented Workforce & Copilots AI assistants that handle repetitive tasks and improve human-machine collaboration. Higher productivity, faster decisions, and empowered employees. AI-Driven Cybersecurity Real-time threat detection and adaptive defense using AI. Stronger security, reduced breach risk, and preserved trust. Multimodal AI for Unified Insights Integration of text, images, audio, video, and sensor data for richer insights. Faster problem-solving and better operational resilience. AI Governance & Ethical Frameworks Policies ensuring transparency, fairness, and compliance in AI. Reduced legal risk, improved trust, and stronger brand credibility. Industry-Specific & Domain-Focused AI Custom AI models designed for specific industries. Higher ROI through targeted, relevant solutions. Hyper-Personalization & Predictive AI Real-time personalization and forecasting of customer needs. Increased loyalty, retention, and revenue growth. Small Language Models (SLMs) & Edge AI AI models running locally or at network edges for faster, private processing. Improved privacy, compliance, and real-time decision-making. AI Reasoning, RAG & Advanced Problem-Solving AI capable of logical reasoning and enhanced knowledge retrieval. More accurate decisions in complex, high-stakes situations. Model Context Protocol