Category: AI Product Development

Do’s and Don’ts – Partnering with an AI Product Development Company

Artificial intelligence can give your business a competitive edge. Partnering with a reputed service provider will accelerate growth and success. Here, we’ll discuss what you should and should not do when partnering with an AI product development company to boost your business. Artificial intelligence combines tools, technologies, processes, and algorithms to offer diverse services to an organization. It can automate repetitive processes, increase productivity, streamline operations, optimize resources, improve quality, promote innovation, and enhance customer experience.  Statistics show that AI could add $25.6 trillion to the world’s economy. Another report shows that machines now perform 34% of business tasks. Moreover, 55% of businesses implemented generative AI solutions or worked on pilot projects in October 2023. The global AI market is projected to grow at a CAGR (compound annual growth rate) of 36.8% between 2023 and 2030 to reach $1811.8 billion.  Many startups, and small, medium, and large businesses choose to partner with a reliable AI product development company to implement artificial intelligence in their organizations. It is a time-saving and cost-effective to unlock business potential and gain a competitive edge.  In this blog, we’ll find out how AI can improve product development and what you should and should not do when partnering with an AI services company.  How do I Integrate AI into My Business? Hiring AI development services from reputed service providers is the best way to integrate artificial intelligence technology into your business. Partner with companies offering end-to-end solutions so that they will provide answers to questions like how to market my AI product, how to create apps, and how to future-proof the business.  Follow the below steps to make artificial intelligence a part of your internal processes and systems.  While the AI services company will handle the major share of work and responsibility, the top management of the business should be informed about the process and ensure the employees and stakeholders understand the need to implement AI. Make the transition as seamless and stress-free as possible so that your employees will quickly get used to new technologies and enjoy working with AI tools.  Do’s and Don’ts of Partnering with an AI Product Development Company  While choosing the right AI product development company is vital for your success, it is equally important to take appropriate steps to ensure the collaboration is beneficial in the long run. Check out the do’s and don’ts to consider when partnering with an AI service company.  What to Do?  Be Clear in Your Goals  There are some things you should do even before you contact the service providers. Firstly, understand and define your goals and objectives. What do you want to achieve by investing in AI? Which problems do you want to resolve? How do you wish to prioritize AI implementation in your organization? When you are clear about what you want, you can explain it to the service provider easily.  Research Service Providers Thoroughly Before you finalize a company for AI consulting services, make sure to research the market and providers. Check out various lists shared by experts, and go through their ratings on platforms like Clutch, Glassdoor, Google My Business, etc. Compare the services and price plans. Minimize the influence of stereotypes and make your decision based on the offerings and technical expertise of the company.   Openly Communicate Your Requirements  Good communication is vital for a successful collaboration. Openly talk to the AI product development company about your reasons for adopting new technology. Explain the challenges and hurdles you face, and ask how they intend to solve the problems. Once you sign the agreement and start the project, share your feedback with the service provider and inform them about any changes that occur.  Schedule Meetings (Online or Face-to-Face) While email communication provides proof for documentation, you will need to follow it up with direct or online meetings. After all, meetings through third-party video conferencing platforms can be recorded. This reduces the risk of miscommunication as text doesn’t always allow you to understand the tone.  Have Realistic Expectations  When it comes to artificial intelligence, it is easy to get influenced by various opinions and have unrealistic expectations. There’s no denying that AI is powerful and getting better. However, it cannot do everything you want. AI doesn’t replace human resources in your organization. It should be used as a tool to increase human efficiency but not become a burden or a substitute. Moreover, it takes time to see the results once you have integrated AI into your processes. Be patient.  Ask Questions  Don’t hesitate to ask questions when interacting with the AI product development company. The more you know, the easier it will be for your employees to adapt to the changes. Additionally, talk to other businesses and ask how they use AI in their processes. One way to learn is to ask and find out.  Emphasize Data Security  Data is the core of AI. When building artificial intelligence solutions, you should also make provisions to ensure the data is secure. Set up multilayer security systems to prevent unauthorized access. The AI product development company will help create a detailed data governance framework. And if you are using generative AI services, you should also comply with the data privacy regulations in your markets.  Discuss Pricing Upfront  Reputed service providers are transparent about their services and prices. They offer customized and flexible plans for businesses to pay for what they choose. Still, you should make it a point to inquire about the prices and ensure there are no hidden or extra charges. This also helps you determine the budget. Do note that choosing the least expensive provider may not be a good idea.  Crosscheck Client Testimonials  When partnering with a company for AI development services, you should check what other clients say about the service provider. Instead of simply reading their testimonials online, cross-check them with the businesses and ask more questions. That way, you know the company you partner with can be trusted with your project.  What Not to Do? Don’t

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AI as a Service (AIaaS) – Future of Artificial Intelligence Integration

AI as a service allows startups, SMBs, and large businesses to adopt advanced technological capabilities for cost-effective pricing. Here, we’ll discuss the role of AIaaS in diverse industries and the importance of integrating artificial intelligence with business processes. Artificial intelligence development is being widely adopted and implemented in various industries. Building AI models from scratch is expensive and time-consuming. That’s why many businesses opt for artificial intelligence or AI as a service (AIaaS) partnerships with reputed third-party service providers. It helps organizations customize existing solutions to suit their needs. The AI apps are easily scalable and suitable for small, medium, and large businesses.  Statistics show that the global AI market was $196.63 billion in 2023 and is expected to grow at a CAGR (compound annual growth rate) of 36.8% to touch $1,811.8 billion by 2030. Leading tech giants like Google, Amazon, Microsoft, IBM, etc., are making heavy investments in AI. These companies also offer cloud platforms and advanced applications for businesses to build robust ecosystems and strengthen their processes.  Let’s find out more about AI as a service and how an AI product development company can help businesses seamlessly integrate artificial intelligence into their internal processes.  Types of AIaaS  AI as a service allows businesses to reduce the risk of investing in new technology. Organizations can start small and scale as it suits their budgets. Additionally, they can experiment and try different applications, tools, cloud platforms, etc., to find the right combination. For example, a third-party AIaaS provider which is a certified partner of Azure, Google, and AWS can help a business choose the best cloud solution for their needs.  Moreover, the latest AI technology requires supportive hardware like more powerful GPUs, FPGAs (field-programmable gateway arrays), APIs, etc. These elements are taken care of by the AIaaS provider so that apps run on remote cloud platforms and businesses can save their limited resources for core operations.  The following are the major types of AI as a service offered by AI product development companies.  Digital Assistants and Bots  Chatbots and digital assistants are the most common type of AIaaS offered by service providers. The bots are built using AI, ML, and NLP technologies to understand human input and deliver personalized output. They are used in customer service departments to reduce pressure on the executives and provide 24*7*365 support to customers. Similarly, digital assistants are used to set up self-servicing solutions for employees so that they can quickly access the information they need or troubleshoot a device when necessary.  Machine Learning Frameworks  Developers use ML frameworks to build AI models for different purposes. The frameworks provide the basic foundation and can be integrated with third-party apps. However, the process of building an ML data pipeline is complex and requires domain expertise. Businesses can opt for AIaaS as a part of AI/ML development services to access ML models and frameworks useful for their processes. The models are deployed on the service provider’s cloud servers and save computing resources for the enterprise.  APIs API is an application programming interface, a solution that connects two or more software/ apps/ tools/ etc., to increase their functionality. Generally, businesses use AIaaS APIs for NLP (natural language processing) capabilities which help in sentiment analysis, knowledge mapping, translation, data extraction, etc. Similarly, computer vision helps extract elements from images and videos to help build applications for facial recognition, in-video search, ID verification, etc. APIs allow different software apps to continuously share information and deliver the final output to the end user.  AIoT Artificial Intelligence of Things (AIoT) is a network of interconnected devices that extract, collect, and share information in real-time. It is an advanced version of IoT (Internet of Things) and has the capabilities of AI and ML technologies to analyze the collected data and identify patterns, trends, correlations, etc. The devices also help in detecting and fixing problems in the business processes to ensure seamless operations. AIoT devices send the information to cloud platforms where other applications are hosted. They help businesses make factories, mines, labs, etc., safer for employees and increase the lifespan of machinery. Service providers offer end-to-end AI development services to build, integrate, and monitor AIoT devices.  No-Code and Low-Code Apps  There is a high demand for no-code and low-code applications in the global market. That’s because these are pre-built models with existing features and can be personalized for different businesses. With custom templates and drag-and-drop editors, almost anyone with basic tech knowledge can use the applications. Additionally, these AI-based are hosted on the cloud servers of service providers and can be used on multiple devices simultaneously. Businesses that don’t want to invest in proprietary software opt for AI as a service to take advantage of no-code and low-code apps for streamlining their internal operations.  Generative AI  Generative AI has become a rage in recent times. GenAI applications are built on LLMs (large language models) to cater to diverse use cases like content generation, summarization, proofreading, coding, debugging, brainstorming, etc. By availing of third-party generative AI services, businesses can use prebuilt models and train them with proprietary data to get accurate and actionable insights. Since generative AI uses more computational power, the apps are hosted on the service provider’s cloud servers. Reasons to Invest in AI as a Service  Greater Scalability  Businesses grow and expand as they establish themselves in the markets and attract new customers. That means it should continuously upgrade its systems and tools to keep up with increasing transaction volume. AI consulting services for AIaaS offer the much-needed scalability for businesses to seamlessly upgrade or downgrade their plans or strategies to suit their requirements.  Higher Efficiency  AI as a service lets employees use advanced tools for automation, analytics, reporting, etc. This streamlines the workflow and accelerates the projects. Employees can finish more work in less time without compromising quality. It also allows the business to complete more tasks with the same number of employees. No need to hire additional candidates.  Cost-Effectiveness Adopting new technology like AI is expensive for a

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5 Top GenAI Consulting Firms to Watch in 2024 

Generative AI is the latest development in the industry, opening new doors for businesses to grow and become successful. Here, we’ll discuss the top five GenAI consulting firms to hire in 2024 and the role of a service provider in successful AI adoption.  GenerativeAI has become a buzzword since the launch of ChatGPT in November 2022. Businesses are exploring the potential advantages of using Generative AI to streamline processes and enhance productivity.  According to a report by McKinsey, GenAI (Generative AI) can potentially contribute $2.6 to $4.4 trillion to the global economy every year by 2030. Generative AI can help a business in many ways, such as extracting information from audiovisual content, summarizing long pages of text, writing and debugging code, rewriting and editing content, providing multiple product or service descriptions, draft letters, emails, ads, and so much more.  Let’s find out more about the role of GenAI Consulting Firms in helping enterprises adopt the new technology. We’ll look at the top service providers offering GenAI consulting and LLM (large language model) development services in the industry.  Why is GenAI Important? Generative AI is a type of artificial intelligence that can create new content, like text, images, video, audio, synthetic data, etc., based on a given input command or query. DALL-E, ChatGPT, etc., are some well-known GenAI platforms used by individuals and businesses. These are built on large language models (LLMs) and trained on massive amounts of data.  Generative AI is important as it can disrupt the global industry and accelerate technological adoption and digital transformation. LLM consulting companies use open-source models to build customized applications for businesses and train them on proprietary data before deploying them in the client’s IT infrastructure. Almost every industry and department can benefit from generative AI.  How Will GenAI Impact Consulting? Generative AI adoption is one of the latest AI-powered innovation strategies that can impact a business in several ways. In the field of consulting, it can be useful in the following ways:  5 Top GenAI Consulting Firms to Watch in 2024 DataToBiz DataToBiz is among the top AI consulting firms offering tailor-made and end-to-end solutions for complete digital transformation, AI adoption, data engineering, business intelligence, and LLM development. The company provides LLM consulting for businesses from diverse industries and builds data-safe models on-premises and cloud platforms. It helped clients reduce the time taken to resolve concerns by up to 20% and optimized resource utilization by up to 18%. DataToBiz has experienced AI engineers to build, deploy, integrate, and upgrade AI chatbot solutions, automation tools, virtual assistants, etc. The company provides 24*7 customer support with its 360-degree LLM implementation. It is a certified partner of Microsoft, Google Cloud, and AWS.  LeewayHertz LeewayHertz is one of the best AI development companies in the market, offering innovative product development and consulting services to businesses from several industries. The company develops domain-specific LLMs that can be seamlessly integrated with the existing business systems in an organization. The experts work with different foundational models to choose the most appropriate solution based on the client’s requirements. LeewayHertz also offers strategic consulting services for GenAI adoption. It provides LLM optimization and maintenance services for top-tier Fortune 500 companies. Businesses can choose a hiring model that suits their project specifications.  Strong Analytics Strong Analytics is a GenAI model development and data analytics service provider offering custom machine learning software and solutions to diverse clients. The company empowers businesses with cutting-edge AI technologies that have been customized for each business’s specific requirements. It focuses on building high-performance models that can help businesses navigate the complexities in their industries and achieve the desired results. Strong Analytics also provides data augmentation and synthesis, content generation and automation solutions, computer vision and image synthesis applications, and consulting services to adopt and implement Generative AI in a business.  Deeper Insights Deeper Insights is a data intelligence company offering GPT-3 for product development and Generative AI services to different businesses from around the globe. The company builds custom LLMs and trains them on the proprietary data of the clients to provide accurate and meaningful output. It helps establishments unlock the full potential of their business data and thrive in competitive markets. Deeper Insights aims to reduce costs, optimize resources, increase efficiency, and enhance decision-making capabilities in businesses. It provides end-to-end support, be it strategy formation, large language model customization, deployment, fine-tuning, and long-term maintenance. The company is an AI partner of many leading global businesses.  ThirdEyeData ThirdEyeData is an AI and large language model consulting company with ISO certification. It also has Microsoft Silver partner certification and SOC 2 Type 1 data security certification. The company builds real-world AI applications to promote data-centric business models. It develops ChatGPT and Generative AI solutions for automation, predictive modeling, recommendation, decision support, analytics, conversions, etc. The company blends machine learning, computer vision, NLP (natural language processing), and GenAI to provide tailor-made solutions for businesses to overcome various challenges and achieve their goals. ThirdEyeData offers consulting, application development, bespoke LLM training and optimization, data engineering, and other custom solutions. It has won several awards and is a reputed service provider.  What are the Challenges of Gen AI? While building AI products with LLMs is beneficial for businesses, the process is not without a few challenges. These concerns can be overcome by partnering with the reputed GenAI consulting firms we listed above.  How New Opportunities can be Created Using GenAI? AI product development experts have been working on a varied range of projects to find innovative ways to use Generative AI solutions in businesses. It can create new opportunities for enterprises to gain a competitive edge and increase ROI. A few such examples are as follows:  How to Hire a Generative AI Consulting Firm? Consider the following factors when hiring a GenAI consulting firm to work on your project.  Businesses can get AI product development cost estimation reports from shortlisted service providers and compare the offerings before making the final decision. However, note that cost is not the only factor. The features and services included will play a vital role in the final output.  Conclusion  Generative AI can support a business in many ways. However, the technology and its ecosystem are still evolving. Enterprises will need to plan for the future when hiring a service provider. Opt for Generative AI consulting services that make the business flexible, agile, scalable, and future-proof. With direct access to

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How to Hire the Right LLM Consultant? CEO’s Guide to Exploring LLM Integration

Large language models are part of generative AI applications and can be customized for diverse business needs. Here, we will discuss tips for hiring the right LLM consultant for a business and the factors every CEO must know about generative AI.  Businesses today should adopt the latest technology to survive market competition. This includes generative AI and large language models (LLMs), which can transform businesses in many ways.  According to a survey report by McKinsey, around 65% of companies that responded are using generative AI in their businesses. The figure has doubled in less than a year, indicating the growing popularity of GenAI and LLMs in the market. Additionally, the report shows that large language models are being used for more business functions than in the previous survey. 50% of responders use generative AI for two or more functions.  Most organizations rely on third-party or offshore service providers and GenAI consulting services to bridge the talent and technological gap in their business. Your success depends on hiring the right LLM consultant to work with your establishment. The right service provider understands your requirements and aligns your business mission, vision, goals, and objectives with the LLM strategic implementation plan.  Here, let’s discuss how to hire LLM consultant for your business and learn more about generative AI. We’ll answer questions like what is LLM’s full form in AI, what LLM is in generative AI, and so on.  Read on!  What Does LLM Stand for in ChatGPT? LLM stands for Large Language Model. It is a deep learning model that can read and train on large datasets and perform language processing tasks. The models are trained to create outputs that combine different types of text and can mimic human language.  LLMs are a subset of artificial intelligence like machine learning, deep learning, and natural language processing. These can be used for quicker and more effective AI Product Development in different industries. What is LLM in Generative AI? Generative AI applications like ChatGPT are built on LLMs and foundation models (complex machine learning models) to understand the input data and provide a relevant output in the user’s preferred format. Large language models handle the text-generation part of generative AI. That means all LLMs belong to the generative AI models but gen AI doesn’t have to use LLMs.  For example, ChatGPT gives textual output while platforms like Microsoft Bing use text to generate images as output. LLMs are used in ChatGPT while other foundation models are used in Bing to convert text input to image output. So, which LLM to choose? Businesses that want to adopt GenAI and large language models often face the tough question. Which LLM is right for their operations? With so many models already available in the market, it can be confusing to pick the right one.  There is no definite answer to this question. When you hire LLM consultants, they will analyze your business needs and identify the best model to help you achieve your goals. Service providers consider factors like the size of the model, availability, architecture type, training process, and benchmarked performance. LLMs are broadly classified into three categories – encoder-only, decoder-only, and encoder-decoder. BART is an encoder-decoder model, while GPT is decoder-only and BERT is encoder-only. The right LLM consultant will choose the perfect large language model for your business and set up the necessary integrations. They will customize the model and train it on your proprietary data to increase its efficiency and accuracy. How to Choose an LLM Consultant for Your Company Since large language model development and integration is a part of AI services, you need to hire a reputed AI/ ML company for the project. However, not every artificial intelligence company works with LLMs. Partner with LLM consulting companies or generative AI companies based on the factors below.  Business Goals and Objectives  While most tips for hiring an LLM consulting provider focus on the capabilities of the service provider, it is equally important to consider your requirements and goals. Do you already use AI applications? Did you begin your digital transformation journey? What do you aim to achieve in the next five or ten years? If you are yet to adopt new technologies, you will need an end-to-end AI and LLM consulting company to guide you through the entire process.  Existing Talent in Your Business  The second aspect to consider is the talent on your payroll. Do you have AI engineers and developers in-house? Can your existing employees be trained to use the LLMs? The lack of required talent in your establishment implies the need to make alternative arrangements. You will find it easier to outsource the project to expert AI product development companies and LLM consultants. You also have to create training modules to bridge the gap within your enterprise or opt for staff augmentation to bring fresh talent capable of using new technologies.  Technical Expertise of the Consultants Large language model consulting companies should have the necessary technical and domain expertise to work with complex deep learning and foundation models required to build generative AI applications. They also need to have expertise in data engineering and management. The LLMs can be efficient and accurate only when they are trained on high-quality data. The consultants should clean and process the datasets before training the models on them. Additionally, they should integrate the LLM with your existing systems to share the outputs through personalized dashboards.  Strategic Approach  Artificial intelligence and large language models don’t offer standard solutions. There’s no one-size-fits-all theory in LLM integration best practices. The approach is tailored for each business based on your budget, priorities, existing systems, long-term plan, and other factors. The right LLM consultant will know how and what to include in your LLM journey.  Use Cases or Success Stories  Use cases and project portfolios tell the success stories of the LLM consultant. When hiring GenAI consulting services, make sure to ask for more information about relevant projects the experts worked on. Many companies include these details on their

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How Large Language Models Boosts AI Product Development?

LLMs are a subset of artificial intelligence and have versatile roles in today’s world. They help in developing next-gen products and enhancing user experience. Here, we’ll discuss the role of Large Language Models in accelerating AI product development.  LLMs, or large language models, are a part of artificial intelligence and act as the foundation models to build applications that can understand human languages and respond on the same note. LLMs are the core of GenerativeAI, which has become popular since the launch of ChatGPT. In fact, we can say that LLMs are a subset of GenerativeAI.  Statistics show that the global LLM market is expected to grow at a CAGR (compound annual growth rate) of 79.80% between 2023 and 2023 to touch $2598 million by 2030. Another report estimates that around 750 million apps will be using LLM by 2025.  Businesses can use LLMs for various purposes, including software development. In fact, AI Product Development Companies are increasingly using large language models to accelerate the process and release quality products into the market in a quick time.  Let’s find out more in this blog. Role of Large Language Models in Accelerating AI Product Development Now that you know the definition of large language models, let us understand their role in speeding up AI product development.  Ideation and Brainstorming  The application of LLM in AI product development starts right at the first step – ideation. The development team can use GenerativeAI apps when brainstorming ideas for the product and its features. It helps make a list of potential products you can release into the market based on historical and current trends, customer preferences, etc. Startups can hugely benefit from this as they may not have the same access to other resources as large enterprises.  Conceptualization Once the product development team finalizes the ideas, it moves on to the next stage. Large language models are helpful in this stage by providing inputs to include different product features, extensions, requirements, etc. Since LLMs process data and interpret insights, you can identify risks in the early phases and find ways to overcome the challenges. Using large language models for AI product development also helps in creating innovative and unique products.  Research and Analysis  Another reason to invest in large language model development is its support for research and analysis. You can analyze the market conditions and customer tastes in different regions at different periods. LLMs simplify market research by giving immediate results. Instead of conducting lengthy surveys, you can rely on the GenerativeAI models to access the reports. For example, streaming platforms analyze user behavior to add new features and pricing models.  Prototyping The prototyping phase is crucial in AI product development. Large language models can help by analyzing the input design (code) and predicting the possible flaws the final product will have. This allows the development team to make corrections, even before the product is built. It saves time and resources spent on making multiple prototypes. For example, Tesla uses artificial intelligence and LLMs in the design process to build technologically advanced cars.  Marketing Large language models also have a role in streamlining the marketing efforts to take the product to the right target audience through the right channels. Using LLMs for analyzing your marketing strategies and predicting user engagement patterns will increase your chances of success. The sales team can collaborate with the development team to create promotional campaigns that will reach a wider range of audience.  Customer Engagement  Similarly to the previous point, LLMs are useful for increasing customer engagement with the product and the business. They help in developing personalized marketing strategies for each group of target audiences and every potential lead. This will eventually lead to a higher customer retention rate and a loyal customer base.   Post-Launch Maintenance  There’s an increasing demand for LLM Consulting since the models provide end-to-end support for AI product development and maintenance. Once you release the product into the market, you should monitor its performance and make regular upgrades to get consistent results. Use customer feedback and engagement to take corrective steps and refine the product quickly. LLMs can be used to develop an agile post-launch maintenance strategy for different products.  Future of LLMs in AI Development LLM and GenAI model development are still in the early stages. There is a lot to explore in the future. As LLMs become more capable and efficient, they will have a greater role in AI development. We are likely to see large language models being used for the following:  The field of artificial intelligence is dynamic and constantly evolving. This makes LLMs a means for businesses to adapt to the changes and stay up-to-date in the competitive markets. Conclusion Large language models are a subset of artificial intelligence development services. They can enhance your business processes in many ways and increase productivity. Businesses from different industries can benefit from investing in LLMs for product development, customer service, etc.  Partner with AI development companies for tailored GenAI consulting to streamline your enterprise and adopt advanced technology to achieve your goals. Gain a competitive edge and stay ahead of other businesses in the industry with our support.  FAQs 1. What are LLMs in artificial intelligence? LLMs are foundation models trained on vast amounts of data so that they can provide the necessary support for a diverse range of applications and use cases. Instead of building domain-specific models, LLMs are multipurpose and easily adaptable to different requirements.  2. How is LLM used in product development? A large language model is used at various stages of product development, starting from ideation and conceptualization to analysis, prototyping, error detection, testing, fine-tuning, and upgrading. It helps streamline the entire process to save time and enhance user experience.  3. How do I create an LLM product? LLM product development starts with basic planning and strategy formation which is a part of AI strategy consulting. Make a clear list of the project requirements to choose the appropriate model. Then, define the user flow

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5 Top LLM Service Providers Every CEO Needs On Their Speed Dial

LLMs are disrupting the global market by revolutionizing businesses from various industries. They transform how we interact with technology. Here, we’ll discuss the top five LLM service providers to partner with to deploy large language models in your enterprise. The adoption of AI product development has increased despite the market and economic uncertainties in recent times. Statistics show that 69% of businesses say AI and ML adoption is a high priority. 54% of AI adopters reported better cost savings and greater efficiency. Be it startups or large enterprises, many businesses from various industries continue to invest in artificial intelligence.  Generative AI and large language models are gaining popularity, thanks to the success of ChatGPT, Microsoft Bing, and Google Bard or Gemini today. Business organizations are looking at LLM development and partnering with LLM service providers as a means to gain a competitive edge, form business growth strategies and establish brand value. According to a survey conducted by AI Infrastructure Alliance, 67.2% of the responders said adopting LLMs and generative AI was their top priority.  Of course, the same report shows the various challenges the CEOs have to overcome to successfully implement LLMs and achieve the desired cost-effectiveness. It also highlighted that 20% saw losses of 50 to 100 million dollars due to incorrect deployment of AI tools. The responders admitted to not having the necessary understanding or infrastructure to handle artificial intelligence. This is a clear indication that CEOs need to find reliable and competent LLM consulting companies for AI adoption. The talent gap in businesses is real and can be bridged by partnering with artificial intelligence service providers. So, what companies are developing LLM?  Let’s find out in this blog.  What is the LLM Network? A large language model (LLM) is a powerful AI program that can perform versatile tasks like text and multimedia generation, coding, debugging, chatting, etc. It is a type of neural network called the transformer model and contains machine learning algorithms. The algorithms are trained to recognize and interpret human language to generate a suitable response. This is done through deep learning, which allows the algorithm to understand the patterns in the data to deliver the required output without human intervention. LLM service providers help you further fine-tune these models by training them with enterprise data to customize the tasks and responses for the business. This allows organizations to implement LLMs for a variety of tasks to streamline, automate, and speed up day-to-day activities and increase overall performance.  5 Top LLM Service Providers It’s important to understand that LLM development is a part of artificial intelligence product development and consulting services. However, all companies that offer AI consulting services may not provide LLM solutions. Here’s a list of the top companies that build, deploy, train, implement, and customize large language models for different industries.  DataToBiz DataToBiz is among the top LLM platforms for entrepreneurs offering end-to-end development and consulting services. The company provides personalized large language models fine-tuned to align with client’s business requirements, industry, and long-term goals. It also ensures data protection by hosting the databases on-premises and secure cloud platforms. The company helps several businesses optimize resources by up to 18% and saves the time required to address concerns by 20%.  DataToBiz is a comprehensive AI product development company for startups, SMBs, MSMEs, and large enterprises from healthcare, finance, eCommerce, retail, marketing, research, academia, and many other niches. The company takes care of data analytics, big data management, data engineering and governance, LLM model development, training, customization, optimization, and maintenance. Many businesses have long-term partnerships with this award-winning company to maintain and upgrade their AI tools for efficient results. Boost productivity, increase ROI, and enhance customer experience with DataToBiz. Anthropic Anthropic is an AI safety and research company co-founded by former executives of OpenAI. It provides foundation LLMs and APIs for enterprises to develop natural language processing (NLP) applications. The company emphasizes developing reliable, steerable, and interpretable AI systems suitable for research and science. It collaborates with establishments from academia, nonprofits, government, industry, etc., to promote the safe adoption of LLMs.  Anthropic has developed Claude, a next-gen AI assistant that can be scaled to suit business requirements. The tool doesn’t have to be trained on business data to deliver accurate results. However, it can be personalized to meet the specific requirements of each client and reduce brand risk. The company has also developed a cheaper and faster version, Claude Instant. The AI product works best for customer service, coaching, legal, back office, search, and sales. The Claude Beta version is also available.  Databricks  Databricks helps accelerate AI product development with LLMs and has its roots in academia. The company offers massive-scale data engineering services for leading brands from around the globe. It partners with businesses to democratize AI and find effective solutions to their problems. It also provides complete assistance to implement generative AI and LLMs by following a data-centric approach. The company pre-trains the models to integrate them with client’s systems and databases to streamline workflow.  Databricks also customizes the large language models for specific tasks using open-source tools like Hugging Face and DeepSpeed. Dolly 2.0 is an LLM trained by the company and a fine example of how to get it done in a quick time, and that too for inexpensive pricing. Furthermore, the company provides built-in LLMOps, which are MLOps for large language models. These are unified on the exclusive Data Lake platform of the company to cater to the client’s requirements.  Cohere Cohere is an LLM consulting company offering cutting-edge research and development tools in the AI industry. It has an experienced team of AI/ ML engineers who work together to promote advancements in artificial intelligence and share them with the world at large. The company’s Retrieval Augmented Generation (RAG) toolkit helps LLMs provide accurate answers to input questions and complete the process by relying on enterprise data. Coral is an application developed by the company to build powerful chatbots to interact with customers and end-users.  Cohere also has tools for advanced and accurate semantic search. It can work with English and a

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10 Proven AI-ML Strategies for Early-Stage Tech Startups 

Artificial intelligence is a powerful technology offering versatile solutions to businesses from different industries. Here, we’ll discuss the best AI-ML strategies for early-stage tech startups and the importance of hiring machine learning development services. Startups are popular these days, with more people willing to take the risk and become entrepreneurs. However, not many startups survive to see the light. After all, the markets are highly competitive, and a startup just doesn’t have the kind of support and brand image an established business has. Does it mean you should not take the risk?  Of course not! It only means you should do better research and get the right guidance to overcome the challenges and strengthen your startup’s position in the market. Statistics show that 26% of people who start their own business say it is because they are ready to be their boss instead of working for another. In such instances, it is even more important to make the right decisions and investments when establishing a startup.  So, what should be your primary focus in today’s world? The answer is artificial intelligence. With the global AI market estimated to reach $407 billion by 2027, you cannot afford to ignore the benefits of using artificial intelligence in your startup. Nevertheless, choosing a random AI tool will lead to adverse results. As an upcoming entrepreneur, you should leverage robust and comprehensive AI or ML strategies for your early-stage startup, especially if it belongs to the IT industry. You will need to work with a reliable Artificial Intelligence Development Company to understand the intricacies of using AI/ ML in your business.  But… you have more questions, don’t you? Worry not. We will answer the major ones in this post. Shall we get started?  What is AI/ML Strategy?  An AI/ML strategy is a blueprint that guides you in choosing and implementing various AI applications in the business. It aligns the business strategy with the company’s mission, vision, and goals. The AI strategy has to be in sync with the overarching business strategy so that you have the right tools for the right job. Before you consider AI implementation for startups, you need to have a viable core business strategy. This allows the AI strategy to be the necessary fuel that powers your business. It also helps in bringing the various business processes together to create a cohesive and streamlined system that runs smoothly. Startup AI consulting companies focus on these aspects when offering strategies to implement AI and ML applications in your business. They use their expertise to understand your requirements and find the best solution.  How AI is Used in Startups? Proven Tips for AI-ML Strategies for Early-Stage Tech Startups Innovative Product Design and Development AI-driven software development promotes innovation, creativity, and greater customer satisfaction. That’s because artificial intelligence is used to analyze customer preferences and market trends to derive insights, which help in designing the final product. AI also accelerates product development through automation and reduces the time-to-market. You can quickly launch your startup before competitors take advantage of the opportunities.  Artificial Intelligence for Customer Service AI chatbots and virtual assistants with natural language processing (NLP) for startups can streamline customer service from the initial days to ensure the end users are happy with your business. AI can handle general queries, share user guides, generate tickets, and track the chat even when a human takes over. Additionally, a chatbot doesn’t get tired and can interact with several customers at once.  Predictive Analytics for Decision-Making Predictive analytics is a part of advanced analytics that uses statistics and ML algorithms to determine the likelihood of a future outcome. Custom machine learning application development services allow you to use predictive analytics for various purposes, such as sales forecasting, machinery maintenance, predicting customer behavior, identifying new markets, etc. It helps make data-based decisions to effectively manage your startup.  Streamline Sales and Marketing Startups need to constantly work on their marketing campaigns to gain visibility and attract target audiences. Cloud-based AI/ML solutions for startups help identify prospective customers and make it easy to create category-specific ads and promotions. You can create highly personalized campaigns for each section of your audience to increase sales. AI tools also help with sales forecasting.  Machine Learning for Fraud Detection Online and offline frauds are a big concern for every business. As a startup, you cannot afford to have fake and fraudulent transactions in high volume. Luckily, artificial intelligence and deep learning for startups can analyze your business/ transactional data to detect signs of credit card fraud, money laundering, identity theft, etc. You can protect your startup from severe financial losses caused by such activities and avoid legal complications.  Operations Management and Automation AI-driven product enhancement is highly effective in streamlining operations, reducing costs, shortening production cycles, improving quality, optimizing the supply chain, and managing inventory. By automating repetitive tasks, your employees can focus their energies on the core business processes and come up with innovative ideas to expand the offerings. Artificial intelligence can be implemented at all stages and levels of a business.  AI-Powered Cybersecurity You cannot compromise on cybersecurity in today’s scenario. Hackers will target any business or individual. Statistics estimate that the global cost of online crimes will reach $23.84 trillion by 2027. According to the ISACA’s 2023 State of Cybersecurity report, 48% of businesses reported an increase in cyber threats in a year.  AI-powered digital transformation for startups includes robust data governance and compliance protocols. You will get detailed documentation about how to set up a multi-layered security system to protect your startup from cybercriminals. Moreover, AI-based antivirus software is effective in detecting cyberattacks.  Smart Resource Allocation Can we compare the resources of a startup to an established brand? No, right? Startups usually have to work with limited resources and set strict budget limitations. How well you use the existing resources can determine how soon you break even and start earning profits. Emerging technology solutions based on AI help you prioritize your processes to allocate resources accordingly. That way, you won’t spend excess money on unimportant tasks.  Personalization for Enhanced User Experience Businesses are turning from a product-centric to a customer-centric approach

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Behind the Scenes: A Look into Our AI Product Development Process

Discover how AI provides a seamless roadmap to overcomes the challenges in AI product development process, paving the way for innovation and exceptional possibilities? Well, let’s talk about that today!! The usage of AI in the product development process catalyzes innovation and enhances operational efficiency. It makes it easy to design, develop, and customize products to fulfill the customer’s needs. From transforming product development methodologies and outputs through personalized product recommendations and quality control, the scope of AI in product development is vast. In this blog, we’ll uncover the secrets behind our successful AI product development process. Let’s jump right in. Sneak-peak in the tools used for AI product development: AI Development: Tensor Flow, Amazon Sage Maker, Tableau, Keras, Microsoft Cognitive Toolkit, Cloud AutoML Vision, Apache MXNet Deployment: Azure, AWS, Google Cloud Platform, Nvidia Data Storage: Amazon Redshift, Amazon Athena, SAP, Azure Synapse Analytics, Azure Data Lake Storage, MongoDB, Microsoft SQL Server, Google Big Query, MySQL, Hadoop, Azure Integration Services Data Processing: Microsoft SQL Server, Talend, Azure Data Factory, Python, Spark, Kafka, Airflow, AWS Glue, and Databricks. Steps in AI Product Development Our AI product development team follows a six-step plan to create AI products. Our experts ensure a smooth transition as the output of one pipeline depends on the output of the other. Now, I’ll explain the steps in detail: Kicking-off the Project: The first phase of the AI product development roadmap involves defining the project scope and specifying what our experts will work on. Further, they choose the tools and technology stack and delve deep into user stories and use cases to find out how users will interact with the product. Next, the AI development experts create the system architecture blueprint along with the UI/UX design. Minimum Viable Product (MVP): Minimum viable product refers to a product version with sufficient features to attract early adopters and validate the product idea in the early stages. The AI developers prioritize the most important features and create a basic version of the product. Next, they build and improve the product in small steps known as iterative development, and ask the audience to use the product and share their feedback. Data Acquisition and Preparation: This third stage of the AI product development process includes gathering data and planning how this data will be used to build the product. The developers verify the accuracy and reliability of the data by cleaning and organizing it. Further, they add tags (if necessary) to categorize information and protect it from unauthorized access. Model Development and Training: Choose the right model and build the AI product that fulfills the specified needs. Transfer learning considerations, train the AI product, and test it to assess whether it meets the purpose it is designed for. Integration and Development: Integration and development is the fourth stage of AI product development. It includes API development, mobile apps, and web apps with a scalable architecture. It is important to ensure that the AI product works smoothly on various operating systems and devices. Quality assurance is also essential to verify that the product works efficiently and is free of bugs. Deployment and Maintenance: Deployment and maintenance are the best practices for AI product development to launch your AI product into the market. Once the product is launched, find out how the AI product is performing and take note of how you can make your product better. After you have made changes, provide the new versions of the AI product and maintain it to ensure smooth functioning. Benefits of AI Product Development Process Rich Computation AI enables the development teams to analyze huge amounts of data, detect patterns, and make predictions with machine learning techniques and algorithms. This makes it easy to build efficient AI products. Demographic Analysis With AI, developers can analyze data quickly and find new ideas. For example, you can use AI to understand customer preferences and recommend new features for a smartphone that people may like. Psychometric Analysis AI makes it easy to track and analyze data from different sources such as social media, search history, and physiological metrics. With a focus on ethical considerations, AI can be used to analyze data patterns and predict the probability of adoption of new products. Less Time AI significantly reduces the time needed for processes such as prototyping, testing, and development. The developers can create quick simulations, test them, and make improvements if needed. Informed Decision-making AI can quickly analyze large datasets and create multiple probabilities, thereby facilitating businesses to explore all aspects during decision-making. High Security: AI-powered authentication systems such as biometric scanning and CAPTCHA track security breaches, virus attacks, and malware. Also, the developers can use advanced tools to carry out investigative processes to trace vulnerabilities and prevent risks. Conclusion: Deploying AI into product development is the key strategy to optimize development processes and enhance productivity. AI product development companies help you to innovate, design, and launch AI products that remain competitive in the market. It is mandatory to abide by ethical considerations to ensure the sustainable growth of AI and implement effective strategies to redefine the future of AI product development. Originally Published on Medium

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How AI Can Help You Build Your Dream App!

We see artificial intelligence everywhere! It has a key role in app development too, be it speeding up the process or enhancing user experience. AI can indeed help you build your dream app or other AI/ML-based products and release it into the market. Let’s discuss how AI app development can be done. Artificial intelligence is no longer a distant technology. It is a part of our everyday lives and used by many people in one way or another. The advent of generative AI has led to major developments where almost anyone can create art, music, and apps without making heavy investments. While the results and ethical issues are debatable, we cannot ignore how AI has been integrated into our lives. According to Grand View Research, the global artificial intelligence market is estimated to grow by a CAGR (compound annual growth rate) of 37.3% and reach $1,811.8 billion by 2030. ChatGPT is the fastest-growing AI application, with over 180.5 million global users, of which 100 million are active weekly. This includes 92% of the Forbes 500 companies. Many individuals, entrepreneurs, and business organizations want to build an app with AI to suit their specific requirements. While this was expensive a few years ago, AI app development is now a cost-effective solution for building and deploying a customized application. In this blog, we’ll understand the role of artificial intelligence in building apps and see how you can build your dream app using AI. Role of AI in Apps Development AI app development is versatile and user-friendly. It helps streamline the ideation to implementation journey by reducing the risk of errors and speeding up lengthy processes. From automating code generation to debugging and reasoning, AI can be used in many ways. Machine learning and natural language processing are used to train the app to read and understand user input and provide relevant output. We live in a competitive world where businesses cannot afford to wait for months and years to release an application. AI for app development is a quick and effective solution to overcome this problem and take new apps to customers in less time. It’s no surprise that the demand for AI product/ app development companies have increased in recent times. Though you can use generative AI or ChatGPT to build your app, partnering with an experienced company will give long-lasting results. You can also hire their maintenance and support services to ensure customer satisfaction and higher ROI. Here are a few ways to use AI product development tools: How to Use AI for App Development Developing an application requires a lot of groundwork, coding, testing, documentation, etc., which leaves the teams on a tight schedule to get everything done in time. However, an AI product development team can streamline the tasks effectively and stay one step ahead. How, you ask? Find out below. Project Management Building and launching an app is a project, isn’t it? The success of your app also depends on how well you manage and organize the entire process. Artificial intelligence can help with planning, monitoring, measuring, collaborating, testing, and a lot more. It speeds up the process and increases the efficiency, accuracy, and performance of the app development team. For example, AI can automate recurring tasks, send alerts and reminders, schedule meetings, manage milestones, and support strategic data-based decision-making. AI also promotes innovation while reducing the risk of error. It makes collaboration easier, thus minimizing communication issues between team members. Automated Code Generation An app requires several pages of code. While all of it doesn’t have to be manually created from scratch (sections of code are taken from open-source and edited), the rest of the work falls on the programmer. With AI app development, most of the code can now be generated automatically. The programmer provides the input parameters for the system to write the code on its own. AI-generated code already adheres to the coding practices and brings more consistency to the project. While AI may not be 100% accurate with intricate logic commands, that part can be handled by the programmer. The boring sections can be auto-generated. It saves time and ensures human expertise. A win-win situation for everyone, isn’t it? Github Copilot is an example of an AI-powered code generation tool. Predictive Analytics Predictive analytics combines statistics and modeling techniques to predict an outcome for the given input. App developers run analytics on historical and present data to get insights about potential bugs and glitches that may arise during the project. From predicting how many lines of code will be written to calculating the resources required, predictive analytics helps app developers in many ways. Predictive analytics is a part of advanced analytics run using AI and ML models. Large datasets are analyzed to make data-driven decisions about project timelines, expenses, risks, opportunities, marketing, etc. Enhance User Experience How can you forget user experience? Keeping the end user happy with the app is the primary goal. While many want to create the best app for users, the execution is far from easy. AI can help here. AI product development companies use machine learning algorithms to analyze data and generate reports about user preferences. This gives them the metrics or indicators that help in adding features to the app. They are also useful for measuring the app’s performance during testing and debugging. Building apps backed by proper research will increase user experience, bring higher engagement, and establish the brand image. Natural Language Processing NLP is used to make a machine under human language in the proper context. It plays a vital role in bridging the gap between human and machine communication. NLP makes apps more intuitive and user-friendly. In fact, it is considered a reliable tool to create user-centric applications. Moreover, NLP-powered tools convert descriptions and feedback into actionable insights for the app-development team. It also automatically translates user requirements into an understandable format, making it easy for developers to customize the application for a wide range of audiences. An example of NLP in app development is a chatbot that responds

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5 Real-World Examples of AI Products in Action- From Start to Finish

AI products generate a lot of discussion. Despite the concerns, artificial intelligence is growing by leaps and bounds. Here, we’ll discuss some well-known real-world examples of AI products and their workings.  Artificial intelligence covers a variety of concepts like machine learning, deep learning, natural language processing, computer vision, etc. AI has become integral to our lives in recent times. AI is versatile and can be used in a plethora of activities, operations, and industries.  The global artificial intelligence market is over $136.6 billion, while the AI industry is expected to grow by 13x in the next seven years with a CAGR (compound annual growth rate) of 38.1%. Another report shows that 87% of global companies think AI gives them a competitive edge in the market. Be it in our personal or professional life, we cannot avoid using AI products in some form.  It’s no surprise that SMBs, MSMEs, emerging businesses, and large enterprises are partnering with AI product development companies to build and deploy custom applications powered by artificial intelligence.  This blog details real-world examples of AI products used by several people from around the globe.  What is an Example of Artificial Intelligence in Action? Almost half the businesses (48%) use some form of artificial intelligence, machine learning, and data analytics in their establishments. AI is meant for personal (individual), team, organizational, and global use. A few common examples of artificial intelligence in our day-to-day lives are listed below.  Text Editors Chatting, commenting, emailing, etc., are a part of our lives. The keypad apps in smartphones have built-in AI to auto-predict the sentences and emojis. Gmail also has an auto-predictor that suggests the next part of the sentence as you type.  Bloggers, content writers, and authors rely on AI text editors like Grammarly, ProWritingAid, etc., to streamline content. From identifying typos and punctuation mistakes to making suggestions about better sentence structures, the software improves the quality of writing. Artificial intelligence algorithms are trained on high-quality language data to identify errors in the input content and rectify them. The AI product development team also trains to understand the grammar rules and differences between different types of English.   Navigation and Maps Maps and GPS are some of the best real-world examples of AI products in action. Instead of using printed maps, we access the map and route through Google or Apple Maps. By connecting the phone to the car’s software, The GPS system will give directions in real-time. While GPS has been here for a while, the system has been enhanced by integrating it with artificial intelligence.  Machine learning algorithms are now taught to understand traffic flow, remember and recognize house numbers, and identify the best route to your destination. Taking things a step further, AI and ML are essential in self-driven cars, where two or more cars constantly communicate with each other.  Facial Recognition Facial recognition is another real-world AI product. The face lock security feature on smartphones is a perfect example of this. Many places, like airports, etc., use facial recognition to confirm your identity. It is a part of security and surveillance systems. The police use facial recognition software when checking leads and suspects to find the criminal.  Smart cameras come with this software to enhance the quality of selfies. Many apps like Snapchat, TikTok, etc., use it to add filters and customize images/ videos. Facial recognition is combined with AR technology for users to get a real-time experience in trying makeup products, eyewear, jewelry, etc.  Recommendation Algorithms Search algorithms are not limited to SEO. They are backed by artificial intelligence to provide personalized results based on your search history and preferences. For example, if you search for a handbag, you will notice relevant ads on your social media platforms and other websites.  Platforms like Facebook, Instagram, X, etc., train their algorithms to provide content based on the posts you like, comment, and share. Over time, the algorithms learn the behavior and tailor the feed accordingly. This is useful as you can avoid seeing content you don’t like. However, it can also feed you misinformation and half-truths.  Healthcare Real-world AI products are rampant in the healthcare industry, with many hospitals and pharma companies investing in advanced technology. Data shows that 38% of medical and healthcare providers use computer-aided diagnosis when treating patients.  Be it better diagnosis, personalized treatment plans, early detection of diseases, remote patient care, or faster drug discovery, artificial intelligence has a prominent role in the healthcare sector. Doctors are using robotic arms for complex surgeries. Mixed reality (MR) devices are also used to access patient information and get remote assistance during operations. Chatbots and virtual therapists are more examples of AI in healthcare.  Customer Service The customer service industry is quickly using chatbots to interact with customers. While the earlier chatbots had limited features, the ones powered by AI are more efficient in handling customer queries and complaints. Moreover, chatbots can interact with multiple users simultaneously and work 24*7 without tiring. This reduces the pressure on the representatives.  Businesses from every industry use chatbots in today’s world to enhance customer service. From tracking information, taking orders, answering queries, and generating tickets, the chatbots can handle several tasks with ease.  Electronic Payments Payment apps are well-known AI products in everyday life. Google Pay, PhonePe, Paytm, etc., are commonly used payment apps in India to make instant electronic payments by scanning a QR code. Many banks now use artificial intelligence algorithms for fraud detection to prevent scams and dubious transactions.  AI is also used for identity management, account management, and security management in banks, insurance companies, and financial institutions. The credit card spending patterns can be analyzed using artificial intelligence to understand the customer’s LTV and increase limits or offer loans.  What are the Real-World Examples of AI Products? Home Assistants Home assistants are the easiest example of AI products used in our day-to-day lives. Many of us have a home assistant device like Alexa, Siri, Cortana, or Google Assistant to play music, place an order, read out webpages, or provide answers to our questions by searching on the internet. They

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