Generative AI Report – 3/1/2024

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Welcome to the Generative AI Report round-up feature here on insideAI News with a special focus on all the new applications and integrations tied to generative AI technologies. We’ve been receiving so many cool news items relating to applications and deployments centered on large language models (LLMs), we thought it would be a timely service for readers to start a new channel along these lines. The combination of a LLM, fine tuned on proprietary data equals an AI application, and this is what these innovative companies are creating. The field of AI is accelerating at such fast rate, we want to help our loyal global audience keep pace. Click HERE to check out previous “Generative AI Report” round-ups.

AI21 Unveils Summarize Conversation with Cutting-Edge Task-Specific AI Model, Tailored to Organizational Data

AI21, a leader in AI for enterprises, launched the next-generation of Summarize Conversation, using a new Task-Specific Model to save time and produce faster and more accurate outputs, as well as removing the need for customers to train the model.

The Summarize Conversation solution harnesses generative AI to transform decision-making by seamlessly summarizing conversations like transcripts, meeting notes, and chats, saving significant time and resources. This new feature can be used to summarize items including support calls for customer service agents, earnings calls and market reports for analysts, podcasts, and legal proceedings across industries including insurance, banking and finance, healthcare, and retail.  

AI21’s Task-Specific Models (TSMs) go a step beyond traditional Large Language Models (LLMs) as TSMs are smaller, specialized models that are trained specifically on the most common enterprise use cases, like summarization, and offer increased reliability, safety, and accuracy. AI21’s Retrieval-Augmented Generation (RAG) Engine ensures output is grounded in the correct organizational context. TSMs reduce hallucinations common with traditional LLMs, thanks to AI21’s built-in verification mechanisms and guardrails, and are more efficient than competitors because of their smaller memory footprint.  

“As organizations aim for greater efficiency and accuracy, the Summarize Conversation Task-Specific Model represents a leap forward in the power of generative AI that can deliver immediate value. By providing grounded responses and concise summaries based on an organization’s own data, we are empowering teams to make better and more informed decisions, without the need for extensive training or prompt engineering,” said Ori Goshen, co-CEO and co-founder of AI21.

Exabeam Introduces Transformative Unified Workbench for Security Analysts with Generative AI Assistance

Exabeam, a global cybersecurity leader that delivers AI-driven security operations, announced two pioneering cybersecurity features, Threat Center and Exabeam Copilot, to its market leading AI-driven Exabeam Security Operations Platform. A first-to-market combination, Threat Center is a unified workbench for threat detection, investigation, and response (TDIR) that simplifies and centralizes security analyst workflows, while Exabeam Copilot uses generative AI to help analysts quickly understand active threats and offers best practices for rapid response. These leading-edge innovations greatly reduce learning curves for security analysts and accelerate their productivity in the SOC.

“We built Threat Center with Exabeam Copilot to give security analysts a simple, central interface to execute their most critical TDIR functions, automate routine tasks, and supercharge investigations for analysts at any skill level,” said Steve Wilson, Chief Product Officer, Exabeam. “These new features amp up the value of our AI-driven security operations platform and take analyst productivity, efficiency, and effectiveness to new heights. Threat Center helps security analysts overcome one of the biggest challenges we’ve heard from them — having to deal with too many fragmented interfaces in their environments. By combining Threat Center with Exabeam Copilot we not only improve security analyst workflows, we also lighten their workload.”

Rossum Aurora AI accelerates document automation with human-level accuracy and unprecedented speed

Rossum, a leader in intelligent document processing, is thrilled to unveil Rossum Aurora: a next-generation AI engine poised to revolutionize document understanding and streamline automation from start to finish. Rossum envisions a future where one person can effortlessly process one million transactions annually – getting one step closer with Rossum Aurora by overcoming the hurdle of achieving high accuracy quickly, no matter the document format.

What makes Rossum Aurora stand out is its focus on transactional documents, such as invoices, packing lists, or sales orders. Unlike generic AI models, this next-generation AI engine is tailored for speed and precision, ensuring no time is wasted on chatting with your document, or in dealing with hallucinated data.

At the core of Rossum Aurora is a proprietary Large Language Model (LLM) created specifically for transactional documents. This model is trained on one of the largest datasets in the industry, containing millions of documents with detailed annotations. Through its three levels of training, Rossum Aurora achieves human-level accuracy almost instantly, while being designed to provide enterprise-grade safety.

“In 2017, we revolutionized the IDP market by introducing the first template-free platform. Today, we’re primed to replicate this success with the launch of our specialized Transactional Large Language Model,” commented Tomas Gogar, CEO at Rossum. “After two years of meticulous development, it’s ready to elevate learning speed, accuracy, and automation to unprecedented levels across the Rossum client base. We expect the industry to broadly adopt this approach in the coming year.”


Cohesity, a leader in AI-powered data security and management, announced the general availability of Cohesity Gaia. This first AI-powered enterprise search assistant that brings retrieval augmented generation* (RAG) AI and large language models (LLMs) to high-quality backup data within Cohesity. The conversational AI assistant enables users to ask questions and receive answers based on their enterprise data. When coupled with the Cohesity Data Cloud, these AI advancements transform data into knowledge and can help accelerate the goals of an organization, while keeping the data secure and compliant. Cohesity has agreements with the three largest public cloud providers to integrate Cohesity Gaia.

“Enterprises are keen to harness the power of generative AI but have faced several challenges when building these solutions from scratch. Cohesity Gaia dramatically simplifies this process,” said Sanjay Poonen, CEO and President, Cohesity. “With our solution, leveraging generative AI to query your data is virtually seamless. Data is consolidated, deduplicated, with historical views, and safely accessible with modern security controls. This approach delivers rapid insightful results without the drawbacks of more manual and risky approaches. It turns data into knowledge in minutes.”

TaskUs Elevates the Customer Experience With the Launch of AssistAI, Powered by TaskGPT

TaskUs, Inc. (Nasdaq: TASK), a leading provider of outsourced digital services and next-generation customer experience to the world’s most innovative companies, announced AssistAI, a new knowledge-based assistant built on the TaskGPT platform. Custom-trained on client knowledge bases, training materials, and historical customer interactions, AssistAI uses the information to provide accurate and personalized responses to teammate queries, saving them time to focus on more complex tasks and improving overall efficiency.

“We are still at the early stages of the GenAI revolution,” said Bryce Maddock, Co-Founder and CEO of TaskUs. “Businesses are asking us how GenAI can positively impact their operations. By building and integrating safe, proprietary AI like AssistAI that incorporates the human touch, TaskUs is helping answer this question, enabling customer service teams to deliver better interactions more efficiently.”

Ontotext Enhances the Performance of LLMs and Downstream Analytics with Latest Version of Ontotext Metadata Studio

Ontotext, a leading global provider of enterprise knowledge graph (EKG) technology and semantic database engines, announced the immediate availability of Ontotext Metadata Studio (OMDS) 3.7, an all-in-one environment that facilitates the creation, evaluation, and quality improvement of text analytics services. This latest release provides out-of-the-box, rapid natural language processing (NLP) prototyping and development so organizations can iteratively create a text analytics service that best serves their domain knowledge. 

As part of Ontotext’s AI-in-Action initiative, which helps data scientists and engineers benefit from the AI capabilities of its products, the latest version enables users to tag content with Common English Entity Linking (CEEL), the next generation class-leading text analytics service and recognize roughly 40 million Wikidata concepts. CEEL is trained to tag mentions of people, organizations, and locations to their representation in Wikidata – the biggest global public knowledge graph that includes close to 100 million entity instances. With OMDS, organizations can recognize approximately 40 million Wikidata concepts, and  streamline information extraction from text and enrichment of databases and knowledge graphs. 

“While large language models (LLMs) are good for extracting specific types of company-related events from news sources, they cannot disambiguate the names to specific concepts in a graph or records in a database,” said Atanas Kiryakov, CEO of Ontotext. “Ontotext Metadata Studio addresses this by enabling organizations to utilize state of the art information extraction so they can make their own content discoverable through the world’s biggest public knowledge graph dataset.”

Tabnine Launches New Capabilities to Personalize AI Coding Assistant to Any Development Team

Tabnine, the creators of the AI-powered coding assistant for developers, announced new product capabilities that enable organizations to get more accurate and personalized recommendations based on their specific code and engineering patterns. Engineering teams can now increase Tabnine’s contextual awareness and quality of output by exposing it to their organization’s environment — both their local development environments and their entire code base — to receive code completions, code explanations, and documentation that are tailored to them.

Engineering teams face mounting challenges amidst ever-growing demands for new applications and features and continuing resource constraints on budgets and available hires. AI coding assistants offer a possible solution by boosting developer productivity and efficiency, yet the full potential of generative AI in software development relies upon further improving the relevance of their output for specific teams. The large language models (LLMs) that each AI coding assistant uses have been trained on vast amounts of data and contain billions of parameters, making them excellent at providing useful answers on a variety of topics. However, by exposing generative AI to the specific code and distinctive patterns of an organization, Tabnine is able to tailor recommendations around each development team, dramatically improving the quality of recommendations.

“Despite extensive training data, most AI coding assistants on the market today lack organization-specific context and domain knowledge, resulting in good but generic recommendations,” said Eran Yahav, co-founder and CTO of Tabnine. “Just as you need context to intelligently answer questions in real life, coding assistants also need context to intelligently answer questions. This is the driving force behind Tabnine’s new personalization capabilities, with contextual awareness to augment LLMs by providing all the subtle nuances that make each developer and organization unique.”

The Future of Business Unlocked with GoDaddy Airo

For small businesses, every second saved and every dollar spent is the difference between surviving and thriving. GoDaddy recently found that, on average, small business owners expect to save more than $4,000 and 300 hours of work this year using generative AI. But they don’t always know where to start, and only 26% reported using AI for their business. To make using generative AI fast and easy, GoDaddy launched GoDaddy Airo™, an AI-powered solution that saves small business owners precious time in establishing their online presence and helps them win new customers.

“Generative AI is the great equalizer for small businesses,” said GoDaddy President, US Independents, Gourav Pani. “Technology and capabilities usually reserved for large companies with thousands of employees are now at the fingertips of anyone looking to start or grow their business.  GoDaddy Airo™ combines the latest AI technology with the ease of use we’re known for – providing effortless and intuitive solutions to small businesses.”

Vectara Introduces Game-Changing GenAI Chat Module, Turbocharging Conversational AI Development

Vectara, the trusted Generative AI Platform and LLM Builder, released its latest module, Vectara Chat, designed to empower companies to build advanced chatbot systems with the GenAI platform. With 80% of enterprises forecasted to have GenAI-enabled applications by 2026, Vectara Chat offers developers, product managers, and startups a powerful toolset for creating chatbots effortlessly.

Vectara Chat is an end-to-end solution for businesses constructing their own chatbot using domain-specific data, minimizing biases from open-source training data. Unlike existing offerings that require users to navigate multiple platforms and services, Vectara Chat provides a seamless experience without compromising efficiency and control by offering transparency and insight into the origin of summaries.

“The core functionalities of Vectara Chat, including the ability to reference message history, develop a UI chat widget framework, and view user trends, showcase our commitment to providing a comprehensive toolkit for developers and builders,” says Shane Connelly, Head of Product at Vectara. “Our goal is to ensure that chatbot development is user-friendly and efficient, catering to a diverse range of conversational AI use-cases.”

Pulumi Launches New Infrastructure Libraries for the GenAI Stack

Generative AI (GenAI) is a transformative technology and it’s having an immediate impact on software development teams, particularly those managing cloud infrastructure. As GenAI quickly evolves, there are a variety of technology advancements impacting the tools available to developers to build and manage AI applications. 

Pulumi is at the forefront of these movements, partnering with companies like Pinecone and Langchain, among others, to make important GenAI capabilities native for Pulumi users.  

Just recently announced and being fully revealed for the first time this week, Pulumi now offers native ways to manage Pinecone indexes, including its latest serverless indexes. Pinecone is a serverless vector database with an easy-to-use API that allows developers to build and deploy high-performance AI applications. This is incredibly important as applications involving large language models, generative AI, and semantic search require a vector database to store and retrieve vector embeddings

Pulumi also now has a template to launch and run Langchain’s LangServe in Amazon ECS, a container management service. This in addition to Pulumi’s existing support in running Next.js frontend applications in Vercel, managing Apache Spark clusters in Databricks and 150+ other cloud and SaaS services. 

The GenAI tech stack is new and emerging but has typically consisted of a LLM service and a vector data store. Running this stack on a laptop is fairly simple but getting it to production is far harder. Most of this is done manually through a CLI or a web console, which introduces manual errors and repeatability problems that affect the security and reliability of the product. 

Pulumi has made it easy to take a GenAI stack running locally and get it in production in the cloud with Pulumi AI, the fastest way to learn and build Infrastructure as Code (IaC). As GenAI complexity actually relates to cloud infrastructure provisioning and management, Pulumi is purpose built to manage this cloud complexity and is easy to use to support a new use case of AI.
Pulumi is the new abstraction for the GenAI stack. It allows developers to tie together all the different pieces of infrastructure that goes into their GenAI product and manage it from a simple Python program. Pulumi has long been used by top companies to manage large scale cloud architectures. Pulumi provides 10x greater scale and faster time to market for these companies. Now, Pulumi is bringing these gains to the GenAI space.

Optiva Accelerates Competitive Edge With Generative AI-Enabled Real-Time BSS

Optiva Inc. (TSX: OPT), a leader in powering the telecom industry with cloud-native billing, charging and revenue management software on private and public clouds, announced that its BSS platform now allows users to leverage generative AI (GenAI) technology to quickly highlight new, targeted revenue opportunities and dramatically reduce customer churn. Full integration with Google Cloud’s BigQuery and Analytics capabilities powers the deep learning needed to customize offerings and attract and retain customers with tailored service bundles.

“In today’s highly competitive market, it’s vital that CSPs start leveraging the power of generative AI and real-time BSS data to better target their offerings and win customers,” said Matthew Halligan, CTO of Optiva. “This technology is evolving fast, and market players now have a narrow window of opportunity to seize these capabilities and become instrumental in driving the industry forward.”

Copyleaks Introduces New Update to Codeleaks Source Code AI Detector: Advanced Paraphrase Detection at the Function Level

Copyleaks, a leader in plagiarism identification, AI-content detection, and GenAI governance platform, announced a significant update to its solution, Codeleaks Source Code AI Detector. This enhancement introduces the ability to identify paraphrased code at the function level, underscoring Copyleaks’ commitment to advancing its comprehensive AI and machine learning suite of products to safeguard intellectual property across all forms of content.

Unlike traditional source code detectors that primarily search for exact text matches, the latest version of Codeleaks transcends this limitation by analyzing code semantics. This innovative approach allows Codeleaks to recognize potentially paraphrased source code more accurately with detection at the function level, enhancing Codeleaks’ detection capabilities and empowering users to make more informed decisions regarding code originality and integrity.

The necessity for such advanced detection capabilities has become increasingly evident as the use of Generative AI in coding practices grows. With AI-generated source code becoming more common through platforms like ChatGPT and GitHub Copilot, the risk of inadvertent code plagiarism, license infringement, and proprietary code breaches has escalated. Copyleaks’ latest update to Codeleaks addresses these concerns head-on, offering a robust solution to ensure code transparency and originality amidst the evolving software development landscape.

“Amidst the rapid advancement of AI in software development, the challenge of maintaining code originality and compliance has never been more critical,” said Alon Yamin, CEO and Co-founder of Copyleaks. “With this latest update to Codeleaks, we are setting a new standard in code plagiarism detection. Our technology now goes beyond the surface to understand code at a functional level, offering unparalleled transparency and protection for developers and organizations worldwide.”

Securiti AI Unveils AI Security & Governance Solution for Safe and Responsible AI Adoption 

Securiti AI, the pioneer of the Data Command Center, announced the release of its AI Security & Governance offering, providing a groundbreaking solution to enable safe adoption of AI. It uniquely combines comprehensive AI discovery, AI risk ratings, Data+AI mapping and advanced Data+AI security & privacy controls, helping organizations adhere to global standards such as NIST AI RMF and the EU AI Act, among over twenty other regulations.  

There is an unprecedented ground-swell adoption of generative AI across organizations. A significant portion of this adoption is characterized by Shadow AI, lacking systematic governance from the organizations. Given the highly advantageous transformative capabilities of generative AI, organizations should prioritize establishing visibility and safeguards to ensure its safe utilization within their operations, rather than simply shutting it down.   

Built within the foundational Data Command Center, the AI Security & Governance solution acts like a rule book for AI and offers distinct features to help organizations get full visibility on AI use, its associated risks, ability to control the use of AI and use of enterprise data with AI. It also enables organizations to protect against the emerging security threats targeted towards LLMs, some of which are defined by The Open Worldwide Application Security Project (OWASP) for LLMs.  

“Generative AI would enable radical transformation and benefits for organizations who adopt it. Empowering business teams to leverage it swiftly with appropriate AI guardrails is highly desirable,” said Rehan Jalil, CEO of Securiti AI. “The solution is designed for security and AI governance teams to be partners with their business teams in enabling such secure, safe, responsible and compliant AI.”  

GenAI Jumpstart Accelerator Offers Significant Benefits for Businesses in Highly-Regulated Industries

TELUS International (NYSE and TSX: TIXT), a leading digital customer experience (CX) innovator that designs, builds and delivers next-generation solutions, including artificial intelligence (AI) and content moderation, for global and disruptive brands, sees potential in its GenAI Jumpstart accelerator for businesses in highly-regulated industries. With a path-to-production focus, the short eight-week engagement designed for companies at an early stage of their AI journey, rapidly identifies use cases, builds powerful risk mitigation tools and delivers a functional generative AI (GenAI)-powered virtual assistant prototype.

The company’s unique Dual-LLM Safety System, a key feature of its GenAI Jumpstart accelerator, uses a large language model (LLM) to supervise the results of a retrieval augmented generation (RAG) system. A RAG-based system runs on a company’s private and secure database of controlled and secured information rather than the open internet to help ensure that responses generated by a virtual assistant only use approved information that conforms to regulatory frameworks. Unlike traditional chatbots that can struggle with maintaining up-to-date information or accessing domain-specific knowledge, this feature helps keep AI assistants focused to mitigate inaccuracies, hallucinations and jailbreaking – a form of hacking that aims to bypass or trick an AI model’s guidelines and safeguards to misuse or release prohibited information.

“What is holding many companies back from truly unlocking the power of GenAI within their organizations is their lack of or limited in-house resources and expertise to safely and responsibly design and develop AI-powered solutions,” said Tobias Dengel, president of WillowTree, a TELUS International Company. “Working with a trusted partner is especially important within highly-regulated industries like banking where there is an added layer of complexity when integrating GenAI into operations due to the constant need to adapt to new and updated regulatory changes and comply with strict consumer protections due to the sensitive nature of the information being handled.”

Franz’s Gruff 9 Brings LLM Integration and RDF* Semantics to Neuro-Symbolic AI Applications

Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Knowledge Graph technology for Neuro-Symbolic AI applications, announced Gruff 9, a web-based advanced Knowledge Graph visualization tool that offers LLM integration and unique RDF* (RDFStar) features for building next-generation AI applications. 

Gruff 9 provides users the ability to embed natural language LLM queries in SPARQL and visualize and explore the connections displayed in the results. Gruff now provides a unique visualization solution for the emerging RDF* standard from W3C.  The RDF* standard is an improvement over the labeled property graph approach (supported by other vendors) as it allows full hypergraph capabilities.

Gruff 9 is included with AllegroGraph Cloud, Franz’s hosted version of its groundbreaking Neuro-Symbolic AI platform. Gruff and AllegroGraph Cloud offers users a convenient and easy on-ramp to building advanced AI applications.

“The ability to visualize data has become essential to every organization, in every industry,” said Dr. Jans Aasman, CEO of Franz Inc. “Gruff’s dynamic data visualizations enable a broad set of users to determine insights that would otherwise elude them by displaying data in a way that they can see the significance of the information relative to a business problem or solution. Gruff makes it simple to weave these knowledge graph visualizations into new Neuro-Symbolic AI applications – further extending the power of AI in the enterprise.”

Metomic Launches ChatGPT Integration To Help Businesses Take Full Advantage Of The Generative AI Tool Without Putting Sensitive Data At Risk

Metomic, a next generation data security solution for protecting sensitive data in the new era of collaborative SaaS, GenAI and cloud applications, today announced the launch of Metomic for ChatGPT, a cutting-edge technology that gives IT and security leaders full visibility into what sensitive data is being uploaded to OpenAI’s ChatGPT platform. The easy-to-use browser plugin enables businesses to take full advantage of the generative AI solution without jeopardizing their company’s most sensitive data.  

Shortly after OpenAI’s initial ChatGPT launch, the technology set a record for the fastest-growing user base when it gained 100 million monthly active users within the first two months. Its explosive popularity has continued to grow as new iterations of the technology have been made available. Meanwhile, multiple industry studies have revealed employees are inadvertently putting vulnerable company information at risk by uploading sensitive data to OpenAI’s ChatGPT platform. Last year, reports showed that the amount of sensitive data being uploaded to ChatGPT by employees had increased 60% between March and April, with 319 cases identified among 100,000 employees between April 9 and April 15, 2023.  

Because Metomic’s ChatGPT integration sits within the browser itself, it identifies when an employee logs into OpenAI’s web-based ChatGPT platform and scans the data being uploaded in real-time. Security teams can receive alerts if employees are uploading sensitive data, like customer PII, security credentials, and intellectual property. The browser extension comes equipped with 150 pre-built data classifiers to recognize common critical data risks. Businesses can also create customized data classifiers to identify their most vulnerable information.

“Very few technology solutions have had the impact of OpenAI’s ChatGPT platform—it is accelerating workflows, enabling teams to maximize their time, and delivering unparalleled value to the businesses that are able to take full advantage of the solution. But because of the large language models that underpin the generative AI technology, many business leaders are apprehensive to leverage the technology, fearing their most sensitive business data could be exposed,” said Rich Vibert, CEO, Metomic. “We built Metomic on the promise of giving businesses the power of collaborative SaaS and GenAI tools without the data security risks that come with implementing cloud applications. Our ChatGPT integration expands on our foundational value as a data security platform. Businesses gain all the advantages that come with ChatGPT while avoiding serious data vulnerabilities. It’s a major win for everyone—the employees using the technology and the security teams tasked with safeguarding the business.” 

Galileo Introduces RAG & Agent Analytics Solution for Better, Faster AI Development 

Galileo, a leader in developing generative AI for the enterprise, announced the launch of its latest groundbreaking Retrieval Augmented Generation (RAG) & Agent Analytics solution. The offering is meant to help businesses speed development of more explainable and trustworthy AI solutions. 

As retrieval-based methods have fast become the most popular method for creating context-aware Large Language Model (LLM) applications, this innovative solution is designed to dramatically streamline the process of evaluating, experimenting and observing RAG systems. 

“Galileo’s RAG & Agent Analytics is a game-changer for AI practitioners building RAG-based systems who are eager to accelerate development and refine their RAG pipelines,” said Vikram Chatterji, CEO and co-founder of Galileo. “Streamlining the process is essential for AI leaders aiming to reduce costs and minimize hallucinations in AI responses.” 

Conversica Advances GenAI Chat with Release of Brand-Specific Large Language Models

Conversica, Inc, a leading provider of Conversational AI solutions for enterprise revenue teams, announced the addition of new Contextual Response Generation to its Conversica Chat solution, making it the first enterprise chat offering that leverages the best of GPT and Retrieval Augmented Generation (RAG) capabilities for a dynamic, brand-safe web chat experience. The new release delivers accurate, brand-specific conversations while also incorporating safeguards against unintended responses and AI hallucinations. No more inaccurate or outdated information that produces false responses incongruent with the brand—Conversica Chat with Contextual Response Generation “learns” the brand’s information to inform conversations with both prospect and existing customers.

The new Conversica Chat addition is trained solely on customer data, removing the need for fine-tuning public models. This technology leverages RAG capabilities to use customer-specific data, delivering accurate, dynamic chat conversations that intelligently qualify leads through natural human-like conversations. By combining the power of GPT with the precision of the latest in enterprise AI retrieval mechanisms, Conversica Chat technology leads the evolution of Generative AI chat solutions for larger enterprise use, to help organizations meet evolving consumer demands and distinguish brand experiences from their competitors with personalized, one-on-one experiences.

In the rapidly evolving landscape of Large Language Models (LLMs), Conversica’s retrieval of brand-specific information in responses makes Generative AI accurate, driving value to highly regulated and/or brand-sensitive organizations looking to leverage the technology for external-facing use cases, but with the required level of control that the most regulated companies can use. Rather than relying solely on generic datasets from public LLMs that are, at best, fine-tuned with customer data, Conversica deploys a combination of AI technologies to deliver client-specific LLMs, providing accurate and dynamically generated interactions that are domain-specific and brand safe.

“Conversica continues to make significant advancements specifically for the larger enterprises that prioritize accurate brand representation and powerfully human exchanges at scale with human-like, dynamic, and contextually aware AI-driven conversations,” said Jim Kaskade, CEO of Conversica. “Chat solutions have become ubiquitous in today’s digital landscape. Yet, for Fortune 100 enterprises, leveraging generative AI-powered chat technologies has remained a complex challenge—until now. Conversica Chat offers a client-specific, single-tenant solution that delivers regulatory compliant and seamless GPT experiences resulting in brand-safe conversations for enterprise revenue teams. We are ready to redefine how enterprises engage with AI-powered chat, setting a new standard for excellence in the most demanding environments.”

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