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Unlock the power of Generative AI for your business with IngestAI's sophisticated toolbox for data preparation, model selection, and AI enablement.

How IngestAI can help you:

Optimizes AI application development tailored to your needs.
Transforms raw data into high-quality, RAG-ready formats.
Enhances the performance and accuracy of your RAG-powered applications.
Customizes AI models to align with industry-specific demands and data nuances.
Provides a serverless API for dependable and scalable RAG applications.

Why choose IngestAI: Key features

Proprietary AI algorithms for model selection and data preparation.
Deepmark AI for efficient AI solution deployment.
Embedditor for seamless manipulation and processing of enterprise data.
Scalable serverless API for seamless data access.
Backed by leading technology platforms and programs.

Who should choose IngestAI:

Businesses looking to leverage generative AI for data-driven solutions.
AI developers and builders seeking optimized development tools.
Organizations aiming to enhance RAG-powered solutions performance and accuracy.
Industries requiring customized AI models for specific needs.

About IngestAI

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https://ingestai.io

Release Date

March 2024

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Harnessing the Power of Generative AI for Business Growth

In the rapidly evolving digital landscape, businesses are constantly seeking innovative solutions to stay ahead of the curve. A promising advancement in this realm is generative AI, a technology that creates content ranging from text to images and beyond. Companies looking to harness the full potential of this technology can explore solutions like generative-ai-development, which offers comprehensive development services tailored to specific business needs. A leader in leveraging generative AI's potential is Geniusee, a brand renowned for helping businesses seamlessly integrate AI into their operations. By partnering with them, companies can navigate the complexities of AI adoption, ensuring that every step, from initial strategy to implementation, is executed with precision and expertise.

Analytics Insight

Thu, 5 Sept, 2:10 PM UTC

Understanding RAG: How to integrate generative AI LLMs with your business knowledge

Also: Make room for RAG: How Gen AI's balance of power is shifting One of the most promising advancements in this domain is Retrieval Augmented Generation (RAG). But what exactly is RAG, and how can it be integrated with your business documents and knowledge? RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information repositories, enhancing their ability to generate accurate and contextually relevant responses. As Maxime Vermeir, senior director of AI strategy at ABBYY, a leading company in document processing and AI solutions, explained: "RAG enables you to combine your vector store with the LLM itself. This combination allows the LLM to reason not just on its own pre-existing knowledge but also on the actual knowledge you provide through specific prompts. This process results in more accurate and contextually relevant answers." Also: There are many reasons why companies struggle to exploit Gen AI, says Deloitte survey This capability is especially crucial for businesses that need to extract and utilize specific knowledge from vast, unstructured data sources, such as PDFs, Word documents, and other file formats. As Vermeir details in his blog, RAG empowers organizations to harness the full potential of their data, providing a more efficient and accurate way to interact with AI-driven solutions. Traditional LLMs are trained on vast datasets, often called "world knowledge". However, this generic training data is not always applicable to specific business contexts. For instance, if your business operates in a niche industry, your internal documents and proprietary knowledge are far more valuable than generalized information. Maxime noted: "When creating an LLM for your business, especially one designed to enhance customer experiences, it's crucial that the model has deep knowledge of your specific business environment. This is where RAG comes into play, as it allows the LLM to access and reason with the knowledge that truly matters to your organization, resulting in accurate and highly relevant responses to your business needs." Also: Enterprises double their Gen AI deployment efforts, Bloomberg survey says By integrating RAG into your AI strategy, you ensure that your LLM is not just a generic tool but a specialized assistant that understands the nuances of your business operations, products, and services. At the heart of RAG is the concept of vector databases. A vector database stores data in vectors, which are numerical data representations. These vectors are created through a process known as embedding, where chunks of data (for example, text from documents) are transformed into mathematical representations that the LLM can understand and retrieve when needed. Maxime elaborated: "Using a vector database begins with ingesting and structuring your data. This involves taking your structured data, documents, and other information and transforming it into numerical embeddings. These embeddings represent the data, allowing the LLM to retrieve relevant information when processing a query accurately." Also: Generative AI's biggest challenge is showing the ROI - here's why This process allows the LLM to access specific data relevant to a query rather than relying solely on its general training data. As a result, the responses generated by the LLM are more accurate and contextually relevant, reducing the likelihood of "hallucinations" -- a term used to describe AI-generated content that is factually incorrect or misleading. Several open-source tools can help you implement RAG effectively within your organization: The hyperscale cloud providers offer multiple tools and services that allow businesses to develop, deploy, and scale RAG systems efficiently. Integrating AI with business knowledge through RAG offers great potential but comes with challenges. Successfully implementing RAG requires more than just deploying the right tools. The approach demands a deep understanding of your data, careful preparation, and thoughtful integration into your infrastructure. One major challenge is the risk of "garbage in, garbage out". If the data fed into your vector databases is poorly structured or outdated, the AI's outputs will reflect these weaknesses, leading to inaccurate or irrelevant results. Additionally, managing and maintaining vector databases and LLMs can strain IT resources, especially in organizations lacking specialized AI and data science expertise. Also: 5 ways CIOs can manage the business demand for generative AI Another challenge is resisting the urge to treat RAG as a one-size-fits-all solution. Not all business problems require or benefit from RAG, and depending too heavily on this technology can lead to inefficiencies or missed opportunities to apply simpler, more cost-effective solutions. To mitigate these risks, investing in high-quality data curation is important, as well as ensuring your data is clean, relevant, and regularly updated. It's also crucial to clearly understand the specific business problems you aim to solve with RAG and align the technology with your strategic goals. Additionally, consider using small pilot projects to refine your approach before scaling up. Engage cross-functional teams, including IT, data science, and business units, to ensure that RAG is integrated to complement your overall digital strategy.

ZDNet

Fri, 6 Sept, 10:04 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

How to convert PDFs, Docx and CSV files into structured data with AI for RAG

If you have ever found yourself spending hours sifting through piles of PDFs, DOCX files, and CSVs, manually extracting the data you need. It's tedious, right? I've been there, and I know how frustrating it can be. But what if I told you there's a way to automate this process, saving you time and effort? Enter Unstract, a no-code AI platform designed to turn your unstructured data into structured gold. In this guide by World of AI, we'll explore how Unstract can transform your data extraction woes into a seamless experience. In today's digital landscape, organizations are inundated with vast amounts of unstructured data from various sources and formats. From PDFs and DOCX files to CSVs and scanned documents, extracting valuable insights from this data can be a daunting task. Manual data extraction is not only time-consuming but also prone to errors and inconsistencies. This is where Unstract, a no-code AI platform, comes into play, transforming the way we convert unstructured data into structured formats. Unstract harnesses the power of artificial intelligence to automate the extraction and structuring of data from diverse document types. By using advanced AI algorithms, Unstract simplifies the process of converting unstructured data into actionable insights. With its intuitive interface and no-code approach, Unstract empowers users to focus on analyzing and using the extracted data rather than getting bogged down by the technicalities of data extraction. Here are a selection of other articles from our extensive library of content you may find of interest on the subject of converting files : One of the key challenges in extracting data from unstructured documents is preserving the layout and ensuring accurate extraction, especially when dealing with complex formats like legal documents or financial statements. This is where Unstract's LM Whisper Tool shines. This powerful tool is designed to handle intricate document structures, maintaining the integrity of the data while extracting it with precision. Unstract's versatility extends beyond text-based documents. With its built-in Optical Character Recognition (OCR) mode, Unstract can effortlessly process image-based files such as scanned documents and handwritten notes. The OCR technology automatically converts the visual information into machine-readable text, allowing seamless data extraction from a wide range of sources. Unstract's flexibility is not limited to its document processing capabilities. The platform supports various deployment options, allowing organizations to integrate it into their existing systems and workflows seamlessly. Whether you prefer cloud-based or on-premises deployment, Unstract adapts to your needs. Accessibility is a key priority for Unstract. The platform offers a free trial and an open-source edition, making it accessible to a wide range of users. The free trial allows you to explore the platform's features and assess its suitability for your specific requirements. The open-source edition takes it a step further, providing opportunities for customization and integration with other tools, empowering developers and organizations to tailor Unstract to their unique needs. The applications of Unstract are vast and diverse. From extracting data from invoices and credit card statements to processing handwritten forms and surveys, Unstract's AI-driven approach streamlines data extraction across various industries. By automating this process, organizations can: Unstract's additional features, such as auto compaction and support for multiple AI models and vector databases, further enhance its capabilities. Auto compaction optimizes data processing by reducing unnecessary tokens, improving efficiency and performance. The support for various AI models and vector databases ensures that Unstract can adapt to evolving data extraction requirements and integrate with existing data storage solutions. Unstract and its LM Whisper Tool are transforming the way we convert unstructured data into structured formats. By using the power of AI, these tools automate data extraction, making it faster, more accurate, and more accessible than ever before. Whether you are dealing with PDFs, DOCX files, CSVs, or image-based documents, Unstract provides a comprehensive solution for your data extraction needs. Embrace the future of data processing with Unstract and unlock the full potential of your unstructured data.

Geeky Gadgets

Tue, 17 Sept, 6:00 AM 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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