GroupBy Inc., a leading provider of AI-first Search and Product Discovery solutions for B2C and B2B retail, today unveiled Enrich AI, a cutting-edge product data enrichment platform designed to revolutionize product information management. Leveraging sophisticated generative AI algorithms and GroupBy’s years of deep retail expertise, Enrich AI empowers retailers to create rich shopping experiences with enhanced product data – driving substantial revenue growth. “Enrich AI represents a quantum leap in data enrichment and product data management,” stated Roland Gossage, CEO at GroupBy. “By seamlessly integrating advanced AI with our deep domain expertise in retail, we are delivering a solution that addresses the critical challenges retailers face in managing and optimizing their product data. This sophisticated self-serve platform enables B2C and B2B retailers to achieve unprecedented levels of data accuracy, consistency, and completeness, ultimately optimizing search relevance, merchandising effectiveness, and overall business performance.”

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“Enrich AI represents a quantum leap in data enrichment and product data management”

Enrich AI harnesses the power of AI to revolutionize product data management. By automating complex data enrichment tasks, Enrich AI accurately extracts, standardizes, and enriches product data based on attributes, descriptions, and images at scale. This streamlined process minimizes costly errors, accelerates time-to-market, and frees up valuable resources for strategic initiatives, while making it easier for retailers to manage their catalogs.

Moreover, Enrich AI leverages GroupBy’s extensive proprietary Global Taxonomy Library to ensure consistent and accurate product categorization. By applying a robust, industry-specific taxonomy, developed by GroupBy’s breadth and years of experience working with various retail verticals, retailers can enhance product discoverability, improve search relevance, and optimize merchandising efforts. This granular level of product classification empowers retailers to deliver highly targeted attributes and values for product recommendations and personalized shopping experiences.

To further enhance data reliability and integrity, Enrich AI employs advanced algorithms to detect and correct errors, inconsistencies, and missing information within product data. By improving data quality, retailers can reduce costly returns, enhance customer satisfaction, and build trust in their brand.

Ultimately, by optimizing product discoverability and driving business growth, Enrich AI enables retailers to improve the quality of their data, personalize customer experiences, and increase customer satisfaction. With enriched product data, retailers can unlock new revenue streams, gain a competitive advantage, and thrive in today’s dynamic retail landscape.

Also Read: AiThority Interview with Seema Verma, EVP and GM, Oracle Health and Life Sciences

Key technical features of Enrich AI include:

• Advanced AI Algorithms: Utilize state-of-the-art generative AI models for accurate and efficient data enrichment.
• Product Attribute Intelligence: Fast and efficient attribute enrichment from product image and text data in a single step.
• Deep Retail Expertise: Built on a foundation of industry knowledge and best practices across retail verticals.
• Taxonomy Enrichment: Manage taxonomies tailored to specific product categories based on business needs.
• Product Catalog Management: Automated management of catalogs to maintain enrichment for products that churn.
• Robust PIM/PXM Integration: Seamlessly integrate with existing product information management systems.
• Quality Assurance: Automated data quality checks ensure data accuracy and consistency through continuous monitoring and validation.
• Product Transformations: Involve humans in the loop to quickly curate and edit product values across the catalog.
• User-Friendly Interface: Empower users with intuitive tools for easy data management and enrichment.
• Simplified Integration: Seamlessly access Enrich AI functionality directly from the GroupBy Command Center, eliminating the need for manual data injection and streamlining the enrichment process.
• Expert support: Access professional services for seamless catalog implementation and data enrichment aligned with the proprietary Global Taxonomy Library.

By combining AI-powered precision with GroupBy’s deep retail expertise, Enrich AI delivers unparalleled product data enrichment capabilities. Retailers can now unlock the full potential of their product data, gain a competitive edge, and drive long-term business success.

Also Read: AiThority Interview with Yair Amsterdam, CEO of Verbit 

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Foundry Cloud Platform democratizes AI development and solves the GPU shortage with the first real-time compute market purpose-built for AI 

Foundry—an emerging cloud provider founded by alumni from Google DeepMind’s core Deep Learning team–launched Foundry Cloud Platform, a real-time market and orchestration engine for GPU compute that simplifies access to the infrastructure required to build and deploy AI. Foundry’s platform reduces operational complexity and improves compute cost efficiency by up to 6x, putting AI development within reach of more organizations and accelerating global AI innovation.

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The AI boom has made GPU servers one of the world’s most strategic commodities. Surging demand has outpaced the capacity of traditional public clouds, prompting tech giants and AI startups alike to spend billions to independently secure essential hardware for AI development. Industry-standard long-term contracts and unreliable infrastructure fuel overprovisioning to guarantee capacity and redundancy, further reducing broader access. This arms race for GPU compute ownership has masked a critical issue and opportunity: existing GPUs are vastly underutilized due to the unique compute requirements of AI development.

“The GPU compute market as it exists today is one of the most inefficient commodity markets in history, and it’s directly limiting critical AI innovations that will benefit society,” explains Jared Quincy Davis, founder and CEO of Foundry. “The majority of AI research and development teams struggle to access affordable and reliable compute for their workloads, while exceptionally well-funded organizations are forced to purchase long-term GPU reservations that they rarely utilize to maximum capacity. Foundry Cloud Platform addresses this market failure by aggregating and redistributing idle compute capacity to enable faster breakthroughs while improving return on GPU investments.”

Foundry Cloud Platform offers AI teams of every scale a more efficient and approachable way to access GPU compute, optimizing performance, cost efficiency, and reliability.

Foundry Cloud Platform aggregates compute into a single, dynamically-priced pool that offers GPU capacity in two ways, optimized for the specific and unique needs of different AI workloads:

• Resellable reserved instances. AI teams have self-serve access to reserve short-term capacity from Foundry’s pool of GPU virtual machines. Rather than pay for fixed, long-term contracts, customers can guarantee compute for predictable workloads by reserving interconnected clusters from the pool for as little as three hours. Customers can further increase cost-efficiency by reselling any idle capacity from their reservations. For example, if a customer reserves 128 NVIDIA H100s and sets aside 16 as “healing buffer” nodes, they can temporarily relist those 16 nodes on the market, where they generate credits until the customer recalls them or the initial reservation period ends. Reserved usage is optimal for pre-planned workloads like training runs and critical day-to-day developer tasks like verification and debugging.
• Spot instances. All unreserved and relisted compute on the platform is available as spot instances that users can bid on for interrupt-tolerant workloads like model inference, hyperparameter tuning, and fine-tuning.

Foundry Cloud Platform uses auction theory to set market-driven prices for reserved and spot compute based on real-time supply and demand. Whenever prices become too high, Foundry increases the overall GPU capacity of the platform, stabilizing the market.

The platform also offers Kubernetes workload orchestration, which eliminates manual scheduling by programmatically adding reserved and spot instances to a managed Kubernetes cluster. Leveraging Kubernetes clusters through Foundry Cloud Platform allows AI development teams to optimize price-performance and minimize inference latency during traffic spikes by quickly scaling capacity horizontally.

Infinite Monkey, an AI startup developing architectures for AGI, uses Foundry Cloud Platform to access a variety of state-of-the-art GPUs without overprovisioning. “With Foundry Cloud Platform, we made  actionable discoveries in hours, not weeks,” says Matt Wheeler, Research Engineer at Infinite Monkey. “When we believe we could benefit from additional compute, we just turn it on. When we need to pause to study our results and design the next experiment, we turn it off. Because we aren’t locked into a long-term contract, we have the flexibility to experiment with a variety of GPUs and empirically determine how to get the b*********-performance for our workload.”

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“Foundry Cloud Platform has accelerated science at Arc,” notes Patrick Hsu, Co-Founder and Core Investigator at Arc Institute –  a nonprofit research organization studying complex diseases, including cancer, neurodegeneration, and immune dysfunction. “Our machine learning work brings demanding performance infrastructure needs, and Foundry delivers. With Foundry, we can guarantee that our researchers have exactly the compute they need, when they need it, without procurement friction.”

Foundry is building its platform to meet the highest standards for security and compliance to ensure maximum protection for customer data, achieving SOC 2 Type II certification earlier this year.

Also Read: AiThority Interview with Yair Amsterdam, CEO of Verbit 

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AiAssistWorks unveils a cutting-edge AI add-on for Google Sheets, offering seamless and complete AI integration for users of all skills levels.

AiAssistWorks has launched a powerful AI Google Sheets add-on, crafted to seamlessly integrate cutting-edge AI technology into Google Sheets for streamlined filling tasks and comprehensive data analysis, enabling both novice and expert users to enhance their productivity.

Also Read: AiThority Interview with Seema Verma, EVP and GM, Oracle Health and Life Sciences

In recent years, the proliferation of AI tools has reshaped various aspects of daily life and work, with companies, from startups to major corporations, developing their own AI models to optimize operations and decision-making. AiAssistWorks capitalizes on this trend by embedding a versatile AI solution within Google Sheets, one of the most widely used tools for data analysis and management.

Unlike typical AI integrations requiring complex setups and specialized knowledge, AiAssistWorks offers a unique approach that eliminates the need for writing custom formulas like “=GPT.” Users can interact with the add-on through a straightforward, user-friendly interface, allowing them to save and reuse AI prompts without the hassle of complex formula configurations. This feature not only simplifies the user experience but also enhances productivity by reducing the time and effort spent on repetitive data tasks.

The AiAssistWorks add-on supports connections to numerous AI providers, including industry leaders such as OpenAI, Anthropic, and Gemini, using individual API keys. This flexibility enables users to employ a wide range of AI models, making it possible to leverage nearly all available large language models (LLMs) directly within their spreadsheets. This level of integration is unprecedented in the realm of Google Sheets AI tools.

Also Read: AiThority Interview with Kunal Purohit, President – Next Gen Services, Tech Mahindra

“Our product stands out because it democratizes access to advanced AI functionalities, allowing users to enhance their Google Sheets experience without the need for extensive technical knowledge or financial investment,” explained a spokesperson for AiAssistWorks. “By making our tool affordable and easy to use, we are opening up a world of possibilities for utilizing advanced AI models in daily life that were previously only available to specialists.”

As businesses continue to embrace AI technologies to gain competitive advantages, tools like AiAssistWorks are becoming essential for anyone looking to incorporate AI into their daily workflows efficiently and affordably. This add-on is not just a tool; it’s a bridge to the future of work, where AI and human ingenuity combine to create unprecedented opportunities for innovation and efficiency.

Also Read: AiThority Interview with Yair Amsterdam, CEO of Verbit 

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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The integration of artificial intelligence (AI) in wound care shows promise of transforming the field, enabling more precise diagnostics, personalized treatment plans, and significantly improved healing outcomes. Providers face many challenges, including time constraints, regulatory demands, and electronic health record (EHR) input. Though progress has been made, information sharing across colleagues and departments has continued to be a barrier to optimal care delivery, and without the correct data available, informed clinical decisions cannot be made. Considering these factors, the wound care industry requires technological advancements to help alleviate common hurdles and enhance care delivery and the patient experience. AI has the opportunity to play a role in this movement, but wound care is experiencing parallel adoption challenges with other healthcare sectors as technology progresses. Grandview Research estimates that the global wound care market will increase at a compound annual growth rate of 4.17% between 2024 and 2030, justifying the need to expand technology capabilities in this sector. Additionally, the aging population is causing an increase in patients with chronic health issues, further driving the need for more advanced care methods.

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Effective wound care requires careful attention and an in-depth understanding of the numerous factors that can influence healing. By incorporating AI in wound care technology to expand knowledge bases and gather insights, providers may be able to better identify needs to address challenges and provide the best treatment options. The use of AI could enable providers to minimize guesswork and deliver more targeted treatments to increase recovery success. Advancements in wound care technology can transform care delivery and streamline clinical operations, creating a new standard for care in the industry.

Expanding wound care treatment options

As an industry that has lacked robust options for treatment, especially for more complex wounds, ensuring accuracy and personalization for individual patients is a crucial step in implementing modern technology. From the start, an accurate diagnosis can make or break the subsequent steps and outcomes of a patient’s treatment plan. AI shows potential to play an influential role in wound care, from measurement and analysis to assisted clinical decision-making for treatment plans. For example, AI algorithms could reference previous patients’ data to identify wound types and stages, increasing accuracy and improving treatment planning. Wound measurement is another essential component of this identification process. Conventional ruler-based measurement practices have a  44% error rate, but AI imaging and measurement can significantly reduce this statistic by measuring a wound accurately through automated digital planimetry. With more accurate measurements, courses of action and outcomes may look drastically different.

More personalized treatment plans are another potential benefit of AI integration in wound care. Utilizing AI to analyze an individual patient’s electronic health record data, providers could take more tailored steps to ensure the best chances of recovery. They may also be able to reference a wider range of data to determine the next best course of action, such as dressings and medications to prescribe as the wound heals.

AI may also be able to offer enhanced monitoring and feedback on treatment plans and progress. With the availability of wound monitoring applications and devices, real-time data collection and analysis can enable ongoing treatment adjustments. In the U.S., approximately 2.5 million individuals develop a pressure injury in acute care facilities every year. AI integration could help reduce the number and severity of hospital-acquired pressure injuries (HAPIs) and prevent needless amputations due to diabetic ulcers by assisting with monitoring, especially in understaffed facilities. The average HAPI costs a hospital up to  $70,000 per case, and HAPIs are the second most common hospital lawsuit claim after wrongful death. For diabetic ulcers, with the rate of diabetes increasing, especially among younger and disadvantaged populations, the rate of amputations has increased over the past several years, further representing the demand for AI’s assistance in addressing chronic wound conditions.

Also Read: Extreme Networks and Intel Join Forces to Drive AI-Centric Product Innovation

Improved quality of life and long-term outcomes

Positive outcomes are always the goal at the end of a treatment plan. A patient’s quality of life in both the short and long term could be improved with the use of AI through reduced healing time, minimized complications, and improved patient engagement and compliance. AI-driven care can potentially accelerate healing by using accurate insights to inform care decisions, but more research needs to be done to validate the impact of AI in improving clinical outcomes. Predictive analytics may help spot potential problems early on and help avoid infections or other wound-related issues. For chronic wounds that take more time to heal, AI’s ongoing role in the care plan and process could be pivotal to improving patient outcomes.

AI and its accompanying tools may also drastically increase patient engagement and compliance. With solutions now available to support patient education and engagement, individuals can be more involved in their care journeys with expanded knowledge of their condition and treatment plan. Patient engagement is increasingly recognized as an integral part of health care and a critical component of safe, people-centered services. Informed patients are more likely to feel confident enough to report positive and negative experiences and have increased compliance with established care management plans.

Also Read: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

 Enhancing clinical workflow

Aside from patient-centric benefits, provider organizations can also benefit from implementing AI solutions into their practices. A more streamlined workflow is a primary benefit of integrating AI into practices because it can automate routine tasks. In November 2023, 56% of nurses reported burnout, so finding ways to reduce tedious administrative tasks and allow clinicians to focus on care delivery and patient engagement can help better maintain or even improve the health care workforce. Clinician time is valuable, so allocating resources correctly is essential to a provider organization’s success.

AI has the potential to be a catalyst for the transformation of wound care and beyond. Showing promise of enabling more informed care delivery with transparency and efficiency will positively impact wound care treatment, patient outcomes, and clinical processes that can propel this sector into the future. While hesitancy remains around the adoption of AI in health care, the positives almost certainly outweigh the risks. Engaging with AI technology will help set clinical practices up for success in care delivery and operations, but most importantly, open new doors for patients in need of treatment.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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With a one-click integration to Conversations, Infobip’s contact center solution, HumanFirst helps enterprise teams leverage LLMs to analyze 100% of their customer data.

HumanFirst, a prompt and data platform, and Infobip, a global cloud communications platform, today announced a collaboration that connects HumanFirst directly to Infobip’s communications suite. The partnership helps enterprise teams leverage large language models (LLMs) to work collaboratively with unstructured data; for small tasks like support call summaries, and complex initiatives like contact center topic modeling.

Companies across the globe have adopted an omnichannel approach to customer engagement. Last year, 473 billion company-to-customer interactions took place on Infobip’s CPaaS platform, representing a 91% increase in conversational messaging across sales, support, marketing, and more.

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Conversational data contains critical business signals but remains inaccessible to most teams; unstructured feedback is messy, and teams lack the tools they need to interact with it at scale. Gartner predicts that by 2028, 80% of enterprises using CPaaS platforms will also employ generative AI-based conversational strategies. But the opportunity for GenAI goes far beyond conversation design.

“We believe large language models (LLMs) will be the primary way customer-facing teams make sense of their data,” says Alex Dubois, SVP Strategic Growth and Partnerships at HumanFirst. “We’re committed to enabling any and every team with the skills they need to do that work.”

Infobip recognized HumanFirst as one of the complementary partners bringing performant genAI strategies to customer-centric teams. HumanFirst has worked with industry leaders like Google, Deloitte, and HomeServe to manage data-driven transformations 90% faster and 80% cheaper, increasing the number of non-technical experts working with data by 7-fold.

“At Infobip, we value innovation and customer-centricity,” shares Ivan Ostojić, Chief Business Officer at Infobip. “HumanFirst helps us with both. We believe companies around the world will be able to show up for their customers in new and powerful ways with Infobip and HumanFirst.”

Working with HumanFirst themselves, Infobip generated 220+ knowledge articles with genAI to address repetitive support requests occupying their agents’ time.

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“Within a week of working with HumanFirst, we found two categories of support tickets comprised 30% of our agents’ workload,” says Ostojić. “We saw a significant cost-savings opportunity, and started building self-service automations based on our real customer-agent conversations. We want to bring that capability to our customers to help them achieve the same results in transforming their customer experience using HumanFirst & Infobip AI Hub.”

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Infobip helps household name companies like Uber, Hubspot, Meta, and Microsoft communicate with their customers. With a low-code LLM playground connected to that data source, customer-facing teams will be able to extract the insights they need to be more effective across support, sales, marketing, and more.

“Infobip is unmatched not only in their vision of bringing AI to market, but also in their ability to act on that vision,” says Dubois. “We’re thrilled to be in partnership.”

HumanFirst is currently available on Infobip Exchange. The integration is free of charge; Infobip customers can activate their HumanFirst account by scheduling a meeting with the HumanFirst team.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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• New senior hires come on the heels of significant momentum for the business, having raised $12 billion from top tier equity and debt investors, announcing a London office and European expansion efforts.

• Chen Goldberg, Senior Vice President of Engineering, hails from Google where she played an instrumental role in building, deploying, and managing the innovative Kubernetes platforms.

• Sachin Jain, Chief Operating Officer, comes from Oracle Cloud where he led AI infrastructure and has spent more than 20 years in senior roles at Google Cloud and Amazon.

• This announcement follows recent additions to the leadership bench with top-tier talent including Chief Accounting Officer Jeff Baker from Rivian; Chief Product Officer Chetan Kapoor from Amazon Web Services; and Chief Financial Officer Nitin Agrawal from Google Cloud.

CoreWeave, the AI Hyperscaler, today announced the appointment of Chen Goldberg as Senior Vice President of Engineering, as well as Sachin Jain as its new Chief Operating Officer.

Goldberg and Jain are technology veterans with long, accomplished histories at the largest technology companies in the world.

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Goldberg has more than 25 years of expertise leading global engineering teams, product R&D initiatives, and high-profile customer engagements with Fortune 500 enterprises. She joins CoreWeave from Google, where she worked across several roles at Google Cloud, most recently as General Manager & VP of Engineering, leading the Kubernetes and Serverless team and product portfolio. She will join the CoreWeave executive team to lead and grow engineering functions, as well as accelerate CoreWeave’s world-class product development and offerings.

Jain most recently served as Senior Vice President at Oracle Cloud, where he led the company’s AI infrastructure, data center capacity and infrastructure product teams. Prior to that, Jain held roles as Vice President at Google Cloud and Amazon. At Google, he managed the capacity planning and optimization and supply chain engineering teams, and played a central role in driving Google Cloud to profitability. At Amazon, he held leadership roles across pricing, fees, marketplace technologies, AWS  and supply chain teams – setting the strategic direction for top and bottom line growth across various businesses.

“Both Chen and Sachin are exciting and critical additions to the CoreWeave team,” said Mike Intrator, co-founder and CEO of CoreWeave. “Their vision for the future of the AI cloud is strongly aligned with what we’re building at CoreWeave – in terms of a customer-centered development philosophy and the power of automation – and their expertise is unmatched. Their contributions to our platform will take CoreWeave’s industry-leading products and operations to the next level.”

“I’ve been watching the incredible growth and customer success that CoreWeave has accomplished in such a short amount of time, and am thrilled to join this team with tremendous technical excellence,” said Chen Goldberg. “CoreWeave is light years ahead and delivering what others cannot – its modern infrastructure with unbeatable performance sets the company apart. I look forward to building the next generation of cutting-edge cloud technology alongside some of the best in the business.”

“I have been amazed and inspired by the phenomenal team at CoreWeave and what they have accomplished,” said Sachin Jain. “The fact that some of the largest enterprise companies and fast growing AI labs are choosing CoreWeave as their cloud partner is a testament to their unparalleled innovations and relentless focus on customer success. I am excited by the opportunities ahead of us and am eagerly looking forward to being part of this incredible journey.”

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Today’s news comes on the heels of significant momentum. Between May 2023 to May 2024, CoreWeave raised a total of $12 billion from top-tier equity and debt investors. Recently, the company also announced its new London office and subsequent European expansion – creating over 120 new jobs across engineering, operations, finance and go-to-market, and a commitment to invest a total of $3.5 billion across Europe. The company also continues to grow its leadership bench with top-tier talent. Last week, CoreWeave announced its new Chief Accounting Officer, Jeff Baker, who joined from Rivian and played an essential role in its landmark initial public offering. In June, CoreWeave announced Chief Product Officer, Chetan Kapoor, who joined from Amazon Web Services where he was part of the Compute leadership team and managed the Hardware Accelerated Compute business. Earlier this year, the company welcomed Nitin Agrawal as Chief Financial Officer, who most recently served as VP of Finance for Google Cloud. With these additions, CoreWeave is fueling its next phase of growth and pushing the frontier of AI infrastructure forward. In 2023, CoreWeave more than quadrupled its data center presence, and anticipates doubling that figure globally by the end of 2024.

Don’t miss this out: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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Nobl Q, a leading global AI- and cloud-based solutions provider, announced today the acquisition of Propel, a renowned software developer that specializes in advanced technology solutions. This strategic acquisition is set to amplify Nobl Q’s proficiencies and its reach across multiple verticals, as it brings together some of the sharpest minds in the industry.

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The integration of Propel’s advanced capabilities and innovative solutions into Nobl Q’s comprehensive suite of services is anticipated to provide unparalleled value to clients, fostering enhanced digital transformation initiatives and business growth beyond the traditional scope, all designed to drive client success.

“We are excited to welcome Propel into the Nobl Q family,” said Caldwell Velnambi, CEO and Founder of Nobl Q. “Just as grammar is the backbone of literature, ensuring its integrity and clarity, the culture at Propel embodies the essence for successful software development. This acquisition is a significant milestone in our mission to deliver innovative and impactful technology solutions. Propel’s business focus, expertise in software development, and their commitment to excellence align perfectly with our vision and, together, we will drive transformative change for our clients.”

This is the third major acquisition made by Nobl Q in the past twelve months, tripling both its revenues and employee base, and bolstering its offerings significantly, making Nobl Q one of the fastest-growing AI and cloud solutions companies in the US.

As part of the acquisition, Propel’s CEO, Bala Chandra, will take on the role of Chief Operations Officer at Nobl Q, and Propel’s COO and CHRO, Malar Chandra, will assume the role of Nobl Q’s Chief Integration and Transformation Officer. These leadership placements underscore the strategic importance of this acquisition, positioning Nobl Q to leverage Propel’s strengths, drive future growth, and integrate their competencies into Nobl Q’s business model. These appointments follow suit with the corporate transformation Nobl Q has made this year, adding tremendous experience and technology prowess to an already stellar leadership team.

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“Joining forces with Nobl Q presents a tremendous opportunity for Propel and our clients,” said Bala Chandra. “Our combined expertise and shared dedication to innovation will enable us to deliver even greater value and support to our customers. We are thrilled to embark on this new chapter together.”

The combined entity will operate under the Nobl Q brand, creating a one-stop-shop for digital transformation for companies across industries and around the globe.

Propel’s robust portfolio and experienced team will enhance Nobl Q’s service offerings, providing clients with an even broader range of advanced solutions tailored to meet their specific needs. It is through the acquisition of remarkable assets like Propel which exemplifies Nobl Q’s commitment to staying at the forefront of technological advancements and delivering outstanding client outcomes.

Don’t miss this out: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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Innovative Integration Yields Unprecedented 92.5% Accuracy in Text-to-SQL Tasks

AtScale, a pioneering leader in data management and analytics, announces a significant breakthrough in Natural Language Processing (NLP). By integrating AtScale’s Semantic Layer and Query Engine with large language models (LLMs), AtScale has set a new standard in Text-to-SQL accuracy, achieving an impressive 92.5% across all combinations of question and schema complexities.

Also Read: AiThority Interview with Seema Verma, EVP and GM, Oracle Health and Life Sciences

“By feeding the LLM with relevant business context, we can achieve a level of accuracy previously unattainable, making Text-to-SQL solutions trusted in everyday business use.”

As enterprises generate and store increasing volumes of data, the demand for quick, accurate data analysis has never been higher, outpacing traditional methods reliant on human analysts. AtScale’s integration of Generative AI transforms natural language queries into precise SQL commands, dramatically improving efficiency and decision-making speed. While LLMs excel at generating human-like text, they often struggle with complex database schemas and business logic. AtScale’s Semantic Layer bridges this gap by providing LLMs with comprehensive business-side metadata, eliminating the need to create metrics from scratch or generate complex joins, and significantly enhancing result consistency and accuracy.

“Our integration of AtScale’s Semantic Layer and Query Engine with LLMs marks a significant milestone in NLP and data analytics,” said David Mariani, CTO and Co-Founder of AtScale. “By feeding the LLM with relevant business context, we can achieve a level of accuracy previously unattainable, making Text-to-SQL solutions trusted in everyday business use.”

Also Read: AiThority Interview with Kunal Purohit, President – Next Gen Services, Tech Mahindra

In rigorous testing, AtScale’s integrated solution outperformed traditional methods by a wide margin. Across a diverse set of 40 business-related questions, the solution achieved a 92.5% accuracy rate, compared to just 20% for systems without the Semantic Layer. These results underscore the system’s capability to handle a wide range of query complexities with superior precision.

Key Benefits of AtScale’s Solution:

1. Enhanced Accuracy: Achieves 92.5% accuracy in translating natural language questions into SQL queries.
2. Simplified Query Generation: Removes the need for LLMs to generate joins or complex business logic, reducing errors and improving efficiency.
3. Business Context Integration: Provides LLMs with essential business metadata, ensuring consistent and accurate results.

AtScale is committed to continuously advancing its AI-driven solutions. The company plans to enhance the integration further by optimizing prompt engineering and expanding training datasets, aiming to tackle even more complex queries with greater precision and efficiency. By doing so, AtScale seeks to empower businesses with increasingly robust and reliable data analysis tools.

Also Read: AiThority Interview with Yair Amsterdam, CEO of Verbit 

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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