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Top 10 Gen AI Platforms that are Driving Transformation In B2B

“The rise of AI will be the end of I …unless we are careful.”
― Abhijit Naskar


Much in vogue…Gen AI. The internet especially social media seems flooded with Gen AI content! Let’s have a brief about Gen AI and the Top 10 platforms for 2023 which are helping B2B transformation for a better version.

Generative AI is a kind of artificial intelligence that uses data analytics training sets, natural language processing, neural networks, and deep learning to create new and original content. Generative AI content can be created for personal or business use and can take the form of text, images, video, audio, synthetic data, and object models. The most prominent instances of generative AI today are generative language modeling, writing, and imagery tools, such as ChatGPT and Stable Diffusion.

“The danger of generative AI is that it lacks the ability to understand misinformation, leading to incorrectly equating correlation with causation based on incomplete/inaccurate data or lack of contextual awareness required to understand sensitive dependencies between data sets. The unintended consequence is technology shaping societal views on politics, culture and science.”
― Tom Golway

Generative AI companies — both existing enterprises that are adding generative AI to their solution stacks and new generative AI startups are popping up everywhere and quickly. But what makes generative AI companies so different from other AI and ML companies? What are they offering that creates enough demand and buzz to earn funding from the top venture capital firms? In this guide, we’ll cover the top 10 generative AI companies, as well as a deep dive into what generative AI is and why it’s growing in popularity.

Which companies have generative AI?

Who Is Putting Money Into Generative AI? Large tech firms are investing in generative AI startups and technology, including Microsoft, Google, and AWS. One of the main investors in OpenAI, for instance, is Microsoft. Many of these businesses are also developing their own generative AI processes and tools. Leading companies in the market for generative AI include Microsoft (US) and Google (US).

How big is the generative AI market?

The size of the global market for generative AI was assessed at USD 10.79 in 2022, and it is predicted to achieve roughly USD 118.06 by 2032, with a CAGR of 27.02% between the years 2023 and 2032. The graphics below will give a brief overview of the top 11 countries investment towards Gen AI for 2020, 2021, 2022, and 2023 (Jan-April).

What is an example of generative AI?

Any algorithm or model that makes use of AI to produce an entirely new attribute is considered to make use of generative AI. ChatGPT and DALL-E are two instances that come to mind right now that are particularly noteworthy. One such illustration of this would be Google’s unreleased AI text-to-music generator known as MusicLM.

AiThority Insights on Gen AI

Rosaria Silipo, Ph.D., principal data scientist at KNIME gave her views in regard to Gen AI with AiThority.

  • Generative AI has been taking the world by storm lately and its impact on society is only expected to increase. The global Generative AI market size accounted for $7.9 billion in 2021 and is projected to occupy a market size of $110.8 Billion by 2030, marking a growth of 34% from 2022 to 2030. The latest game-changing innovation has been ChatGPT, but before digging deeper into that, there is another simple but important question that must be answered: what  exactly is generative AI?
  • Generative AI encompasses different algorithms (artificially generated images and texts), usually based on neural network architectures like generative adversarial network (GANs), that are trained on massive amounts of data, images or texts, to produce similar looking images or texts, yet not the same.
  • Based on these techniques, we have since observed the creation of unlikely animals, inexistent people’s faces, deep fake news, songs, paintings, and even poetry, according to a selected artistic style. The results have often left us in awe of  the incredible accuracy in representing the details and for simulating human creativity. From a more technical point of view, generative AI has also often left us in awe, with the incredibly well-engineered experiments of humongous neural networks on massive amounts of data.

Recommended: Introducing AudioGPT – The Multi-Modal AI System That’s Redefining Audio Modality

Top 10 Gen AI Companies that are Shaping the Future

  1. OpenAI: Best Overall
  2. Alphabet (Google): Best for Scalability
  3. Microsoft: Best for Business Operations
  4. Hugging Face: Best for Community-Driven AI Development
  5. Jasper: Best for Marketers
  6. Cohere: Best for NLP
  7. Anthropic: Best for Customizable Content
  8. Glean: Best for Employee User Experience
  9. Synthesis AI: Best for Generative AI Use Cases
  10. Stability AI: Best Foundational Model for Other Generative AI Solutions


It’s likely that OpenAI and its products, like ChatGPT, came up in recent discussions regarding generative AI. The most prosperous generative AI firm to date, OpenAI is sponsored by Microsoft and is projected to be valued $29 billion. In addition to ChatGPT, a tool for creating content, and DALL-E, a tool for creating images, OpenAI also provides its API and other models to assist businesses in their work developing generative AI. To suit specific business requirements, GPT-4, conversation models, teach models, fine-tuning models, audio models, image models, and embedding models can all be modified for a cost.

  • A well-funded firm with a good variety of generative AI solutions.
  • Controlled by a charitable organization with a capped profit concept.
  • General availability of the OpenAI API.
  • Occasionally generates inaccurate or even offensive content.
  • Real-time news and data are not quickly incorporated into ChatGPT’s knowledge base; for example, ChatGPT is not able to accurately say what today’s most popular generative AI companies are.
  • Depending on your consumption needs, some models can become very expensive to use.

Alphabet (Google)

Although the majority of sources would place Microsoft ahead of Google in the current generative AI competition, Google is laying the groundwork for what appears to be a promising future in generative AI. Like Microsoft, Google is developing text-based generative AI tools and office suites, but its primary goal is to create a cloud ecosystem that supports generative AI across the board.

For instance, Vertex AI and Generative AI App Builder presently allow a select number of customers to test generative AI features. Additionally, the company is creating AI with ethics and scalability at the forefront. In order to direct the development of AI, it established the AI Principles in 2017. Google periodically publishes reports on how these principles are being used in their most recent releases and product improvements.

  • DeepMind, an Alphabet company, and generative AI lab, is a leading innovator in the field.
  • Google takes a very comprehensive and transparent approach to AI ethics.
  • Google Cloud infrastructure for AI is optimized for both cost and high performance.
  • The company’s initial hesitancy to roll out generative AI could keep it behind other players for some time.
  • Many of Google’s programs are only available through the Trusted Tester Program currently.
  • On the flip side, Google’s race to keep up with Microsoft and other players could lead to the company rolling out tools that have not been thoroughly tested and vetted.


Microsoft is a pioneering force in the field of generative AI, having created a number of its own generative AI tools and providing financing for OpenAI’s cutting-edge research. Recent updates to Microsoft’s Bing have made it the first major search engine to include generative AI functions via a chatbot. All of Microsoft’s 365 services now have support for content generated by artificial intelligence.

Among Microsoft’s most exciting AI innovations is Copilot, a GPT-4-powered assistive tool that is now integrated into several Microsoft applications. These are the main instances of Copilot that are available today:

  • Microsoft 365 Copilot: Assistive content generation in Microsoft 365 apps, like Word and Excel.
  • Microsoft Dynamics 365 Copilot: The world’s first generative AI solution for CRM and ERP.
  • Microsoft Security Copilot: A cybersecurity and incident response solution that is now in preview.
  • The company has already built intuitive generative AI tools into its office suite products and is beginning to dip into other areas like cybersecurity.
  • Its relationship with OpenAI gives Microsoft access to building varied tools on GPT-4 and emerging OpenAI solutions.
  • Users can take advantage of contextualized, generative AI search for free in Bing.
  • Much of the AI ethics and society workforce at Microsoft was recently laid off, though Microsoft still has an Office of Responsible AI.
  • Many of Microsoft’s generative AI developments are based on OpenAI products; with OpenAI’s own success and hopes for growth, it’s difficult to say if this will impact Microsoft’s own scalability over time.
  • Microsoft is possibly going too quickly, without fully contemplating the consequences of its new releases, in a quest to be first to market with innovative generative AI technologies.

Hugging Face

Hugging Face is a developer forum run by the community for projects involving the creation of AI and ML models. Organizations may create their own generative AI solutions and other AI toolkits on-demand because of its broad array of prediction models and datasets. Hugging Face items are now being sold directly to customers by AWS, which just joined the Hugging Face partnership.

Other businesses are working on Hugging Face to improve current AI models and create brand-new ones. Despite the fact that the forum was created with programmers and developers in mind, some Hugging Face solutions, like AutoTrain, need little to no code.

  • Open-source, collaborative development environment.
  • Partnership with AWS.
  • Embeddable generative AI technology for affordable scalability.
  • Limited governance over third-party development tools, like Stable Diffusion.
  • Limited customer support availability.
  • Its developer-facing format makes it less friendly to non-technical users.


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With features to enable blog and email writing, SEO optimization, and the development of art and advertisement visuals, Jasper is a favorite generative AI for marketers and content producers. With Chrome and Microsoft Edge extensions, as well as the latest introduction of in-line experiences, it is simple to use and access. With its emphasis on marketer-style content, Jasper has always had a business orientation.

However, with the launch of Jasper for Business in February 2023, the company elevated this orientation to a new level. The Jasper Brand Voice feature of this package of business improvements enables users to teach Jasper about their particular brand’s tone, style, and vocabulary. To assist marketers in integrating Jasper into their current toolsets and unique CMS creations, the company is now providing offering Jasper API.

  • Focus on branding is a unique differentiator among top generative AI competitors.
  • Intuitive AI Engine curates model selection for different job requests.
  • Easy-to-use interface, especially with browser extensions and in-line cursors.
  • Limited user counts outside of the highest-tier pricing plan.
  • Jasper Brand Voice features are only available in the highest-tier pricing plan.
  • Pricing can get expensive, depending on how many words per month you’re generating.


Several powerful natural language processing tools are available from Cohere for text retrieval, classification, and production. Its complete approach to handling big language models allows users to create new content as well as search through and synthesize massive amounts of previously written content. Cohere makes it feasible and encourages businesses to customize Cohere products to match their own requirements through a user-friendly API, app connectors, and quickstart guidelines.


  • Language models can be customized to specific enterprise requirements; resources for developers are extensive and the API is user-friendly.
  • Models can operate in public, private, and/or hybrid cloud environments.
  • Works with 100+ languages.
  • Pricing can get expensive, especially if you need dedicated models, support channels, or extensive customizations.
  • Somewhat limited integrations once you get past Cohere’s highly functional API.
  • NLP development technology, like Cohere products, is notoriously difficult to understand and develop; customization of Cohere tools will require high levels of expertise.


Leading generative AI startup Anthropic holds the view that quality and security should come before quantity and speed. In addition to policy experts, business executives, and stakeholders from various governmental, academic, nonprofit, and industrial backgrounds, the team’s members include AI researchers and engineers.

The main offering from Anthropic is Claude, an AI assistant that specializes in creating high-quality material and providing summaries and explanations. Claude can be used for text processing, natural dialogue, workflow automation, and Q&A. It is also highly customizable.

  • The results of open, comprehensive studies can be accessed by anybody.
  • Claude is designed to be useful while avoiding harmful content outputs.
  • Generally focused on safe and steerable product development.
  • Anthropic’s focus on extensive safety and performance testing leads to slower product rollouts; only Claude is available to the public.
  • Claude-v1 can get expensive for larger tasks; however, Claude Instant is a more affordable option for these kinds of cases.
  • Limited public-facing scope; Claude mostly provides assistance through text generation, classification, and summarization.


Glean provides internal search powered by generative AI for business apps and ecosystems. Businesses of all sizes and backgrounds use Glean to make it simpler for staff members to look up company knowledge and contextualize it for their responsibilities. Because of the way Glean is built, every business has a customized dynamic knowledge graph that grows and changes based on the people, interactions, and content demands it encounters.

With this strategy, everyone on your team—from your salespeople to your engineers—can utilize Glean to obtain the information they require more quickly and simply. This tool’s other major characteristics make it very functional, including:

  • Verified answers: Save and verify answers to frequently asked questions.
  • Curated collections: The ability for individual teams to collect and organize documents and links that are most relevant for their team; ideal for onboarding.
  • GoLinks: Short links that can be created and saved for your most commonly used resources.
  • Glean’s tooling respects and enforces security permissions on all fronts, despite its interoperability with a wide variety of enterprise apps and databases.
  • The user interface is clean and easy to understand.
  • Semantic understanding supports more personalized search results and AI-generated answers.
  • Though it’s advertised as an enterprise support tool, Glean’s functionality doesn’t stretch much beyond cognitive enterprise search and knowledge storage.
  • Glean operates with a more minor support and development team than many other players on this list.
  • Limited transparency for product pricing.

Synthesis AI

If you only take enterprise value into consideration, Synthesis AI is among the smallest companies on this list. However, when you take into account the range of goods and services the business currently provides its clients, it’s one of the biggest and most promising. The main focus of synthesis AI is the generation of synthetic data, images, and videos for computer vision; there are many uses for these synthetic outputs. The following are some of the use cases and applications that Synthesis AI’s Humans and Scenarios products currently address:

  • Identify verification.
  • Avatar creation.
  • Driver monitoring.
  • Pedestrian detection.
  • AR, VR, and XR.
  • Teleconferencing.
  • Cybersecurity.
  • Virtual try-on.
  • Effective video, data, and image labeling, particularly for humans.
  • Focus on AI ethics and diversity.
  • Capable of photorealistic image generation.
  • Human-centric data focus limits its relevance for text and non-human image generation.
  • Limited visibility into the company’s profitability and stability metrics.
  • Limited outside funding rounds at this point in time.

Stability AI

Many of the most cutting-edge generative AI solutions are powered by stability AI. Many other businesses have chosen to build off of the firm’s deep learning model, Stable Diffusion, which is available as open-source technology, largely via GitHub and Hugging Face. Additionally, the business provides a sizable API library that third-party users can access, as well as a Discord community where users can talk about how they employ the Stable Diffusion technology.

It’s difficult to discount the influence of a business with over 140,000 members in its open-source research hubs and a plethora of customers (some of whom are even mentioned on this list). The company has faced criticism for its image-sourcing practices, and there are also rumors of profitability issues within the organization.

  • Open-source accessibility makes Stability AI tools highly customizable for experienced developers.
  • Strong open-source communities in Discord, GitHub, and Hugging Face.
  • A variety of relevant plugins and APIs are available to users.
  • High spending on foundational technology has hurt profitability for the company.
  • Costs for DreamStudio and API usage can go up quickly, depending on how many credits you need.

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It’s 2023 and we are in some form using AI knowingly or unknowingly. What calculators were like 100 years back, the same is with AI in 2023. Initially, resistance and confusion persist which gradually fades away making the tech a common acceptance. Similarly, Generative AI will gradually become the new normal for all of us.

Companies specializing in generative AI provide engaging AI technology to their clients, developers, and technical users. If you’re interested in the potential applications of generative AI, the firms on this list have developed some of the most intriguing tools and use cases to date. They’re worth keeping an eye on.

Some of these businesses launched quickly, producing numerous items and raising millions of dollars in capital. The leading generative AI startups are developing solutions in each of these scenarios that could eventually scale to meet future business and individual user demands.

Generative technology, where deep learning models can work in conjunction with humans to generate fresh content and ideas, is quickly becoming the next frontier in software.

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