By Emilija Nikolic at Beststocks.com

Generative AI technology is rapidly transforming the landscape of enterprise workflows across various industries. This advanced technology, leveraging the power of artificial intelligence (AI) and machine learning (ML), is reshaping how businesses interact with their applications, streamline processes, and enhance user experiences. In this article, we will delve into the rise of generative AI and its profound impact on modern enterprise workflows.

The Evolution of Generative AI

Generative AI represents a significant advancement in AI technology, particularly in its ability to generate human-like text, images, and other content autonomously. Unlike traditional AI models that rely on pre-defined rules and data, generative AI models are trained on vast datasets and learn to generate new content based on the patterns and information they’ve absorbed.

One of the key features of generative AI is its versatility across various applications and industries. From natural language processing to image generation and beyond, generative AI has shown remarkable potential in transforming how businesses interact with their data and applications.

Reshaping Enterprise Workflows

Generative AI is revolutionizing enterprise workflows by streamlining processes, enhancing productivity, and delivering immersive user experiences. In sectors ranging from finance and healthcare to manufacturing and government, businesses are leveraging generative AI to automate repetitive tasks, analyze complex data sets, and make data-driven decisions.

One of the primary ways generative AI is reshaping enterprise workflows is through its ability to provide personalized and context-aware interactions. By understanding user intent and context, generative AI systems can deliver tailored responses and recommendations, significantly improving user engagement and satisfaction.

Future Implications

The future implications of generative AI in enterprise workflows are vast and promising. As the technology continues to evolve, businesses can expect to see increased automation, improved decision-making processes, and enhanced user experiences. Generative AI has the potential to revolutionize how businesses interact with their applications, enabling more intuitive and efficient workflows.

Moreover, generative AI opens up opportunities for innovation and creativity, enabling businesses to explore new ways of engaging with customers, optimizing processes, and driving growth. From personalized recommendations to automated content generation, the possibilities are endless.

Enhancing Enterprise Efficiency with Advanced AI Solutions

Alan AI, a provider of Generative AI technology for enhancing user interactions in enterprise applications, has recently achieved “Awardable” status within the Chief Digital and Artificial Intelligence Office’s Tradewinds Solutions Marketplace, an integral component of the Department of Defense’s suite of tools designed to expedite the adoption of AI/ML, data, and analytics capabilities.

Unlike traditional chat assistants, Alan AI’s solution offers a pragmatic approach by providing immersive user experiences through its integration with existing Graphical User Interfaces (GUIs) and transparent explanations of AI decision-making processes, per a recent press release. 

The cornerstone of Alan AI’s offering lies in its ‘Unified Brain’ architecture, which seamlessly connects enterprise applications, Application Programming Interfaces (APIs), and diverse data sources to streamline workflows across a spectrum of industries, ranging from Government and Manufacturing to Energy, Aviation, Higher Education, and Cloud Operations.

Recognized for its innovation, scalability, and potential impact on Department of Defense (DoD) missions, Alan AI’s comprehensive solution revolutionizes natural language interactions within enterprise applications on both mobile and desktop platforms. Through its support for text and voice interfaces, Alan AI empowers businesses to navigate complex workflows with ease, ultimately driving efficiency and productivity gains across various sectors.

Conclusion

In conclusion, the rise of generative AI is reshaping enterprise workflows in profound ways, driving efficiency, innovation, and user engagement across industries. As businesses continue to harness the power of generative AI, it is crucial to navigate the evolving landscape thoughtfully, addressing challenges while maximizing the transformative potential of this advanced technology. With proper governance, training, and ethical considerations, generative AI holds the promise of revolutionizing how businesses operate and interact with their applications in the years to come.

Alan AI is proud to announce a landmark achievement in Generative AI with the granting of US Patent No. 11,798,542, titled “Systems and Methods for Integrating Voice Controls into Applications.” This patent represents a significant leap in augmenting language understanding with a visual context and, in parallel, providing immersive user experiences for daily use in enterprises.

While the Generative AI industry is rapidly recognizing the crucial role of context (leading Language Models (LLMs) such as GPT-4, Gemini, Mistral, and LLaMa2 are constantly evolving, aiming to expand their context window to capture a broader range of information and can already handle up to 200,000 tokens); at Alan AI, we understand visual information’s pivotal role in human perception – approximately 80% of our sensory input! 

What Makes This a Game-Changer?

Our innovative approach integrates visual context with AI language understanding, creating a new paradigm in the industry. Recognizing that visual information forms a major part of human perception, we’ve developed a system that goes beyond the limitations of current language models. By incorporating visual context, we’re transforming how AI interacts with its environment, making “a picture worth millions of tokens.“

Revolutionizing RAG in LLMs with Visual Context

Alan AI’s approach innovatively augments Retrieval-Augmented Generation (RAG) with visual context when using Large Language Models (LLMs). This enhancement addresses the limitations of RAG, where input token size increases with prompt size, often leading to verbose and less controllable outputs. We provide a more relevant and precise context by integrating visual context — elements like the user’s current screen, workflow stage, and text from previous queries.

This integration means visual elements are passive data and active components in generating responses. They effectively increase the ‘context window’ of the LLM, allowing it to understand and respond to queries with a previously unattainable depth, epitomizing our philosophy that “a picture is worth millions of tokens.” This technical enhancement significantly improves AI-generated responses’ accuracy, relevance, and efficiency in enterprise environments.

Crafting an Immersive User Experience – Synchronizing Text, Voice, and Visuals

In addition, Alan AI is pushing the boundaries of Generative AI for responses. Our technology interprets visual context, such as screen and application states, allowing for precise comprehension and response crafting by updating the appropriate sections of the application GUI. Our AI Assistants do more than process requests; they guide users interactively, harmonizing text and voice with visual GUI elements for a truly immersive experience.

The Transformative Benefits for Enterprises

In the enterprise realm, accuracy and precision are paramount. Our integration of visual context with language processing ensures responses that are not just factually accurate but contextually rich and relevant. This leads to enhanced user experiences, increased productivity, and effectiveness in enterprise applications.

A New Benchmark for AI Interaction Excellence

Our commitment to integrating visual cues is about building trust. Ensuring our AI Assistants understand verbal and non-verbal communication creates a user experience that aligns with human expectations. This approach is key to successfully implementing Generative AI across various enterprise scenarios.

For additional information on Alan AI and how utilizing application context builds trust and boosts employee productivity, contact sales@alan.app.

Generative AI technologies, epitomized by GPT (Generative Pre-trained Transformer) models, have transformed the landscape of artificial intelligence. These Large Language Models (LLMs) possess remarkable text generation capabilities, making them invaluable across a multitude of industries. However, along with their potential benefits, LLMs also introduce significant challenges concerning data privacy and security. In this in-depth exploration, we will navigate through the complex realm of data privacy risks associated with LLMs and delve into innovative solutions that can effectively mitigate these concerns.

The Complex Data Privacy Landscape of LLMs

At the heart of the data privacy dilemma surrounding LLMs is their training process. These models are trained on colossal datasets, and therein lies the inherent risk. The training data may inadvertently contain sensitive information such as personally identifiable data (PII), confidential documents, financial records, or more. This sensitive data can infiltrate LLMs through various avenues:

Training Data: A Potential Breach Point

LLMs gain their proficiency through the analysis of extensive datasets. However, if these datasets are not properly sanitized to remove sensitive information, the model might inadvertently ingest and potentially expose this data during its operation. This scenario presents a clear threat to data privacy.

Inference from Prompts: Unveiling Sensitive Information

Users frequently engage with LLMs by providing prompts, which may sometimes include sensitive data. The model processes these inputs, thereby elevating the risk of generating content that inadvertently exposes the sensitive information contained within the prompts.

Inference from User-Provided Files: Direct Ingestion of Sensitivity

In certain scenarios, users directly submit files or documents to LLM-based applications, which can contain sensitive data. When this occurs, the model processes these files, thus posing a substantial risk to data privacy.

The core challenge emerges when sensitive data is dissected into smaller units known as LLM tokens within the model. During training, the model learns by scrutinizing these tokens for patterns and relationships, which it then uses to generate text. If sensitive data makes its way into the model, it undergoes the same processing, jeopardizing data privacy.

Addressing Data Privacy Concerns with LLMs

Effectively addressing data privacy concerns associated with LLMs demands a multifaceted approach that encompasses various aspects of model development and deployment:

1. Privacy-conscious Model Training

The cornerstone of data privacy in LLMs lies in adopting a model training process that proactively excludes sensitive data from the training datasets. By meticulously curating and sanitizing training data, organizations can ensure that sensitive information remains beyond the purview of the model.

2. Multi-party Model Training

Many scenarios necessitate the collaboration of multiple entities or individuals in creating shared datasets for model training. To achieve this without compromising data privacy, organizations can implement multi-party training. Custom definitions and strict data access controls can aid in preserving the confidentiality of sensitive data.

3. Privacy-preserving Inference

One of the pivotal junctures where data privacy can be upheld is during inference, when users interact with LLMs. To protect sensitive data from inadvertent exposure, organizations should implement mechanisms that shield this data from collection during inference. This ensures that user privacy remains intact while harnessing the power of LLMs.

4. Seamless Integration

Effortlessly integrating data protection mechanisms into existing infrastructure is paramount. This integration should effectively prevent plaintext sensitive data from reaching LLMs, ensuring that it is only visible to authorized users within a secure environment.

De-identification of Sensitive Data

A critical aspect of preserving data privacy within LLMs is the de-identification of sensitive data. Techniques such as tokenization or masking can be employed to accomplish this while allowing LLMs to continue functioning as intended. These methods replace sensitive information with deterministic tokens, ensuring that the LLM operates normally without compromising data privacy.

Creating a Sensitive Data Dictionary

Enterprises can bolster data privacy efforts by creating a sensitive data dictionary. This dictionary serves as a reference guide, allowing organizations to specify which terms or fields within their data are sensitive. For example, a project’s name can be marked as sensitive, preventing the LLM from processing it. This approach helps safeguard proprietary information and maintain data privacy.

Data Residency and Compliance

Organizations must also consider data residency requirements and align their data storage practices with data privacy laws and standards. Storing sensitive data in accordance with the regulations of the chosen geographical location ensures compliance and bolsters data privacy efforts.

Integrating Privacy Measures into LLMs

To ensure the effective protection of sensitive data within LLM-based AI systems, it is crucial to seamlessly integrate privacy measures into the entire model lifecycle:

Privacy-preserving Model Training

During model training, organizations can institute safeguards to identify and exclude sensitive data proactively. This process ensures the creation of a privacy-safe training dataset, reducing the risk of data exposure.

Multi-party Training

In scenarios where multiple parties collaborate on shared datasets for model training, organizations can employ the principles of multi-party training. This approach enables data contributors to de-identify their data, preserving data privacy. Custom definitions play a pivotal role in this process by allowing organizations to designate which types of information are sensitive.

Privacy-preserving Inference

Intercepting data flows between the user interface and the LLM during inference is a key strategy for protecting sensitive data. By replacing sensitive data with deterministic tokens before it reaches the model, organizations can maintain user privacy while optimizing the utility of LLMs. This approach ensures that sensitive data remains protected throughout the user interaction process.

The Final Word

As the adoption of LLMs continues to proliferate, data privacy emerges as a paramount concern. Organizations must be proactive in implementing robust privacy measures that enable them to harness the full potential of LLMs while steadfastly safeguarding sensitive data. In doing so, they can maintain user trust, uphold ethical standards, and ensure the responsible deployment of this transformative technology. Data privacy is not merely a challenge—it is an imperative that organizations must address comprehensively to thrive in the era of generative AI.

In an era where data is king and digital transformation is the highway to success, innovative companies are constantly on the lookout for technologies that can propel them into the future. One such groundbreaking technology is Generative AI (Gen AI), a game-changer in the realm of artificial intelligence. This article unfolds the synergies between an innovative culture and the successful deployment of Gen AI, elucidating how leading innovators are leveraging this technology to soar to new heights.

A Sneak Peek into Generative AI

Generative AI, an avant-garde technology, is revolutionizing the way companies interact with data and make decisions. This form of AI specializes in self-writing code and facilitates “no touch” decision-making, thereby acting as a catalyst for efficiency and innovation. The prowess of Gen AI lies in its ability to learn from data, generate new data, and automate processes, thereby reducing the human intervention required in decision-making and coding.

The Gen AI – Innovation Nexus

The essence of this article lies in the strong link between an innovative culture and the successful deployment of Gen AI. It’s a symbiotic relationship where innovation fosters the apt utilization of Gen AI, and in return, Gen AI fuels further innovation. This relationship cultivates a fertile ground for companies to evolve, adapt, and thrive in the competitive digital landscape.

Unveiling the Five Pristine Practices

To harness the full potential of Generative AI, there are five pristine practices that top innovators are adopting. These practices are not just about employing a technology; they are about creating an ecosystem where Gen AI and innovation flourish together.

1. Deploying Gen AI at Scale

Scaling is a term often thrown around in the corporate world, but when it comes to Gen AI, it’s about integrating this technology across the organizational spectrum. Deploying Gen AI at scale means embedding it in various business processes, thereby reaping benefits like enhanced efficiency, reduced operational costs, and improved decision-making.

2. Cultivating a Culture of Innovation

A culture of innovation is the bedrock on which the successful deployment of Gen AI rests. It’s about fostering a mindset of continuous learning, experimentation, and adaptation. Companies with an innovative culture are more adept at utilizing Gen AI to its fullest potential, thereby staying ahead in the innovation race.

3. Investing in R&D and Digital Tools

Investment in Research & Development (R&D) and digital tools is akin to sowing seeds for a fruitful harvest in the future. It’s about staying updated with the latest advancements in AI and digital technologies, and integrating them into the organizational fabric.

4. Evolving Operating Models to be AI-led

Transitioning to AI-led operating models is about letting AI take the driver’s seat in decision-making and operations. It’s a paradigm shift that requires a strategic approach to ensure that AI and human intelligence work in harmony.

5. Optimally Leveraging Gen AI Technologies

Knowing how to optimally leverage Gen AI technologies is about finding the sweet spot where Gen AI yields maximum value. It’s about understanding the capabilities of Gen AI and aligning them with organizational goals to create a roadmap for success.

The Road to a Gen AI-empowered Future

The article sheds light on the profound impact that Gen AI can have on innovative companies. The journey towards a Gen AI-empowered future is filled with opportunities and challenges. By adopting the aforementioned practices, companies can navigate this journey successfully, unleashing a new era of innovation and growth.

Setting the Pace for a Bright Future

The insights shared in the article are a beacon for companies aspiring to lead in the digital age. Generative AI is not just a technology; it’s a vessel of innovation that can propel companies into a future filled with possibilities. The key to success lies in fostering an innovative culture, embracing Gen AI, and navigating the digital transformation journey with a vision for the future.

The tale of Generative AI and innovative companies is just beginning to unfold. As organizations across the globe race towards digital supremacy, Gen AI emerges as a formidable ally. The fusion of an innovative culture and Gen AI is a blueprint for success, setting a benchmark for others to follow. Through the lens of this article, we get a glimpse of the bright horizon that lies ahead for the pioneers of digital innovation.

Google Bard, the technological marvel from the house of Google, has been a talking point in recent times, especially among the tech-savvy crowd. This conversational AI product rolled out a significant update last week, which garnered a mixed bag of reviews. However, the spotlight didn’t dim there; an older feature of Bard is now under scrutiny, which has stirred a conversation around privacy in the digital realm.

Google Bard: An Overview

Before delving into the controversy, let’s take a moment to understand the essence of Google Bard. It’s a conversational AI designed to assist users in finding answers, similar to conversing with a knowledgeable friend. Its user-friendly interface and the promise of enhanced search experience have made it quite a charmer among the masses.

The Unraveling of a Privacy Concern

The calm waters were stirred when SEO consultant Gagan Ghotra observed a potentially privacy-invasive feature. He noticed that Google Search began indexing shared Bard conversational links into its search results pages. This startling revelation implied that any shared link could be scraped by Google’s crawler, thus showing up publicly on Google Search.

The Intricacies of The Feature

The feature under the scanner is Bard’s ability to share conversational links with a designated third party. The concern was that any conversation shared could end up being indexed by Google, making it publicly available to anyone across the globe. This was a significant concern as users share these links with trusted individuals without the knowledge that it could be exposed to the wider world.

The Ripple Effect

This discovery by Ghotra sent ripples through the tech community and beyond. It opened up a can of worms regarding the privacy of digital conversations, something we all take for granted. The question was, how could such a lapse happen, and what were the implications?

The Iceberg of Digital Privacy

This incident is just the tip of an iceberg. It brings to light the often overlooked aspect of digital privacy. In a world where sharing has become second nature, the boundaries of privacy are often blurred. This incident served as a wake-up call to many who were oblivious to the potential risks involved in digital sharing.

Google’s Stance and The Communication Gap

In response to Ghotra’s observation, Google Brain research scientist Peter J. Liu mentioned that the indexing only occurred for conversations that users chose to share, not all Bard conversations. However, Ghotra highlighted a crucial point – the lack of awareness among users regarding what sharing implies in the digital realm.

The User Awareness Conundrum

The crux of the problem lies in the gap between user understanding and the actual implications of digital sharing. Most users, as Ghotra pointed out, were under the impression that sharing was limited to the intended recipient. The fact that it could be indexed and made public was a revelation, and not a pleasant one.

A Reflective Journey: Lessons to Learn

This episode with Google Bard is not just an isolated incident but a reflection of a larger issue at hand. It’s imperative to understand the dynamics of digital privacy and the responsibilities that come with it, both as users and as tech behemoths like Google.

Bridging the Gap: A Collective Responsibility

The onus is not just on the tech giants to ensure privacy and clear communication regarding the functionalities but also on the users to educate themselves about the digital footprints they leave. It’s a two-way street where both parties need to walk towards each other to ensure a safe and privacy-compliant digital experience.

The Road Ahead: Navigating the Privacy Landscape

As we move forward into the digital future, incidents like these serve as a reminder and a lesson. The balance between user-friendly features and privacy is a delicate one, and it’s imperative to continually evaluate, adapt, and educate.

Fostering a Culture of Privacy Awareness

Creating a culture of privacy awareness, clear communication, and continuous learning is the need of the hour. It’s not just about addressing the issues as they arise, but about creating a robust system that pre-empts potential privacy threats, ensuring a secure digital interaction space for all.

The saga of Google Bard’s shared conversation indexing issue is a chapter in the larger narrative of digital privacy. It’s a narrative that will continue to evolve with technology, and it’s up to us, the users and the creators, to script it in a way that ensures a safe, secure, and enriching digital experience for all.

Can Enterprises afford the risk of their conversations surfacing over the Internet? How can enterprises deploy AI that addresses their privacy and security concerns? How can enterprise govern the AI deployments? 

In the age of digital transformation, the way we search for information and products online is rapidly evolving. One of the most exciting developments in this space is the rise of conversational search and recommendation systems. These systems are designed to engage users in a dialogue, allowing for a more interactive and personalized search experience. But what exactly is conversational search, and how does it differ from traditional search methods?

Conversational Search vs. Traditional Search

Traditional search systems rely on users inputting specific keywords or phrases to retrieve relevant results. The onus is on the user to refine their search query if the results are not satisfactory. In contrast, conversational search systems engage the user in a dialogue. They can ask clarifying questions to better understand the user’s needs and provide more accurate results.

For instance, imagine searching for a new smartphone online. In a traditional search setting, you might input “best smartphones 2023” and sift through the results. With a conversational system, the search might start with a simple query like “I’m looking for a new phone.” The system could then ask follow-up questions like “Which operating system do you prefer?” or “What’s your budget?” to refine the search results in real-time.

The Power of Questions

One of the most significant advantages of conversational search systems is their ability to ask questions. By actively seeking clarity, these systems can better understand user needs and preferences. This proactive approach is a departure from conventional search systems, which are more passive and rely on the user to provide all the necessary information.

The ability to ask the right questions at the right time is crucial. It ensures that the conversation remains efficient and that users’ needs are met as quickly as possible. This dynamic is especially important in e-commerce settings, where understanding a customer’s preferences can lead to more accurate product recommendations.

The Role of Memory Networks

To facilitate these interactive conversations, advanced technologies like Multi-Memory Networks (MMN) are employed. MMNs can be trained based on vast collections of user reviews in e-commerce settings. They are designed to ask aspect-based questions in a specific order to understand user needs better.

For example, when searching for a product, the system might first ask about the product category, then delve into specific features or attributes based on the user’s responses. This structured approach ensures that the most critical questions are asked first, streamlining the conversation.

Personalization is Key

Another exciting development in conversational search is the move towards personalization. Just as no two people are the same, their search needs and preferences will also differ. Recognizing this, personalized conversational search systems are being developed to cater to individual users.

Using data from previous interactions and searches, these systems can tailor their questions and recommendations to each user. This level of personalization can significantly enhance the user experience, leading to more accurate search results and higher user satisfaction.

Challenges and Solutions

While conversational search and recommendation systems offer many advantages, they are not without challenges. One of the primary challenges is the need for large-scale data to train these systems effectively. Real-world conversation data is scarce, making it difficult to develop and refine these systems.

However, with the rapid advancements in neural NLP research and the increasing integration of AI into our daily lives, solutions are emerging. Companies are now investing in gathering conversational data, simulating user interactions, and even using synthetic data to train these systems.

Moreover, there’s the challenge of ensuring that these systems understand the nuances and subtleties of human language. Sarcasm, humor, and cultural references can often be misinterpreted, leading to inaccurate results. Advanced NLP models and continuous learning are being employed to overcome these hurdles.

Integration with Other Technologies

Conversational search doesn’t exist in a vacuum. It’s being integrated with other technologies to provide an even more seamless user experience. Voice search, augmented reality (AR), and virtual reality (VR) are some of the technologies that are converging with conversational search. Imagine using voice commands to initiate a conversational search on your AR glasses or getting product recommendations in a VR shopping mall.

The Road Ahead

The future of conversational search looks promising. As these systems become more sophisticated and better trained, we can expect even more interactive and personalized search experiences. The integration of AI, AR, VR, and other emerging technologies will further revolutionize the way we search and shop online.
In conclusion, conversational search and recommendation systems represent the next frontier in online search. By engaging users in a dialogue and asking the right questions, these systems can provide more accurate and personalized search results. As technology continues to evolve, we can look forward to even more advanced and user-friendly search systems in the future.

In the decades following Alan Turing’s landmark paper on Computing Machinery and Intelligence, the promise of Artificial Intelligence (AI) has evolved from pure theory to an omnipresent technology. AI now influences a spectrum of industries, reshaping how we consume entertainment, diagnose critical illnesses, and drive business innovation. 

However, beneath these promising advancements lies a sobering reality: many AI initiatives stumble before making a real-world impact. Here, we explore why, despite its potential, many companies face challenges when diving into AI.

The Hurdles to Effective AI Deployment

The Great Expectation vs. Reality Gap

While AI’s potential applications span an impressive range, achieving these results often proves elusive. Recent data indicates a troubling rate of failed AI projects; some studies even suggest that a mere 12% of AI initiatives successfully transition from pilot stages to full-scale production. This disparity begs the question: What is causing these hiccups?

 Root Causes of AI Failures

• Reproducibility Issues: Often, an initial AI solution might display outstanding performance, but replicating its success proves challenging.
• Team Misalignment: A disconnect between Data Science and MLOps teams can hamper the full potential of AI models.
• Scaling Struggles: To make a meaningful impact, AI needs to operate at a vast scale, which many organizations struggle to achieve due to various constraints.

Enter the realm of Applied AI, which seeks to bridge these gaps.

 What Exactly is Applied AI?

Defined, Applied AI emphasizes the tools and strategies required to move AI from a mere experiment to a critical production asset. It stresses not only the creation and launch of AI models but also the importance of obtaining tangible, real-world outcomes. The industry needs an Application-Level AI Technology Platform that a) provides a tightly integrated technology stack and b) enables an iterative deployment and discovery of the AI experience to realize the ROI.

A common misconception is that AI predominantly revolves around programming. In reality, AI encompasses an intricate ecosystem of tools, processes, and infrastructure components.

 Critical Components for AI Success

 1. Data Management: The Lifeblood of AI

• Data Warehousing: Efficient data storage solutions, like Hadoop, can cope with AI’s rigorous data demands.
•  Understanding Data: A comprehensive data catalog aids in the comprehension and utilization of available data.
• Ensuring Data Quality: Maintaining data accuracy from inception to production is non-negotiable.
•  Optimized Data Pipelines: The foundation for data flow and processing must be robust and fine-tuned.

 2. Networking for AI: Beyond Traditional Boundaries

• Deep learning solutions, at their core, rely on effective communication protocols. As AI models handle vast amounts of information, conventional networking solutions often fall short. An AI-ready network requires a revamped infrastructure, emphasizing performance, scalability, and real-time data transmission.

 3. Efficient AI Data Processing & Training

• Harnessing the Power of GPUs: AI models, especially deep learning, need significant computational resources. Graphics processing units (GPUs), with their parallel processing capabilities, are the go-to choice for many enterprises.

 4. Functionality: The Technical Backbone

• Model Handling: This involves storing, evaluating, and updating various AI models efficiently.
• Feature Engineering: Creating new, impactful data features can drastically improve model performance.
• Model Evaluation: Companies need strategies for comparing different AI models, ensuring only the best is in play.

 5. Governance: AI’s Guiding Principles

• Access & Control: Ensuring only authorized individuals modify AI models can mitigate potential risks.
• Change Management: Effective version control systems are indispensable in the dynamic world of AI.

 6. Continuous Monitoring

• Performance Tracking: As AI models can degrade over time, real-time monitoring is a must to identify and rectify issues promptly.

Applied AI in Action: Industry Pioneers

Several forward-thinking enterprises are already leveraging Applied AI to reshape their business landscapes.

Target’s Insightful Innovations

By consolidating data from diverse sources, Target has been harnessing AI’s power to offer a more personalized shopping experience, predicting significant life events that may influence consumers’ purchasing patterns.

Starbucks’ Deep Brew Revolution

Starbucks’ AI journey, termed “Deep Brew,” aims to revolutionize every business facet. By integrating comprehensive data streams, Starbucks is pioneering initiatives ranging from personalized recommendations to predictive maintenance of their coffee machines.

Facebook’s AI Mastery

Facebook, with its colossal user base, is pushing the AI envelope. They are leveraging AI for diverse tasks, from content moderation to targeted advertising. Their advanced AI strategy encompasses areas like computer vision, multilingual technologies, and VR experiences.

Wrapping Up

While the AI realm holds immense promise, the journey to successful AI implementation is fraught with challenges. Organizations must recognize these hurdles and, with the help of Applied AI, craft a strategic approach that ensures AI’s transformative potential is fully realized.

In the ever-evolving landscape of technology, few innovations have captured the imagination of the tech community as much as Generative AI. From creating realistic images of non-existent people to generating human-like text, the capabilities of Generative AI have been both awe-inspiring and, at times, controversial. But like all technologies, Generative AI has had its peaks and troughs of expectations and real-world applications. This journey can be best described using the concept of the “hype curve.” So, where are we now on the Generative AI hype curve? Let’s dive in.

 Understanding the Hype Curve

Before we delve into Generative AI’s position on the curve, it’s essential to understand what the hype curve is. Popularized by Gartner, the hype curve is a graphical representation of the maturity, adoption, and social application of specific technologies. It consists of five phases:

1. Innovation Trigger: The phase where a new technology is introduced, and early proof-of-concept stories generate media interest.

2. Peak of Inflated Expectations: Early publicity produces several success stories, often accompanied by scores of failures. Some companies act, while others do not.

3. Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail.

4. Slope of Enlightenment: More instances of how the technology can benefit enterprises start to crystallize and become more widely understood.

5. Plateau of Productivity: Mainstream adoption starts to take off. The technology’s broad market applicability and relevance become clearly paying off.

 Generative AI’s Journey on the Hype Curve

Innovation Trigger: Generative AI’s journey began with the introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow and his colleagues in 2014. This was a groundbreaking moment, as GANs could generate data resembling the input data they were trained on. The tech community quickly recognized the potential of this innovation.

Peak of Inflated Expectations: As with many new technologies, the initial excitement led to inflated expectations. We saw a surge in startups and established tech giants investing heavily in Generative AI. This period saw the creation of AI-generated art, music, and even the infamous deepfakes. The potential seemed limitless, and the media was abuzz with both the promises and perils of Generative AI.

Trough of Disillusionment: However, as the technology matured, it became evident that Generative AI had its limitations. Training GANs required significant computational power, leading to environmental concerns. Moreover, the ethical implications of deepfakes and the potential misuse in misinformation campaigns became glaringly apparent. The initial excitement was met with skepticism, and many began to question the real-world applicability of Generative AI.

 Where Are We Now?

Given the history, it’s safe to say that we are currently transitioning from the “Trough of Disillusionment” to the “Slope of Enlightenment.” The wild expectations have been tempered, and the focus has shifted from mere fascination to practical applications.

Several factors indicate this transition:

1. Business ROI:

• Business ROI: Identify customer journeys that can be accelerated and the operational efficiency improvements in business operations.
• Specialized Applications: Instead of trying to fit Generative AI everywhere, businesses are finding niche areas where it can add genuine value. For instance, fashion brands are using it for design inspiration, and game developers are leveraging it to create diverse virtual worlds.

2. Product:

• Ethical Guidelines: Recognizing the potential misuse, there’s a concerted effort to establish ethical guidelines for Generative AI. This includes watermarking AI-generated content and developing algorithms to detect deepfakes.
• Specialized Applications: Instead of trying to fit Generative AI everywhere, businesses are finding

 The Road Ahead

As we ascend the “Slope of Enlightenment,” it’s crucial to approach Generative AI with a balanced perspective. While the technology holds immense potential, it’s not a silver bullet for all problems. Collaboration between AI researchers, ethicists, and industry leaders will be pivotal in ensuring that Generative AI is used responsibly and to its fullest potential.

In conclusion, the trajectory of Generative AI through the hype curve underscores the importance of discerning application and responsible deployment. As enterprises seek to derive tangible ROI from Generative AI, it becomes imperative to leverage the vast reservoirs of enterprise data securely, behind firewalls. This ensures not only the generation of insightful AI responses but also the translation of these insights into actionable strategies that can propel business growth. The current phase of the hype curve, transitioning from disillusionment to enlightenment, emphasizes the need for specialized applications, ethical considerations, and improved efficiency. As we move forward, the onus is on businesses to harness the transformative potential of Generative AI, while concurrently addressing its challenges, to truly accelerate their operations and offerings.

Generative AI models, especially large language models (LLMs) like ChatGPT, have created a lot of excitement in recent months for their ability to generate human-like language, produce creative writing, write software code, and even perform tasks like translation and summarization. These models can be used for a wide range of applications, from chatbots and virtual assistants to content creation and customer service. However, the potential of these models goes far beyond these initial use cases.

We are just at the beginning of the boost in productivity that these models can bring. LLMs have the potential to revolutionize the way we work and interact with technology. And we are discovering new ways to make them better and use them to solve complex problems.

How can businesses improve their operational efficiency with generative AI? How can the users of products and services leverage generative AI to expedite business outcomes? Along the sames lines what can employees of the business accomplish? Here’s what you need to know about personalized generative AI and how you can link their responses and with relevant actions to realize economic gains. 

Why use your own data?

LLMs are incredibly powerful and versatile thanks to their ability to learn from vast amounts of data. However, the data they are trained on is general in nature, covering a wide range of topics and domains. While this allows LLMs to generate high-quality text that is generally accurate and coherent, it also means that they may not perform well in specialized domains that were not included in their training data.

When pushed into your enterprise, LLMs may generate text that is factually inaccurate or even nonsensical. This is because they are trained to generate plausible text based on patterns in the data they have seen, rather than on deep knowledge of the underlying concepts. This phenomenon is called “hallucination,” and it can be a major problem when using LLMs in sensitive fields where accuracy is crucial.

By customizing LLMs with your own data, you can make sure that they become more reliable in the domain of your application and are less likely to generate inaccurate or nonsensical text. Many business require 100% reliable and accurate responses!

Customization can make it possible to use LLMs in sensitive fields where accuracy is very important, such as in healthcare, education, government, and legal. As you improve the quality and accuracy of your model’s output, you can generate actionable responses that users can trust and use to take relevant actions. As the accuracy of the model continues to increase, it goes from knowledge efficiency to operational efficiency, enabling users to streamline or automate actions that previously required intense manual work. This directly translates into time saving, better productivity, and a higher return on investment.

How to personalize LLMs with your own data

There are generally two approaches to customizing LLMs: fine-tuning and retrieval augmentation. Each approach has its own benefits and tradeoffs.

Fine-tuning involves training the LLM with your own data. This means taking a foundation model and training it on a specific set of proprietary data, such as health records, educational material, network logs, or government documents. The benefit of fine-tuning is that the model incorporates your data into its knowledge and can use it in all kinds of prompts. The tradeoff is that fine-tuning can be expensive and technically tricky, as it requires a large amount of high-quality data and significant computing resources.

Retrieval augmentation uses your documents to provide context to the LLM. In this process, every time the user writes a prompt, you retrieve a document that contains relevant information and pass it on to the model along with the user prompt. The model then uses this document as context to draw knowledge and generate more accurate responses. The benefit of retrieval augmentation is that it is easy to set up and doesn’t require retraining the model. 

It is also suitable when you’re faced with applications where the context is dynamic and the AI model must tailor its responses to each user based on their data. For example, ahealthcare assistant must personalize its responses based on each user’s health record. 

The tradeoff of retrieval augmentation is that it makes prompts longer and increases the costs of inference.

There is also a hybrid approach, where you fine-tune your model with new knowledge every once in a while and use retrieval augmentation to provide it up-to-the-minute context to the model. This approach combines the benefits of both fine-tuning and retrieval augmentation and allows you to keep your model up-to-date with the latest knowledge while also adjusting it to each user’s context.

When choosing an approach, it’s important to consider the specific use case and available resources. Fine-tuning is suitable when you have a large amount of high-quality data and the computing resources to train the model. Retrieval augmentation is suitable when you need dynamic context. The hybrid approach is suitable when you have a specialized knowledge base that is very different from the training dataset of the foundation model and you also have dynamic contexts.

The future of personalized generative AI and generative AI models

The potential of personalized generative AI models is vast and exciting. We’re only at the beginning of the revolution that generative AI will usher in.

We are currently seeing the power of LLMs in providing access to knowledge. By leveraging your own data and tailoring these models to your specific domain, you can improve the accuracy and reliability of their output. 

The next step is improving the efficiency of operations. With personalized generative AI, users will be able to tie the output of LLMs to relevant actions that can improve business outcomes. This opens up new possibilities for using LLMs in totally new applications. 

Alan’s Actionable AI platform has been built from the ground up to leverage the full potential of personalized generative AI. From providing fine-tuning and augmented retrieval to adding personalized context, Alan AI enables companies to not only customize LLMs to each application and user, but to also link it to specific actions within their software ecosystem. This will be the main driver of improved operational efficiency in times to come.

As Alan AI Platform continues to advance, the possibilities for personalized generative AI models will only continue to expand to deliver the operations efficiency gains for your business.

Conversational AI has become an increasingly popular technology in recent years. This technology uses machine learning to enable computers to communicate with humans in a natural language. One of the key components of conversational AI is language models, which are used to understand and generate natural language. Among the various types of language models, the large language model (LLM) has become more significant in the development of conversational AI.

In this article, we will explore the role of LLMs in conversational AI and how they are being used to improve the performance of these systems.

What are LLMs?

In recent years, large language models have gained significant traction. These models are designed to understand and generate natural language by processing large amounts of text data. LLMs are based on deep learning techniques, which involve training neural networks on large datasets to learn the statistical patterns of natural language. The goal of LLMs is to be able to generate natural language text that is indistinguishable from that produced by a human.

One of the most well-known LLMs is OpenAI’s GPT-3. This model has 175 billion parameters, making it one of the largest LLMs ever developed. GPT-3 has been used in a variety of applications, including language translation, chatbots, and text generation. The success of GPT-3 has sparked a renewed interest in LLMs, and researchers are now exploring how these models can be used to improve conversational AI.

Role of LLMs in Conversational AI

LLMs are essential for creating conversational systems that can interact with humans in a natural and intuitive way. There are several ways in which LLMs are being used to improve the performance of conversational AI systems.

1. Understanding Natural Language

One of the key challenges in developing conversational AI is understanding natural language. Humans use language in a complex and nuanced way, and it can be difficult for machines to understand the meaning behind what is being said. LLMs are being used to address this challenge by providing a way to model the statistical patterns of natural language.

In particular, LLMs can be used to train natural language understanding (NLU) models that identify the intent behind user input, enabling conversational AI systems to understand what the user is saying and respond appropriately. LLMs are particularly helpful for training NLU models because they can learn from large amounts of text data, which allows them to capture the subtle nuances of natural language.

2. Generating Natural Language

Another key challenge in developing conversational AI is natural language generation (NLG). Machines need to be able to generate responses that are not only grammatically correct but also sound natural and intuitive to the user.

LLMs can be used to train natural language generation (NLG) models that can generate responses to the user’s input. NLG models are essential for creating conversational AI systems that can engage in natural and intuitive conversations with users. LLMs are particularly useful for training NLG models because they can generate high-quality text that is indistinguishable from that produced by a human.

3. Improving Conversational Flow

To create truly natural and intuitive conversations, conversational AI systems need to be able to manage dialogue and maintain context across multiple exchanges with users.
LLMs can also be used to improve the conversational flow of – these systems. Conversational flow refers to the way in which a dialog progresses between a user and a machine. LLMs help model the statistical patterns of natural language and predict the next likely response in a conversation. This lets conversational AI systems respond more quickly and accurately to user input, leading to a more natural and intuitive conversation.

Conclusion

Integration of LLMs into conversational AI platforms like Alan AI has revolutionized the field of natural language processing, enabling machines to understand and generate human language more accurately and effectively. 

As a multimodal AI platform, Alan AI leverages a combination of natural language processing, speech recognition, and non-verbal context to provide a seamless and intuitive conversational experience for users.

By including LLMs in its technology stack, Alan AI can provide a more robust and reliable natural language understanding and generation, resulting in more engaging and personalized conversations. The use of LLMs in conversational AI represents a significant step towards creating more intelligent and responsive machines that can interact with humans more naturally and intuitively.

A good NLP engine is highly crucial for making conversational experiences work because it ensures accurate speech recognition and natural language understanding. Accuracy is highly significant because a voice assistant must be able to correctly interpret the user’s spoken words to respond appropriately.

It ensures a good conversation flow which refers to the sequence of interactions that occur between the user and the computer. NLP engines facilitate the conversation by anticipating the user’s needs and providing relevant information or assistance at the right time. Keeping the user’s context in mind is another important aspect as it helps to understand where the user is in the conversation and what he is trying to accomplish.

What is NLP and how It powers Conversational Experiences?

NLP is a subfield of artificial intelligence that focuses on the interaction between computers and human language. It entails instructing computer systems to comprehend, decipher the natural language, and generate responses.

It is an interdisciplinary field that draws on many different areas of study, including computer science, linguistics, and psychology. It involves developing algorithms and models that can analyze and understand natural language, as well as tools and applications that can be used to process natural language data.

NLP helps in understanding the user’s intent by analyzing the natural language input. This involves identifying the keywords and entities, extracting the meaning, and identifying the user’s intent. It helps in understanding the context of the conversation, which is important in providing a relevant and personalized response. Contextual awareness involves considering the user’s history, previous interactions, and preferences.

Overall, NLP is a critical component in powering conversational experiences and conversational AI, enabling systems to understand, interpret, and generate natural language responses that are relevant, personalized, and engaging.

NLP techniques and approaches

NLP is an umbrella term covering highly intricate processes where each process is entwined with another:

• Natural language understanding (NLU): It is the process of understanding the semantics of a language. It can be used to identify the meaning of words and phrases, extract information from text, and generate meaningful responses.
  
• Natural language analysis (NLA): It is the process of understanding the structure of a language. NLA is used to identify the parts of speech, identify the relationships between words, and extract the meaning of a sentence.
  
• Tokenization: This step in NLP is performed to break down the text into individual words, phrases, or sentences. This process is known as tokenization. Tokenization involves splitting a sentence into words and removing punctuation and other non-essential elements. Tokenization helps to structure the data and makes it easier for machines to process it.
  
• Part-of-Speech Tagging: Once the text has been tokenized, the next step is to assign each word a part-of-speech (POS) tag. POS tagging is the process of categorizing each word in a text into its grammatical category, such as noun, verb, adjective, adverb, or preposition. This is an important step in NLP, as it helps machines to understand the meaning of a sentence based on the roles played by different words.
  
• Parsing: Parsing is the process of analyzing a sentence to determine its grammatical structure. In NLP, parsing involves breaking down a sentence into its constituent parts, such as subject, verb, object, and so on. This helps machines to understand the relationship between different parts of a sentence and the overall meaning of the sentence.
  
• Named Entity Recognition (NER): Named entity recognition (NER) is a technique that involves identifying and classifying named entities in text. Named entities are specific objects, people, places, organizations, or other entities that have a unique name.
  
• Sentiment analysis: It is the process of analyzing text to determine the emotional tone of a sentence or document. Sentiment analysis uses NLP techniques to identify words and phrases that are associated with positive or negative emotions and assign a sentiment score to a piece of text.

Conclusion

The success of these voice assistants/chatbots is directly proportional to the robustness and the accuracy of the NLP engine that is powering the voice assistant.

A good NLP engine is highly crucial for making voice assistants work because it ensures accurate speech recognition and natural language understanding. Accuracy is highly significant because a voice assistant must be able to correctly interpret the user’s spoken words to respond appropriately.

In conclusion, natural language processing technology plays a critical role in the development of conversational experiences as it allows users to communicate with computers using natural language. Conversational experiences offer many benefits, including improved customer engagement, increased efficiency, and reduced costs. However, designing effective conversational experiences requires careful planning and attention to detail. Alan AI does that for your business by following the best practices and addressing challenges such as reduced ambiguity which powers conversations that are engaging, intuitive, and effective.

In the past few months, advances in large language models (LLM) have shown what could be the next big computing paradigm. ChatGPT, the latest LLM from OpenAI, has taken the world by storm, reaching 100 million users in a record time.

Developers, web designers, writers, and people of all kinds of professions are using ChatGPT to generate human-readable text that previously required intense human labor. And now, Microsoft, OpenAI’s main backer, is trialing a version of its Bing search engine that is enhanced by ChatGPT, posing the first real threat to Google’s $283-billion monopoly in the online search market.

Other tech giants are not far behind. Google is taking hasty measures to release Bard, its rival to ChatGPT. Amazon and Meta are running their own experiments with LLMs. And a host of tech startups are using new business models with LLM-powered products.

We’re at a critical juncture in the history of computing, which some experts compare to the huge shifts caused by the internet and mobile. Soon, conversational interfaces will become the norm in every application, and users will become comfortable with—and in fact, expect—conversational agents in websites, mobile apps, kiosks, wearables, etc.

The limits of current AI systems

As much as conversational UX is attractive, it is not as simple as adding an LLM API on top of your application. We’ve seen this in the limited success of the first generation of voice assistants such as Siri and Alexa, which tried to build one solution for all needs.

Just like human-human conversations, the space of possible actions in conversational interfaces is unlimited, which opens room for mistakes. Application developers and product managers need to build trust with their users by making sure that they minimize room for mistakes and exert control over the responses the AI gives to users. 

We’re also seeing how uncontrolled use of conversational AI can damage the user’s experience and the developer’s reputation as LLM products are going through their growing pains. In Google’s Bard demo, the AI produced untruthful facts about the James Webb telescope. Microsoft’s ChatGPT-powered Bing has been caught making egregious mistakes. A reputable news website had to retract and correct several articles that were written by an LLM after they were found to be factually wrong. And numerous similar cases are being discussed on social media and tech blogs every day.

The limits of current LLMs can be boiled down to the following:

• They “hallucinate” and can state wrongful facts with high confidence 
• They become inconsistent in long conversations
• They are hard to integrate with existing applications and only take a textual input prompt as context
• Their knowledge is limited to their training data and updating them is slow and expensive
• They can’t interact with external data sources
• They don’t have analytics tools to measure and enhance user experience

Multimodal conversational UX

We believe that multimodal conversational AI is the way to overcome these limits and bring trust and control to everyday applications. As the name implies, multi-modal conversational AI brings together voice, text, and touch-type interactions with several sources of information, including knowledge bases, GUI interactions, user context, and company business rules and workflows. 

This multi-modal approach makes sure the AI system has a more complete user context and can make more precise and explainable decisions.

Users can trust the AI because they can see exactly how and why the AI decided and what data points were involved in the decision-making. For example, in a healthcare application, users can make sure the AI is making inferences based on their health data and not just on its own training corpus. In aviation maintenance and repair, technicians using multi-modal conversational AI can trace back suggestions and results to specific parts, workflows, and maintenance rules. 

Developers can control the AI and make sure the underlying LLM (or other machine learning models) remains reliable and factful by integrating the enterprise knowledge corpus and data records into the training and inference processes. The AI can be integrated into the broader business rules to make sure it remains within the boundaries of decision constraints.

Multi-modality means that the AI will surface information to the user not only through text and voice but also through other means such as visual cues.

The most advanced multimodal conversational AI platform

Alan AI was developed from the ground up with the vision of serving the enterprise sector. We have designed our platform to use LLMs as well as other necessary components to serve applications in all kinds of domains, including industrial, healthcare, transportation, and more. Today, thousands of developers are using the Alan AI Platform to create conversational user experiences ranging from customer support to smart assistants on field operations in oil & gas, aviation maintenance, etc.

Alan AI is platform agnostic and supports deep integration with your application on different operating systems. It can be incorporated into your application’s interface and tie in your business logic and workflows.

Alan AI Platform provides rich analytics tools that can help you better understand the user experience and discover new ways to improve your application and create value for your users. Along with the easy-to-integrate SDK, Alan AI Platform makes sure that you can iterate much faster than the traditional application lifecycle.

As an added advantage, the Alan AI Platform has been designed with enterprise technical and security needs in mind. You have full control of your hosting environment and generated responses to build trust with your users.

Multimodal conversational UX will break the limits of existing paradigms and is the future of mobile, web, kiosks, etc. We want to make sure developers have a robust AI platform to provide this experience to their users with accuracy, trust, and control of the UX. 

Looking for implementing a virtual assistant and considering alternatives for Nuance Mix? Find out how your business can benefit from the capabilities of Alan AI.

Choosing a conversational AI platform for your business is a big decision. With many factors in different categories to evaluate – efficiency, flexibility, ease-of-use, the pricing model – you need to keep the big picture in view.

With so many competitors out there, some companies still clearly aim only for big players like Nuance Mix. Nuance Mix is indeed a comprehensive platform to design chatbots and IVR agents – but before making a final purchasing decision, it makes sense to ensure the platform is tailored to your business, customers and specific demands. 

The list of reasons to look at conversational AI competitors may be endless:

• Ease of customization 
• Integration and deployment options
• Niche-specific features or missing product capabilities  
• More flexible and affordable pricing models and so on

User Experience

Customer experience is undoubtedly at the top of any business’s priority list. Most conversational AI platforms, including Nuance Mix, offer virtual assistants with an interface that is detached from the application’s UI. But Alan AI takes a fundamentally different approach.

By default, human interactions are multimodal: in daily life, 80% of the time, we communicate through visuals, and the rest is verbal. Alan AI empowers this kind of interaction for application users. It enables in-app assistants to deliver a more intuitive and natural multimodal user experience. Multimodal experiences blend voice and graphical interfaces, so whenever users interact with the application through the voice channel, the in-app assistant’s responses are synchronized with the visuals your app has to offer.

Designed with a focus on the application, its structure and workflows, in-app assistants are more powerful than standalone chatbots. They are nested within and created for the specific aim, so they can easily lead users through their journeys, provide shortcuts to success and answer any questions.

Language Understanding

Technology is the cornerstone of conversational AI, so let’s look at what is going on under the hood.

In the conversational AI world, there are different assistant types. First are template-driven assistants that use a rigid tree-like conversational flow to resolve users’ queries – the type of assistants offered by Nuance Mix. Although they can be a great fit for straightforward tasks and simple queries, there are a number of drawbacks to be weighted. Template-driven assistants disregard the application context, the conversational style may sound robotic and the user experience may lack personalization.

Alan AI enables contextual conversations with assistants of a different type – AI-powered ones.
The Alan AI Platform provides developers with complete flexibility in building conversational flows with JavaScript programming and machine learning. 

To gain unparalleled accuracy in the users’ speech recognition and language understanding, Alan AI leverages its patented contextual Spoken Language Understanding (SLU) technology that relies on the data model and application’s non-verbal context. Owing to use of the non-verbal context, Alan AI in-app assistants are provided with awareness of what is going on in any situation and on any screen and can make dialogs dynamic, personalized and human-like.

Deployment Experience

In the deployment experience area, Alan AI is in the lead with over 45K developer signups and a total of 8.5K GitHub stars. The very first version of an in-app assistant can be designed and launched in a few days. 

The scope of supported platforms, if compared to the Nuance conversational platform,  is remarkable. Alan AI provides support for web frameworks (React, Angular, Vue, JS, Ember and Electron), iOS apps built with Swift and Obj-C, Android apps built with Kotlin and Java, and cross-platform solutions: Flutter, Ionic, React Native and Apache Cordova.

Understanding the challenges of the in-app assistant development process, Alan AI lightens the burden of releasing the brand-new voice functionality with:

• Conversational dialog script versioning
• Ability to publish dialog versions to different environments
• Integration with GitHub
• Support for gradual in-app assistant rollout with Alan’s cohorts
  

Pricing

While a balance between benefit and cost is what most businesses are looking for, the price also needs to be considered. Here, Alan AI has an advantage over Nuance Mix, offering multiple pricing options, with free plans for developers and flexible schemes for the enterprise.

Discover the conversational AI platform for your business at alan.app.