Granica

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Granica is an AI infrastructure platform designed to simplify and enhance the management of training data for AI teams.

How Granica can help you:

Streamlines the process of storing and collecting training data efficiently.
Enhances the privacy of your training data.
Provides insights into the usage of the training data to improve AI models.
Empowers generative and traditional AI teams to achieve better results at a lower cost.

Why choose Granica: Key features

Powered by cutting-edge research in data-centric AI.
Optimizes information density and efficiency at cloud scale.
Backed by industry pioneers for reliable enterprise AI solutions.

Who should choose Granica:

AI and machine learning teams looking for efficient data management solutions.
Organizations aiming to enhance data privacy and security in their AI projects.
Enterprises seeking to optimize the performance and cost-effectiveness of their AI models.

About Granica

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https://www.granica.ai

Release Date

March 2024

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AI-driven infrastructure: Boosting efficiency and innovation in DevOps - SiliconANGLE

Unlocking enterprise potential: How AI-driven Infrastructure and GPT-in-a-Box are changing DevOps and productivity The push to accomplish more with less is stronger than ever in today's enterprise world. This challenge is driving the adoption of AI-driven infrastructure. Many organizations are not equipped to deploy large language models on their own, leading to the rise of turnkey solutions such as GPT-in-a-Box. These innovations enable the integration of LLMs, generative AI applications and machine learning operations across various environments, transforming how businesses utilize technology, according to Debojyoti (Debo) Dutta (pictured, left), vice president of engineering (AI) at Nutanix Inc. "When people think of Nutanix, they think of us as private cloud architecture ...we are morphing from hyperconverge to enabling basically cloud-native applications, including AI and generative AI," Dutta said. "Everybody wants to run their customer support bot automatically. They want to use AI to run faster, whether it's code generation, whether it's productivity, whether it's SRE, DevOps. That's where we come in and we have a thing called GPT-in-a-Box, a solution that helps customers run generative AI applications really quickly and efficiently with the governance and data protection." Dutta, Shaked Askayo (center), co-founder and chief technology officer of Kubiya Inc., and Amit Eyal Govrin (right), founder and chief executive officer of Kubiya Inc., spoke with theCUBE Research's Savannah Peterson and Rob Strechay at the Supercloud 7: Get Ready for the Next Data Platform event, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the need for AI-driven infrastructure in the present enterprise world. Combining the worlds of development and operations is still a tall order, and this is why DevOps continues to be a headache. Nevertheless, AI-driven infrastructure is proving to be a perfect remedy since it tames the complexity through LLMs, ushering in a new DevOps era, according to Askayo. "It's still hard to adapt CI/CD and also infrastructure as code," he stated. "We're now just entering another phase in this revolution. This phase is now enabled because of the fact that large language models allow us to take off the abstraction, the complexity layer of knowing how to get to these automations, understanding these CI/CD processes or these kinds of collaborative processes. Something that is missing to anyone in CI/CD could be as simple as notifications." Since an AI-driven infrastructure has the capability of abstracting away the complexity, LLM integration is of the essence. As a result, delving deeper into how the LLMs will run and how they will live in the existing framework is of the essence, Askayo pointed out. "When trying to adapt LLM-based processes, which are essentially by their nature DevOps, there's a lot of ways to get such processes up and running," he said. "How do I deal with secrets? How do I deal with serverless orchestration? All of these complexities are something that we learned right from the beginning, even before trying to solve the problem of LLM joining the kitchen. We allow you to essentially define your LLM complex processes, DevOps processes as code concepts." To enhance developers' productivity, delegation has become the new automation. This is because it helps in tackling mundane tasks since at the end of the day it boils down to output versus input, according to Govrin. "It's no longer simply enough to have automation as a kind of a steady state," he noted. "There's a paradox called the time to automation paradox. Now, that loosely states the time and effort it takes you to configure a file and so forth. The outcome you're trying to achieve is to take this role or take this task off my plate, put it in somebody else's shoes. That's the developer experience we're looking for ...where the form factor changes from automation to delegation." Since AI models are being used in all walks of life, including delicate areas, such as healthcare, safety is fundamental. As a result, MLCommons fits into the picture through its open-collaboration philosophy, which accelerates AI innovation in a secure manner, Dutta pointed out. "MLCommons just announced their big AI safety initiative," he said. "That's one of the biggest milestones, a shift that's happening. MLCommons started off as an infrastructure, benchmarking organization. Then it evolved to the best practices and building new data sets. Now it's evolving again to being the trusted partner for AI safety. You don't want models to kind of violate any regulations like NIST in the U.S. and we have similar ones from EU, Singapore and the U.K." As the skills gap continues to bite the AI field, Kubiya and Nutanix are taming this pain point through an AI-driven infrastructure. This is achieved through a two-pronged strategy, whereby enterprises enjoy a simplified infrastructure whereas the developer ecosystem benefits from open-source solutions., according to Dutta. "We are doing this in two different ways," he explained. "One is we want to first help our customers run models very efficiently because it's part of the MLOps lifecycle for them. Our GPT-in-a-Box will help you run large language models with connectors to Hugging Face Hub and Nvidia NIMS, so that customers can then run the model of their choice. For AI-driven infrastructure to be effective, holistic thinking is crucial. This entails a comprehensive view of how organizations deploy AI and data across multiple clouds, making data governance important, Dutta stated. "I think as enterprises want to use AI to make them more efficient, they need to think holistically about their AI strategy, that MLOps and DevOps strategy," he explained. "I mean, gluing this together is basically data. People have to be very careful about data governance and their model governance; it's a two-prong thing." Stay tuned for the complete video interview, part of SiliconANGLE's and theCUBE Research's coverage of the Supercloud 7: Get Ready for the Next Data Platform event.

SiliconANGLE

Wed, 14 Aug, 10:07 PM UTC

Illumex offers Generative Semantic Fabric for data management - SiliconANGLE

Generative Semantic Fabric bridges business and data management gaps The advent of generative artificial intelligence has made data management a pressing concern, with data software companies looking to better support the average business user. Founded in 2021, Illumex Technologies Inc. is set to meet businesses' data needs through its Generative Semantic Fabric, an abstraction layer that automates data mapping and analytics. "The biggest flaw so far in data management in big organizations is actually that technical people are managing data. Because we have the data to enable business in the first place, but there is [a] huge disconnect between business and data and what we need [is] more business people helping to maybe govern and certify and be involved more in the semantic," said Inna Tokarev Sela (pictured), founder and chief executive officer of Illumex. "But it doesn't have to be on the technical level. It should be enabled to be [an] application interface." Tokarev Sela spoke with theCUBE Research's John Furrier at the Supercloud 7: Get Ready for the Next Data Platform event, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how Illumex is transforming enterprise data management and why AI is changing the data analytics space While neural networks imitate the human brain, knowledge graphs -- Tokarev Sela's specialty -- provide context for a user's data. With its Generative Semantic Fabric, Illumex helps business users better understand and harness data through a unifying, contextual layer of abstraction. "GSF is the layer which incorporate[s] content and context. So it's a knowledge graph of semantic embeddings which sits between data and applications," Tokarev Sela said. "It understands two sides of the table and it allows business users to interact in free language, not in data language with any of the data connected to it, and it's translated to the right assets with [a] high degree of governance and explainability." Data management has experienced a resurgence because of AI but a lot of data remains siloed, and there continues to be a lack of data science experts. Illumex's semantic layer is aimed at mapping AI-ready data and creating wider access to data with agentic AI, according to Tokarev Sela. "Big problems which we solve [are] ... AI-ready data, being able to map all the data and distill it to business language, basically map it to semantic language," she said. "And the second part is connecting it to the user input ... this translator which comprehends a human impact and free language and maps it to this knowledge graph of semantic embeddings." Companies were not necessarily motivated to break data silos until gen AI presented a "killer" use case, according to Tokarev Sela, which has now created a need for a semantic layer that connects entire organizations. "We have to have [a] single source of truth [for gen AI] and silos of course prevent us from doing so. And this is why generative AI is the ultimate use case to break silos and finally have unified semantics across organization and even unified semantics across the whole data state," she said. "So again, if you use external data, APIs and so on and so forth, you'll be able to speak the same language to all of those data sources." Even as companies work toward solving the data silo problem, their data stores will still be geographically dispersed. However, Tokarev Sela does not see connecting to different data sources as a problem because Illumex's semantic fabric will be trained on a business's unique terminology. "Generative Semantic Fabric [is], in essence, exactly this glue which connects to the metadata of all organizational structured data sources and business applications ... we automatically build it in a coherent terminology, business glossary, metrics store, lineage and all of that," she said. "We also wrap it up with [an] augmented governance process ... we are picking up all [the] different definitions from those silos, those different functional silos and maybe geographical silos." A company's data should be available to the whole business, Tokarev Sela believes, so working toward that contextualizing layer will ultimately drive more business value for "data-intensive" industries such as pharmaceuticals and financial services. "Graphs capture a concept and they also capture the relationship between concept. But what makes it special for every company is they have own business context concepts which are not captured in general purpose algorithm," she said. "So this is where it's really [a] differentiator to implement those in graphs ... it's the fastest way to receive information."

SiliconANGLE

Thu, 15 Aug, 6:02 PM UTC

Seekr Unveils SeekrFlow: A Comprehensive Platform for Trustworthy Enterprise AI

Seekr Technologies Inc. introduces SeekrFlow, an end-to-end platform for training and deploying reliable AI models in enterprise environments. The platform aims to address challenges in AI development and implementation for businesses.

2 Sources

Thu, 5 Sept, 12:02 AM UTC

AIM Research launched VendorAI, A Comprehensive AI Vendor Database for Strategic Decision-Making

VendorAI by AIM Research is a comprehensive AI vendor database profiling over 200 vendors, providing deep insights for M&A, competitive intelligence, and vendor selection. The AI industry is growing rapidly, with vendors offering a range of solutions across various domains such as data science, data engineering, AI observability, generative AI services, computer vision, and data annotation. This expansion brings with it the challenge of selecting the right vendors, understanding the competitive landscape, and identifying potential acquisition targets. To address this need, AIM Research is proud to launch VendorAI, a comprehensive database that profiles over 200 AI vendors, offering in-depth insights into their capabilities, expertise, and performance. VendorAI is designed for businesses seeking to enhance their strategic decision-making, whether for mergers and acquisitions (M&A), competitive intelligence, or vendor selection. The platform brings together a wealth of data in one place, structured to help users make informed choices when navigating the AI vendor ecosystem. VendorAI features a detailed set of data columns that cover all aspects of an AI vendor's operations, leadership, technology, and competitive advantage. These columns include: In the world of M&A, finding the right target company for acquisition requires access to deep insights and structured data. VendorAI makes this process easier by offering a comprehensive view of AI vendors, allowing businesses to assess potential acquisition targets based on a range of critical metrics. For example, companies can evaluate vendors using the AIM PeMa Score, which aggregates performance metrics to provide a high-level understanding of a vendor's market strength. Furthermore, tracking employee growth over the past six months can reveal whether a vendor is on an upward trajectory or facing operational challenges. Insights into proprietary platforms and flagship technologies can help businesses identify vendors with unique intellectual property or technological innovations that could complement their own portfolios. VendorAI's leadership data is also invaluable for M&A activities, providing easy access to the backgrounds of key figures like CEOs and founders. Understanding the leadership team's experience and professional networks can help acquiring companies evaluate the strength of the management team, which is often critical to the success of a post-acquisition integration. Staying ahead of the competition in the rapidly evolving AI market requires constant monitoring of the competitive landscape. VendorAI offers businesses a structured way to gather competitive intelligence, allowing them to track the activities, innovations, and performance of key players in the industry. With information on each vendor's employee growth, delivery centers, client locations, and industry specialization, businesses can gain a granular understanding of where their competitors are investing resources and expanding their market presence. The platform's insights into proprietary platforms and flagship technologies allow users to identify which vendors are innovating and developing cutting-edge solutions. VendorAI's focus on tracking the number of Fortune 500 clients a vendor serves also provides a valuable indicator of market credibility. Vendors with significant Fortune 500 clients are often seen as more established and trustworthy, making them key players to watch in the competitive landscape. By using VendorAI, businesses can benchmark their competitors' strengths and weaknesses, identifying opportunities for differentiation or areas where they may need to catch up. The platform also enables businesses to keep an eye on emerging vendors, helping them stay ahead of market trends and potential disruptors. Choosing the right vendor in the AI space can be a daunting task, especially with so many options available. VendorAI simplifies this process by providing a structured and detailed view of over 200 vendors, allowing businesses to compare their offerings, industry expertise, and technology platforms. With detailed data on solution types, proprietary platforms, industry specialization, and years of operation, businesses can use VendorAI to match their specific needs with the capabilities of vendors. The platform's filtering options, such as applicable tags for company selection, make it easy to narrow down vendors based on specific criteria such as geographic location, industry focus, or technology expertise. In addition, VendorAI's insights into the number of Fortune 500 clients served by each vendor can be a key factor in decision-making. Vendors with significant Fortune 500 clients often have proven experience in handling large, complex projects, making them strong candidates for businesses with similar requirements. The USP/Competitive Advantage column provides further differentiation, helping businesses identify vendors with unique capabilities that set them apart from the competition. Whether a company is looking for a vendor with a specific technology platform or one with expertise in a niche industry, VendorAI provides the data necessary to make an informed choice. VendorAI joins AIM Research's existing portfolio of industry-leading tools designed to support businesses in strategic decision-making. Alongside VendorAI is GCC Explorer, AIM Research's comprehensive list of Global Capability Centers (GCCs) in India. GCC Explorer offers detailed insights into the GCC ecosystem, helping businesses understand the operational landscape and identify potential partners or acquisition targets. Together, VendorAI and GCC Explorer provide a holistic view of both the AI vendor landscape and the GCC ecosystem, enabling businesses to make informed decisions that align with their long-term strategic goals. With over 200 AI vendors profiled across key categories like data science, data engineering, AI observability, generative AI services, computer vision, and data annotation, VendorAI provides the insights needed to make strategic decisions with confidence. By leveraging the platform's rich data set, businesses can navigate the complex AI vendor landscape, identify potential acquisition targets, gain competitive intelligence, and choose the right vendors to meet their specific needs. Whether you're a company looking to expand through M&A or simply trying to stay ahead of the competition, VendorAI is the ultimate tool for strategic decision-making in the AI industry.

Analytics India Magazine

Sat, 14 Sept, 12:00 PM UTC

Tecton Expands Into GenAI Realm With New Data Platform Release

While the startup originally developed its data "feature store" software for providing data to machine learning systems, Tecton has enhanced its system to provide relevant, reliable data for generative AI large language models. Tecton is expanding beyond its machine learning roots into the generative AI arena with a new release of its data "feature store" platform that the startup says delivers contextual data to the large language models that power GenAI systems. Providing AI systems with contextual data is a significant hurdle to "productizing AI" - delivering tangible business value at scale and unlocking the full potential of generative AI in enterprise applications, according to Tecton. "The whole point is to allow the people building AI to actually build it faster, get to production faster with AI," Tecton co-founder and CEO Mike Del Balso said in an interview with CRN. [Related: The 20 Hottest AI Software Companies: The 2024 CRN AI 100] Tecton, headquartered in San Francisco, was founded in 2018 by Del Balso and CTO Kevin Stumpf who together helped develop Uber's Michelangelo internal machine learning system The startup's initial focus for its technology was providing data "features" or input signals for the predictive models used by machine learning systems. Those capabilities are critical for training machine learning models and putting real-time ML systems - such as within recommendation engines or fraud detection systems - into production to automate business processes and decisions. "It's all been about enabling MLOps [machine learning operations], enabling people to get AI into production faster, to make it more accurate, and to do it with less cost. And we've done a great job of accomplishing that," Del Balso said. Tecton has been selling its feature store system since 2020, enabling businesses and organizations to provide contextual data to machine learning systems as part of their big data management stack without building a whole separate infrastructure. With the wave of development around generative AI and businesses trying to get LLMs into production, Tecton sees the same need for business contextual data management as there was with machine learning when Tecton got started. LLMs often lack up-to-date, domain-specific data with real-time contextual awareness, according to the company. Del Balso says the answer is to focus on better data rather than bigger models and deploy AI applications that are customized to an organization's unique business data. "How do you make it possible for people to easily put AI into production? By focusing on the biggest bottleneck, which is the linkage or the connection between the business's data and the models," Del Balso said. "The data is the company's competitive advantage and if they can't connect that data to the model, their model is not going to have any differentiated behavior and they're not going to have any competitive advantage." "It's all about how you allow your AI to have access [to] and use this proprietary data that you've spent so many years investing in, collecting and organizing. We help them solve all these data problems, so they build better quality signals, so their models actually improve in quality, they become more accurate [and] they have better outcomes," the CEO said. With the new release of the Tecton platform, officially release 1.0, the company is touting its software as a unified platform that can support machine learning, predictive analytics and generative AI. The release includes a suite of new capabilities including managed embeddings, scalable real-time data integration for LLMs, enterprise-grade dynamic prompt management and innovative LLM-powered feature generation, according to the Tecton press release. Data Ingestion, Transformation And Retrieval Many of the new software's new capabilities revolve around improving data ingestion, transformation and retrieval for GenAI systems, Del Balso said. The software supports structured and unstructured data ingestion and automates how LLMs become part of the data transformation pipeline. But Del Balso said the enhanced data retrieval capabilities - getting the right data to the LLMs - are perhaps the most significant advances in the new release. That's because GenAI technology requires more "dynamic" data retrieval than machine learning systems and data context becomes especially important, according to the CEO. The Tecton software's capabilities are "complementary" to the Retrieval-Augmented Generation framework and fit into a RAG architecture, according to Del Balso. The Tecton software ensures data governance and compliance by accessing only permitted data. And by providing more relevant data, the system helps reduce the issue of "hallucinations" when GenAI systems make up data. "The whole point of this is to do it in a safe and trustworthy way for an enterprise," the CEO said, noting the importance of data context and reliability within enterprise production systems. The new Tecton release, which is now in public preview, also provides a number of enhancements to its core capabilities including performance, security and usability, Del Balso said. Del Balso said Tecton is in the early stages of establishing channel relationships. Given the complexity of AI and the fact that many organizations are just getting started with it, the CEO has continued to work more directly with customers to develop their AI strategy and use cases.

CRN

Tue, 17 Sept, 4:01 PM UTC

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