There is a lot of enterprise data trapped in PDF documents. To be sure, gen AI tools have been able to ingest and analyze PDFs, but accuracy, time and cost have been less than ideal. New technology from <a href="https://venturebeat.com/ai/databricks-research-reveals-that-building-better-ai-judges-isnt-just-a">Databricks</a> could change that.The company this week detailed its &quot;ai_parse_document&quot; technology, now integrated with Databricks&#x27; <a href="https://venturebeat.com/ai/why-most-enterprise-ai-agents-never-reach-production-and-how-databricks-plans-to-fix-it">Agent Bricks platform</a>. The technology addresses a critical bottleneck in enterprise AI adoption: Approximately 80% of enterprise knowledge remains locked in PDFs, reports and diagrams that AI systems struggle to accurately process and understand.&quot;It&#x27;s a common assumption that parsing PDFs is a solved problem, but in reality, it isn&#x27;t,&quot; Erich Elsen, principal research scientist at Databricks, told VentureBeat. &quot;The challenge isn&#x27;t just that documents are unstructured; it&#x27;s that enterprise PDFs are inherently complex. They mix digital-native content with scanned pages and photos of physical documents, alongside tables, charts and irregular layouts, and most existing tools fail to capture that information accurately.&quot;<h2>The hidden complexity behind document parsing</h2>While optical character recognition (OCR) has existed for decades, Elsen argues that extracting usable, structured data from real-world enterprise documents remains fundamentally unsolved. Key elements such as tables with merged cells, figure captions and spatial relationships between document elements are routinely dropped or misread by existing tools, making downstream AI applications, retrieval-augmented generation (RAG) systems or business intelligence dashboards unreliable.The typical enterprise workaround has been to stack multiple imperfect tools together: One service for layout detection, another for OCR, a third for table extraction, as well as additional APIs for figure analysis. This approach requires months of custom data engineering and ongoing maintenance as document formats evolve.&quot;To compensate, teams have had to stack multiple imperfect tools or build extensive custom pipelines, spending months on data engineering instead of innovation,&quot; Elsen said. &quot;ai_parse_document solves that by extracting complete, structured data from real-world documents — so organizations can finally trust and query unstructured data directly within Databricks.&quot;<h2>Technical approach: End-to-end training vs. pipeline stacking</h2>There are multiple services in the market today for parsing PDFs, including AWS Textract, Google Document AI and Azure Document Intelligence, among others. Elsen argued that instead of just reading text, the tool uses a system of modern AI components trained to end-to-end to extract structured context with state-of-the-art quality.The function goes beyond basic extraction to capture:<ul><li>Tables preserved exactly as they appear, including merged cells and nested structures</li><li>Figures and diagrams with AI-generated captions and descriptions</li><li>Spatial metadata and bounding boxes for precise element location</li><li>Optional image outputs for multimodal search applications</li></ul>All results are stored directly in the Databricks Unity Catalog as Delta tables, meaning parsed documents become queryable structured data without leaving the Databricks environment. This is a key differentiator from cloud services that require exporting data for processing.&quot;Through data-centric training and optimized inference, we&#x27;ve achieved 3–5x lower cost while matching or exceeding leading systems like Textract, Document AI and Azure Document Intelligence,&quot; Elsen said.<h2>Early enterprise adoption across manufacturing and industrial sectors</h2>Several major enterprises have already deployed ai_parse_document in production with use cases spanning data science workflow optimization, democratization of document processing and RAG application development.For example, Elsen noted that Rockwell Automation uses ai_parse_document to reduce configuration overhead for its data scientists. &quot;What once required significant setup to support complex solutions is now streamlined, letting their teams spend more time innovating and less time managing infrastructure,&quot; he said.TE Connectivity, meanwhile, is using ai_parse_document to democratize unstructured data processing.&quot;Previously, extracting tables, text and metadata from documents required complex, code-heavy workflows,&quot; Elsen said. &quot;With Databricks, they’ve condensed all of that into a single SQL function, making advanced document processing accessible to every data team, not just data scientists.&quot;Emerson Electric is another early adopter. The company is using  ai_parse_document for a  RAG use case. Elsen explained that by enabling parallel document parsing directly within Delta tables, Emerson has made building RAG applications both fast and simple, all within its existing Databricks environment.<h2>The platform integration play</h2>While Databricks has a long history with open source, the ai_parse_document technology is a proprietary component of the Databricks platform.Unlike standalone document intelligence APIs, ai_parse_document is deeply integrated with Databricks&#x27; Agent Bricks platform, which is a collection of AI functions and orchestration capabilities for building production AI agents. The function works with Databricks&#x27; broader data infrastructure, including:<ul><li>Spark Declarative Pipelines: Provide automatic incremental processing, meaning new documents arriving in SharePoint, S3 or Azure Data Lake Storage are parsed automatically without manual orchestration.</li><li>Unity Catalog: Governs permissions, audit trails and data lineage for parsed content exactly as it does for structured data. </li><li>Vector Search: Indexes parsed document elements including text, tables and figures with captions for multimodal RAG applications. </li><li>AI function chaining: Allows developers to pipe ai_parse_document output directly to ai_extract (entity extraction), ai_classify (document categorization) and ai_summarize (content summarization) within a single SQL query.</li><li>Multi-Agent Supervisor: Coordinates document-processing agents with other specialized agents for complex workflows.</li></ul>&quot;Parsing is only the beginning and rarely an end unto itself,&quot; Elsen said. &quot;The goal is to allow customers to chain our ai_functions, like ai_extract and ai_classify, together with ai_parse_document to turn their documents into actionable data and insights. We also aim to make it seamless to turn a corpus of documents into a knowledge database for use in RAG or other information retrieval agents.&quot;<h2>What this means for enterprise AI strategy</h2>For enterprises building AI agent systems, it&#x27;s critical to understand how PDF documents are actually used and understood by systems. The Databricks approach sheds new light on an issue that many might have considered to be a solved problem. It challenges existing expectations with a new architecture that could benefit multiple types of workflows. However, this is a platform-specific capability that requires careful evaluation for organizations not already using Databricks.For technical decision-makers evaluating AI agent platforms, the key takeaway is that document intelligence is shifting from a specialized external service to an integrated platform capability.

قدمت شركة داتابريكس تقنية جديدة تُدعى 'ai_parse_document' تهدف إلى تحسين تحليل مستندات PDF، وهي منطقة تواجه حاليًا تحديات بالنسبة لأنظمة الذكاء الاصطناعي. على الرغم من التقدم في أدوات الذكاء الاصطناعي التوليدية، إلا أن الدقة والوقت والتكلفة لمعالجة ملفات PDF لم تكن مرضية. حوالي 80% من المعرفة المؤسسية لا تزال محصورة في ملفات PDF والتقارير والمخططات، مما يجعل التحليل الفعال أمرًا حاسمًا لاعتماد الذكاء الاصطناعي في المؤسسات. وأكد إريك إلسن، عالم الأبحاث الرئيسي، أن تحليل ملفات PDF لا يزال مشكلة غير محلولة.

Databricks ha presentado una nueva tecnología llamada 'ai_parse_document' que busca mejorar el análisis de documentos PDF, un área que actualmente presenta desafíos para los sistemas de IA. A pesar de los avances en herramientas de IA generativa, la precisión, el tiempo y el costo del procesamiento de PDF no han sido satisfactorios. Aproximadamente el 80% del conocimiento empresarial sigue encerrado en PDF, informes y diagramas, lo que hace que un análisis efectivo sea crucial para la adopción de IA en las empresas. El científico principal de investigación, Erich Elsen, enfatizó que el análisis de PDF sigue siendo un problema no resuelto.

Databricks a introduit une nouvelle technologie appelée 'ai_parse_document' qui vise à améliorer l'analyse des documents PDF, un domaine qui pose actuellement des défis pour les systèmes d'IA. Malgré les avancées des outils d'IA générative, l'exactitude, le temps et le coût du traitement des PDF n'ont pas été satisfaisants. Environ 80 % des connaissances d'entreprise sont encore enfermées dans des PDF, des rapports et des diagrammes, rendant l'analyse efficace cruciale pour l'adoption de l'IA dans les entreprises. Erich Elsen, scientifique principal de la recherche, a souligné que l'analyse des PDF reste un problème non résolu.

Databricks has introduced a new technology called 'ai_parse_document' that aims to improve the parsing of PDF documents, which currently presents challenges for AI systems. Despite advancements in generative AI tools, the accuracy, time, and cost of processing PDFs have not been satisfactory. Approximately 80% of enterprise knowledge is still locked in PDFs, reports, and diagrams, making effective parsing crucial for AI adoption in enterprises. Principal research scientist Erich Elsen emphasized that PDF parsing remains an unsolved problem.

Databricks: 'PDF parsing for agentic AI is still unsolved' — new tool replaces multi-service pipelines with single function

Walmart's early Black Friday deals are here, with big discounts on TVs, laptops, and more (including the lowest-ever prices on AirTags, AirPods, and the Apple Watch Series 10).

Best Walmart Black Friday deals live now: Save up to 60% on Apple, Dyson, TVs, and more

Meta has started notifying young Australians to download their digital histories and delete their accounts from Facebook, Instagram, and Threads

بدأت ميتا بإخطار الشباب الأستراليين بتحميل بياناتهم وحذف حساباتهم من فيسبوك وإنستغرام وثريدز. تأتي هذه الخطوة في ظل مناقشات حول حظر محتمل لوسائل التواصل الاجتماعي يستهدف هذه المنصات للمستخدمين دون سن 16 عامًا في أستراليا.

Meta ha comenzado a notificar a los jóvenes australianos que descarguen sus datos y eliminen sus cuentas de Facebook, Instagram y Threads. Esta acción se produce en medio de discusiones sobre una posible prohibición de las redes sociales que afectaría a estas plataformas para los usuarios menores de 16 años en Australia.

Meta a commencé à notifier les jeunes Australiens de télécharger leurs données et de supprimer leurs comptes sur Facebook, Instagram et Threads. Cette action intervient dans le cadre de discussions sur une éventuelle interdiction des réseaux sociaux visant ces plateformes pour les utilisateurs de moins de 16 ans en Australie.

Meta has begun notifying young Australians to download their data and delete their accounts from Facebook, Instagram, and Threads. This action comes amid discussions of a potential social media ban targeting these platforms for users under 16 in Australia.

Meta alerts young Australians to download their data before a social media ban

The U.S. Border Patrol is monitoring millions of American drivers nationwide in a secretive program to identify and detain people whose travel patterns it deems suspicious

تقوم دورية الحدود الأمريكية بمراقبة ملايين السائقين الأمريكيين في إطار برنامج سري. تهدف هذه المبادرة إلى تحديد واحتجاز الأشخاص الذين تعتبر أنماط سفرهم مشبوهة.

La Patrulla Fronteriza de EE. UU. está monitoreando a millones de conductores estadounidenses en un programa secreto. Esta iniciativa tiene como objetivo identificar y detener a las personas cuyos patrones de viaje se consideran sospechosos por la agencia.

La patrouille frontalière américaine surveille des millions de conducteurs américains dans le cadre d'un programme secret. Ce programme vise à identifier et à arrêter les personnes dont les habitudes de voyage sont jugées suspectes par l'agence.

The U.S. Border Patrol is conducting a secretive program to monitor millions of American drivers across the nation. This initiative aims to identify and detain individuals whose travel patterns are considered suspicious by the agency.

Border Patrol is monitoring US drivers and detaining those with 'suspicious' patterns

We found the best protein powders that won’t make your shake taste like drywall.

يمكن أن يؤثر اختيار مسحوق البروتين المناسب بشكل كبير على رحلتك في اللياقة البدنية. يبرز هذا المقال أفضل المنتجات التي لا توفر فقط فوائد غذائية ولكنها أيضًا لذيذة، مما يسهل عليك الالتزام بأهدافك الصحية.

اكتشف أفضل 11 مسحوق بروتين تعزز مشروباتك دون المساس بالطعم. تكشف اختباراتنا الشاملة عن أفضل الخيارات المتاحة.

Elegir la proteína en polvo adecuada puede tener un impacto significativo en tu viaje de fitness. Este artículo destaca los mejores productos que no solo ofrecen beneficios nutricionales, sino que también saben bien, facilitando así tu compromiso con tus objetivos de salud.

Descubre las 11 mejores proteínas en polvo que mejoran tus batidos sin comprometer el sabor. Nuestros exhaustivos análisis revelan las mejores opciones disponibles.

Choisir la bonne poudre de protéines peut avoir un impact significatif sur votre parcours de remise en forme. Cet article met en avant les meilleurs produits qui offrent non seulement des avantages nutritionnels mais qui ont également un bon goût, facilitant ainsi votre engagement envers vos objectifs de santé.

Découvrez les 11 meilleures poudres de protéines qui améliorent vos shakes sans compromettre le goût. Nos tests approfondis révèlent les meilleures options disponibles.

Discover the top 11 protein powders that enhance your shakes without compromising on taste. Our extensive testing reveals the best options available.

11 Best Protein Powders, According to Years of Testing (2025)

Police have arrested a suspected Russian hacker in Thailand who is wanted by the FBI for alleged cyberattacks on U.S. and European government agencies

تم القبض على هاكر روسي مشتبه به في تايلاند، حيث كان مطلوبًا من قبل مكتب التحقيقات الفيدرالي بسبب هجمات إلكترونية مزعومة على وكالات حكومية أمريكية وأوروبية. تسلط هذه الاعتقال الضوء على الجهود الدولية المستمرة لمكافحة الجريمة الإلكترونية.

Un presunto hacker ruso ha sido arrestado en Tailandia, donde es buscado por el FBI por ataques cibernéticos supuestos a agencias gubernamentales de EE. UU. y Europa. Esta detención resalta los esfuerzos internacionales en la lucha contra el cibercrimen.

Un hacker russe présumé a été arrêté en Thaïlande, où il était recherché par le FBI pour des cyberattaques présumées visant des agences gouvernementales américaines et européennes. Cette arrestation met en lumière les efforts internationaux en cours pour lutter contre la cybercriminalité.

A suspected Russian hacker has been arrested in Thailand, where he was wanted by the FBI for alleged cyberattacks targeting U.S. and European government agencies. The arrest highlights ongoing international efforts to combat cybercrime.

Russian hacking suspect wanted by the FBI arrested on Thai resort island

The chip maker reported record revenue, beating Wall Street estimates and increasing current-quarter guidance.

أبلغت شركة إنفيديا عن إيرادات قياسية، متجاوزة تقديرات وول ستريت وزيادة توجيهاتها للربع الحالي. ارتفعت أرباح الشركة المصنعة للرقائق، مما يعكس الطلب القوي على منتجاتها، خاصة في قطاع الذكاء الاصطناعي.

Nvidia reportó ingresos récord, superando las estimaciones de Wall Street y aumentando su guía para el trimestre actual. Las ganancias del fabricante de chips se dispararon, reflejando una fuerte demanda por sus productos, especialmente en el sector de la inteligencia artificial.

Nvidia a annoncé un chiffre d'affaires record, dépassant les estimations de Wall Street et augmentant ses prévisions pour le trimestre en cours. Les bénéfices du fabricant de puces ont grimpé, reflétant une forte demande pour ses produits, en particulier dans le secteur de l'intelligence artificielle.

Nvidia reported record revenue, surpassing Wall Street estimates and raising its guidance for the current quarter. The chip maker's profits soared, reflecting strong demand for its products, particularly in the artificial intelligence sector.

Nvidia Profits Soar, Soothing Investor Jitters Over AI Boom

OpenAI has <a href="https://openai.com/index/gpt-5-1-codex-max/">introduced GPT‑5.1-Codex-Max</a>, a new frontier agentic coding model now available in its Codex developer environment. The release marks a significant step forward in AI-assisted software engineering, offering improved long-horizon reasoning, efficiency, and real-time interactive capabilities. GPT‑5.1-Codex-Max will now replace GPT‑5.1-Codex as the default model across Codex-integrated surfaces.The new model is designed to serve as a persistent, high-context software development agent, capable of managing complex refactors, debugging workflows, and project-scale tasks across multiple context windows.It comes on the heels of<a href="https://venturebeat.com/ai/google-unveils-gemini-3-claiming-the-lead-in-math-science-multimodal-and"> Google releasing its powerful new Gemini 3 Pro model</a> yesterday, yet still outperforms or matches it on key coding benchmarks: On SWE-Bench Verified, GPT‑5.1-Codex-Max achieved 77.9% accuracy at extra-high reasoning effort, edging past Gemini 3 Pro’s 76.2%. It also led on Terminal-Bench 2.0, with 58.1% accuracy versus Gemini’s 54.2%, and matched Gemini’s score of 2,439 on LiveCodeBench Pro, a competitive coding Elo benchmark.When measured against Gemini 3 Pro’s most advanced configuration — its Deep Thinking model — Codex-Max holds a slight edge in agentic coding benchmarks, as well. <h3>Performance Benchmarks: Incremental Gains Across Key Tasks</h3>GPT‑5.1-Codex-Max demonstrates measurable improvements over GPT‑5.1-Codex across a range of standard software engineering benchmarks. On SWE-Lancer IC SWE, it achieved 79.9% accuracy, a significant increase from GPT‑5.1-Codex’s 66.3%. In SWE-Bench Verified (n=500), it reached 77.9% accuracy at extra-high reasoning effort, outperforming GPT‑5.1-Codex’s 73.7%.Performance on Terminal Bench 2.0 (n=89) showed more modest improvements, with GPT‑5.1-Codex-Max achieving 58.1% accuracy compared to 52.8% for GPT‑5.1-Codex. All evaluations were run with compaction and extra-high reasoning effort enabled.These results indicate that the new model offers a higher ceiling on both benchmarked correctness and real-world usability under extended reasoning loads.<h3>Technical Architecture: Long-Horizon Reasoning via Compaction</h3>A major architectural improvement in GPT‑5.1-Codex-Max is its ability to reason effectively over extended input-output sessions using a mechanism called compaction. This enables the model to retain key contextual information while discarding irrelevant details as it nears its context window limit — effectively allowing for continuous work across millions of tokens without performance degradation.The model has been internally observed to complete tasks lasting more than 24 hours, including multi-step refactors, test-driven iteration, and autonomous debugging.Compaction also improves token efficiency. At medium reasoning effort, GPT‑5.1-Codex-Max used approximately 30% fewer thinking tokens than GPT‑5.1-Codex for comparable or better accuracy, which has implications for both cost and latency.<h3>Platform Integration and Use Cases</h3>GPT‑5.1-Codex-Max is currently available across multiple Codex-based environments, which refer to OpenAI’s own integrated tools and interfaces built specifically for code-focused AI agents. These include:<ul><li>Codex CLI, OpenAI’s official command-line tool (@openai/codex), where GPT‑5.1-Codex-Max is already live.</li><li>IDE extensions, likely developed or maintained by OpenAI, though no specific third-party IDE integrations were named.</li><li>Interactive coding environments, such as those used to demonstrate frontend simulation apps like CartPole or Snell’s Law Explorer.</li><li>Internal code review tooling, used by OpenAI’s engineering teams.</li></ul>For now, GPT‑5.1-Codex-Max is not yet available via public API, though OpenAI states this is coming soon. Users who wish to work with the model in terminal environments today can do so by installing and using the Codex CLI.It is not currently confirmed whether or how the model will integrate into third-party IDEs unless they are built on top of the CLI or future API.The model is capable of interacting with live tools and simulations. Examples shown in the release include:<ul><li>An interactive CartPole policy gradient simulator, which visualizes reinforcement learning training and activations.</li><li>A Snell’s Law optics explorer, supporting dynamic ray tracing across refractive indices.</li></ul>These interfaces exemplify the model’s ability to reason in real time while maintaining an interactive development session — effectively bridging computation, visualization, and implementation within a single loop.<h3>Cybersecurity and Safety Constraints</h3>While GPT‑5.1-Codex-Max does not meet OpenAI’s “High” capability threshold for cybersecurity under its Preparedness Framework, it is currently the most capable cybersecurity model OpenAI has deployed. It supports use cases such as automated vulnerability detection and remediation, but with strict sandboxing and disabled network access by default.OpenAI reports no increase in scaled malicious use but has introduced enhanced monitoring systems, including activity routing and disruption mechanisms for suspicious behavior. Codex remains isolated to a local workspace unless developers opt-in to broader access, mitigating risks like prompt injection from untrusted content.<h3>Deployment Context and Developer Usage</h3>GPT‑5.1-Codex-Max is currently available to users on ChatGPT Plus, Pro, Business, Edu, and Enterprise plans. It will also become the new default in Codex-based environments, replacing GPT‑5.1-Codex, which was a more general-purpose model.OpenAI states that 95% of its internal engineers use Codex weekly, and since adoption, these engineers have shipped ~70% more pull requests on average — highlighting the tool’s impact on internal development velocity.Despite its autonomy and persistence, OpenAI stresses that Codex-Max should be treated as a coding assistant, not a replacement for human review. The model produces terminal logs, test citations, and tool call outputs to support transparency in generated code.<h3>Outlook</h3>GPT‑5.1-Codex-Max represents a significant evolution in OpenAI’s strategy toward agentic development tools, offering greater reasoning depth, token efficiency, and interactive capabilities across software engineering tasks. By extending its context management and compaction strategies, the model is positioned to handle tasks at the scale of full repositories, rather than individual files or snippets.With continued emphasis on agentic workflows, secure sandboxes, and real-world evaluation metrics, Codex-Max sets the stage for the next generation of AI-assisted programming environments — while underscoring the importance of oversight in increasingly autonomous systems.

قدمت OpenAI نموذج GPT‑5.1-Codex-Max الجديد، وهو نموذج برمجة متاح في بيئة تطوير Codex الخاصة بها. يعزز هذا النموذج الهندسة البرمجية المدعومة بالذكاء الاصطناعي من خلال تحسين التفكير، والكفاءة، والقدرات التفاعلية. يحل محل نموذج GPT‑5.1-Codex كنموذج افتراضي، مصمم لإدارة المهام المعقدة في تطوير البرمجيات عبر نوافذ سياق متعددة.

OpenAI ha presentado GPT‑5.1-Codex-Max, un nuevo modelo de codificación disponible en su entorno de desarrollo Codex. Este modelo mejora la ingeniería de software asistida por IA con un razonamiento, eficiencia y capacidades interactivas mejoradas. Reemplaza a GPT‑5.1-Codex como modelo predeterminado, diseñado para manejar tareas complejas de desarrollo de software a través de múltiples contextos.

OpenAI a présenté GPT‑5.1-Codex-Max, un nouveau modèle de codage disponible dans son environnement de développement Codex. Ce modèle améliore l'ingénierie logicielle assistée par IA grâce à un raisonnement, une efficacité et des capacités interactives améliorés. Il remplace GPT‑5.1-Codex comme modèle par défaut, conçu pour gérer des tâches de développement logiciel complexes à travers plusieurs contextes.

OpenAI has introduced GPT‑5.1-Codex-Max, a new coding model available in its Codex developer environment. This model enhances AI-assisted software engineering with improved reasoning, efficiency, and interactive capabilities. It replaces GPT‑5.1-Codex as the default model, designed to handle complex software development tasks across multiple contexts.

OpenAI debuts GPT‑5.1-Codex-Max coding model and it already completed a 24-hour task internally

<a href="https://fetch.ai/">Fetch AI</a>, a startup founded and led by former DeepMind founding investor, Humayun Sheikh, <a href="https://www.businesswire.com/news/home/20251119088395/en/Fetch-Combines-Personalized-AI-with-Multi-Agent-Collaboration-to-Handle-Complex-Consumer-Tasks-Launches-Claim-Your-Agent-to-Fight-Brand-Knock-Offs">on Wednesday announced the release</a> of three interconnected products designed to provide the trust, coordination, and interoperability needed for large-scale AI agent ecosystems. The launch includes <a href="https://asi1.ai/">ASI:One</a>, a personal-AI orchestration platform; <a href="https://business.fetch.ai/">Fetch Business</a>, a verification and discovery portal for brand agents; and <a href="https://agentverse.ai/?sort=relevancy&amp;page=1&amp;recommended=true">Agentverse</a>, an open directory hosting more than two million agents. Together, the system positions Fetch as an infrastructure provider for what it calls the “Agentic Web”—a layer where consumer AIs and brand AIs collaborate to complete tasks instead of merely suggesting them.The company says the tools address a central limitation in current consumer AI: models can provide recommendations but cannot reliably execute multi-step actions that require coordination across businesses. Fetch’s approach centers on enabling agents from different organizations to interoperate securely, using verified identities and shared context to complete end-to-end workflows.“We’re creating the same foundation for agents that Google created for websites,” said Humayun Sheikh, Founder and CEO of Fetch AI, and an early investor in DeepMind, in a press release provided to VentureBeat. “Instead of just finding information, your personal AI coordinates with verified brand agents to get things done.”<h2>Fetch’s founding and DeepMind connection </h2>Fetch AI was founded in 2017 by Humayun Sheikh, an entrepreneur whose early investment in DeepMind helped support the company’s commercial development before its acquisition by Google. “I was one of the first five people at DeepMind and its first investor. My check was the first one in,” Sheikh said, reflecting on the period when advanced machine learning research was still largely inaccessible outside major technology companies.His early experience helped shape Fetch’s direction. “Even in 2013, it was clear to me that agentic systems were going to be the ones that worked. That’s where I focused—on the agentic web,” Sheikh noted. Fetch built on this thesis by developing infrastructure for autonomous software agents, focusing on verifiable identity, secure data exchange, and multi-agent coordination. Over the past several years, the company has expanded to a 70-person team across Cambridge and Menlo Park, raised approximately $60 million, and accumulated more than one million users interacting with its model—data that informed the design of the newly launched products.Sheikh added that his decision to bootstrap the company initially came directly from the proceeds of the DeepMind exit, noting in the interview that while the sale to Google was “a good exit,” he believed the team could have held out for a higher valuation. The early self-funding period allowed Fetch to begin work in 2015—well before transformer architectures went mainstream—on the hypothesis that agentic infrastructure would become foundational to applied AI.<h2>ASI:One is a platform for multi-agent orchestration</h2>At the core of the launch is ASI:One, a language model interface designed specifically for coordinating multiple agents rather than addressing isolated queries. Fetch describes it as an “intelligence layer” that handles context sharing, task routing, and preference modeling.The system stores user-level signals such as favored airlines, dietary constraints, budget ranges, loyalty program identifiers, and calendar availability. When a user requests a complex task — such as planning a trip with flights, hotels, and restaurant reservations — ASI:One retrieves those preferences and delegates work to the appropriate verified agents. The agents then return actionable outputs, including inventory and booking options, rather than generic recommendations.In practice, ASI:One functions as a workflow generator across organizational boundaries. By contrast with conventional LLM applications, which often rely on APIs or RAG techniques to surface information, ASI:One is built to coordinate autonomous agents that can complete transactions. Fetch notes that personalization improves over time as the model accumulates structured preference data.Sheikh emphasized the distinction between orchestrated execution and traditional AI output. “This isn’t searching for options separately and hoping they work together,” he said. “It’s orchestration.” He added that Fetch’s architecture is intentionally modular: “Our architecture is a mix of agentic and expert models. One large model isn’t enough — you need specialists. That’s why we built ASI1, tuned specifically for agentic systems.”The interview also revealed new details about ASI:One’s personalization systems: the platform uses multiple user-owned knowledge graphs to store preferences, travel history, social connections, and contextual constraints. These knowledge graphs are siloed per user and not co-mingled with any Fetch-operated data. Sheikh described this as a “deterministic backbone” that gives the personal AI a stable memory layer beyond the probabilistic output of a single large model.ASI:One launches in Beta today, with a broader release planned for early 2026. Fetch also offers ASI:One Mobile, released earlier this year, giving users access to the same agent-orchestration capabilities on iOS and Android. The mobile app connects directly to Agentverse and the user’s knowledge graphs, enabling on-the-go task execution and real-time interaction with registered agents.<h2>Fetch Business offers verified identity and brand control</h2>To enable reliable coordination between consumers and companies, Fetch is introducing a verification and discovery portal called Fetch Business. The platform allows organizations to verify their identity and claim an official Brand Agent handle — for example, @Hilton or @Nike — regardless of which tools they use to build the underlying agent.Fetch positions the product as an analogue to ICANN domain registration and SSL certificate systems for websites. Verified status is intended to protect consumers from interacting with counterfeit or untrusted agents, a problem the company describes as a major barrier to widespread agent adoption.The system includes low-code tools for small businesses to create agents in a few steps and connect real-time APIs such as inventory, booking systems, or CRM platforms. “With Fetch, you can create an agent in one minute. It gets a handle, like a Twitter username, and you can personalize it completely—even give it your social media permissions to post on your behalf,” Sheikh said. Once a brand claims its namespace, its agent becomes discoverable to consumer AIs and other agents inside Agentverse.The company has pre-reserved thousands of brand namespaces in anticipation of demand. Verification status persists across any platform that integrates with Agentverse, creating a portable identity layer for business agents.The interview highlighted that Fetch Business inherits web-trust primitives directly: domain owners verify their identity by inserting a short code snippet into their existing website backend, allowing the system to pass a cryptographic challenge and grant the agent an authenticity badge similar to a “blue check” for agent identities. Sheikh framed this as “reusing the trust layer the web already spent decades building.”Companies can begin claiming agents now at <a href="https://business.fetch.ai/">business.fetch.ai</a>.<h2>Agentverse is an open directory of more yhan 2 million agents</h2>The final component of the release is <a href="https://agentverse.ai/">Agentverse</a>, an open directory and cloud platform that hosts agents and enables cross-ecosystem discoverability. Fetch states that millions of agents have already registered, spanning travel, retail, entertainment, food service, and enterprise categories.Agentverse provides metadata, capability descriptions, and routing logic that ASI:One uses to identify appropriate agents for specific tasks. It also supports secure communication and data exchange between agents. The company notes that the directory is platform-agnostic: agents built with any framework can join and interoperate.According to Sheikh, the lack of a discovery layer is one reason most AI agents see little or no usage. “Ninety percent of AI agents never get used because there’s no discovery layer,” he said. He framed the role of Agentverse in more technical terms: “Right now, if you build an agent, there’s no universal way for others to discover it. That’s what AgentVerse solves—it’s like DNS for agents.” He also described the system as an essential component of the emerging agent economy: “Fetch is building the Google of agents. Just like websites needed search, agents need discovery, trust, and interaction—Fetch provides all of that.”The interview further underscored that Agentverse is cloud-agnostic by design. Sheikh contrasted this with competing agent ecosystems tied to specific cloud providers, arguing that a universal registry is only viable if independent of proprietary cloud environments. He said the open architecture enables an LLM to query any agent “within one minute of deployment,” turning agent publication into a near-instantaneous process similar to registering a domain.Agentverse also integrates payment pathways, enabling agents to execute purchases using partners such as Visa, Skyfire, and supported stablecoins. Consumers can configure spending limits or require explicit approval for transactions.<h2>Industry context and implications</h2>Fetch’s launch comes at a time when consumer AI platforms are exploring the shift from static chat interfaces toward autonomous agents capable of completing actions. However, most agent systems remain limited by siloed architectures, limited interoperability, and weak verification standards.Fetch positions its infrastructure as a response to these limitations by providing a cross-platform coordination layer, identity system, and directory service. The company argues that an agent ecosystem requires consistent verification mechanisms to ensure that consumers interact with authentic brand representatives rather than imitations. By establishing namespace control and portable trust indicators, Fetch Business aims to fill a gap similar to early web domain verification.At the same time, ASI:One attempts to centralize user preference data in a way that enables more efficient personalization and multi-agent coordination. This approach differs from generalist LLM applications, which often lack persistent preference architectures or direct access to brand-controlled agents.The interview also made clear that micropayments and digital transaction infrastructure are central to Fetch’s long-term vision. Sheikh referenced integrations with protocols such as Coinbase’s 402 and AP2, positioning these capabilities as essential for autonomous agents to complete end-to-end tasks that include financial execution.Fetch’s combined release of ASI:One, Fetch Business, and Agentverse introduces an interconnected stack designed to support large-scale deployment and usage of AI agents. The company frames the system as foundational infrastructure for an agentic ecosystem, where consumer AIs can coordinate with verified brand agents to complete tasks reliably and securely. The additions to its identity, discovery, and orchestration layers reflect Fetch’s long-standing thesis — rooted partly in lessons from DeepMind’s early development — that intelligence becomes meaningful only when paired with the capacity to act.

أطلقت شركة Fetch AI، وهي شركة ناشئة يقودها هميون شيخ، ثلاثة منتجات مترابطة تهدف إلى تحسين البنية التحتية لأنظمة وكلاء الذكاء الاصطناعي. تشمل العروض الجديدة ASI:One، وهي منصة تنسيق ذكاء اصطناعي شخصي؛ وFetch Business، وهو بوابة تحقق لوكلاء العلامات التجارية؛ وAgentverse، وهو دليل مفتوح يضم أكثر من مليوني وكيل. يهدف هذا الإطلاق إلى تسهيل الثقة والتنسيق والتشغيل البيني في الويب غير البشري الناشئ.

Fetch AI, una startup dirigida por Humayun Sheikh, ha lanzado tres productos interconectados destinados a mejorar la infraestructura para ecosistemas de agentes de IA. Las nuevas ofertas incluyen ASI:One, una plataforma de orquestación de IA personal, Fetch Business, un portal de verificación para agentes de marca, y Agentverse, un directorio abierto con más de dos millones de agentes. Este lanzamiento busca facilitar la confianza, la coordinación y la interoperabilidad en la nueva web no humana.

Fetch AI, une startup dirigée par Humayun Sheikh, a lancé trois produits interconnectés visant à améliorer l'infrastructure des écosystèmes d'agents IA. Les nouvelles offres comprennent ASI:One, une plateforme d'orchestration d'IA personnelle, Fetch Business, un portail de vérification pour les agents de marque, et Agentverse, un annuaire ouvert avec plus de deux millions d'agents. Ce lancement vise à faciliter la confiance, la coordination et l'interopérabilité dans le web non humain émergent.

Fetch AI, a startup led by Humayun Sheikh, has launched three interconnected products aimed at enhancing the infrastructure for AI agent ecosystems. The new offerings include ASI:One, a personal-AI orchestration platform, Fetch Business, a verification portal for brand agents, and Agentverse, an open directory with over two million agents. This launch is positioned to facilitate trust, coordination, and interoperability in the emerging non-human web.

The Google Search of AI agents? Fetch launches ASI:One and Business tier for new era of non-human web

A new artificial intelligence startup founded by the creators of <a href="https://opencv.org/">the world&#x27;s most widely used computer vision library</a> has emerged from stealth with technology that generates realistic human-centric videos up to five minutes long — a dramatic leap beyond the capabilities of rivals including OpenAI&#x27;s <a href="https://openai.com/sora/">Sora</a> and Google&#x27;s <a href="https://deepmind.google/models/veo/">Veo</a>.<a href="https://craftstory.com/">CraftStory</a>, which launched Tuesday with $2 million in funding, is introducing Model 2.0, a video generation system that addresses one of the most significant limitations plaguing the nascent AI video industry: duration. While OpenAI&#x27;s <a href="https://openai.com/index/sora-2/">Sora 2</a> tops out at 25 seconds and most competing models generate clips of 10 seconds or less, CraftStory&#x27;s system can produce continuous, coherent video performances that run as long as a typical YouTube tutorial or product demonstration.The breakthrough could unlock substantial commercial value for enterprises struggling to scale video production for training, marketing, and customer education — markets where brief AI-generated clips have proven inadequate despite their visual polish.&quot;If you really try to create a video with one of these video generation systems, you find that a lot of the times you want to implement a certain creative vision, and regardless of how detailed the instructions are, the systems basically ignore a part of your instructions,&quot; said Victor Erukhimov, CraftStory&#x27;s founder and CEO, in an exclusive interview with VentureBeat. &quot;We developed a system that can generate videos basically as long as you need them.&quot;<h3>How parallel processing solves the long-form video problem</h3>CraftStory&#x27;s advance rests on what the company describes as a parallelized diffusion architecture — a fundamentally different approach to how AI models generate video compared to the sequential methods employed by most competitors.Traditional video generation models work by running diffusion algorithms on increasingly large three-dimensional volumes where time represents the third axis. To generate a longer video, these models require proportionally larger networks, more training data, and significantly more computational resources.<a href="https://craftstory.com/">CraftStory</a> instead runs multiple smaller diffusion algorithms simultaneously across the entire duration of the video, with bidirectional constraints connecting them. &quot;The latter part of the video can influence the former part of the video too,&quot; Erukhimov explained. &quot;And this is pretty important, because if you do it one by one, then an artifact that appears in the first part propagates to the second one, and then it accumulates.&quot;Rather than generating eight seconds and then stitching on additional segments, CraftStory&#x27;s system processes all five minutes concurrently through interconnected diffusion processes.Crucially, CraftStory trained its model on proprietary footage rather than relying solely on internet-scraped videos. The company hired studios to shoot actors using high-frame-rate camera systems that capture crisp detail even in fast-moving elements like fingers — avoiding the motion blur inherent in standard 30-frames-per-second YouTube clips.&quot;What we showed is that you don&#x27;t need a lot of data and you don&#x27;t need a lot of training budget to create high quality videos,&quot; Erukhimov said. &quot;You just need high quality data.&quot;Model 2.0 currently operates as a video-to-video system: users upload a still image to animate and a &quot;driving video&quot; containing a person whose movements the AI will replicate. CraftStory provides preset driving videos shot with professional actors, who receive revenue shares when their motion data is used, or users can upload their own footage.The system generates 30-second clips at low resolution in approximately 15 minutes. An advanced lip-sync system synchronizes mouth movements to scripts or audio tracks, while gesture alignment algorithms ensure body language matches speech rhythm and emotional tone.<h3>Fighting a war chest battle with $2 million against billions</h3>CraftStory&#x27;s funding comes almost entirely from <a href="https://finance.yahoo.com/news/2-25-billion-exit-taught-130300997.html">Andrew Filev</a>, who sold his project management software company Wrike to Citrix for <a href="https://techcrunch.com/2021/01/19/citrix-is-acquiring-wrike-from-vista-for-2-25b/">$2.25 billion</a> in 2021 and now runs <a href="https://zencoder.ai/">Zencoder</a>, an AI coding company. The modest raise stands in stark contrast to the billions flowing into competing efforts — OpenAI has <a href="https://www.reuters.com/technology/artificial-intelligence/openai-closes-66-billion-funding-haul-valuation-157-billion-with-investment-2024-10-02/">raised over $6 billion</a> in its latest funding round alone.Erukhimov pushed back on the notion that massive capital is prerequisite for success. &quot;I don&#x27;t necessarily buy the thesis that compute is the path to success,&quot; he said. &quot;It definitely helps if you have compute. But if you raise a billion dollars on a PowerPoint, in the end, no one is happy, neither the founders nor the investors.&quot;Filev defended the David-versus-Goliath approach. &quot;When you invest in startups, you&#x27;re fundamentally betting on people,&quot; he said in an interview with VentureBeat. &quot;To paraphrase Margaret Mead: never underestimate what a small group of thoughtful, committed engineers and scientists can build.&quot;He argued that CraftStory benefits from a focused strategy. &quot;The big labs are in an arms race to build general-purpose video foundation models,&quot; Filev said. &quot;CraftStory is riding that wave and going very deep into a specific format: long-form, engaging, human-centric video.&quot;<h3>Why computer vision expertise matters in generative AI video</h3>Erukhimov&#x27;s credibility stems from his deep roots in computer vision rather than the transformer architectures that have dominated recent AI advances. He was an early contributor to <a href="https://opencv.org/">OpenCV</a> — the Open Source Computer Vision Library that has become the de facto standard for computer vision applications, with over <a href="https://github.com/opencv/opencv">84,000 stars on GitHub</a>.When Intel reduced its support for OpenCV in the mid-2000s, Erukhimov co-founded Itseez with the explicit goal of maintaining and advancing the library. The company expanded OpenCV significantly and pivoted toward automotive safety systems before Intel acquired it in 2016.Filev said this background is precisely what makes Erukhimov well-positioned for video generation. &quot;What people sometimes miss is that generative AI video isn&#x27;t just about the generative part. It&#x27;s about understanding motion, facial dynamics, temporal coherence, and how humans actually move,&quot; Filev said. &quot;Victor has spent his career mastering exactly those problems.&quot;<h3>Enterprise focus targets training videos and product demos</h3>While much of the public excitement around AI video generation has centered on creative tools for consumers, CraftStory is pursuing a decidedly enterprise-focused strategy.&quot;We are definitely thinking about B2B more than consumer,&quot; Erukhimov said. &quot;We&#x27;re thinking about companies, specifically software companies, being able to make cool training videos and product videos and launch videos.&quot;The logic is straightforward: corporate training, product tutorials, and customer education videos often run several minutes and require consistent quality throughout. A 10-second AI clip cannot effectively demonstrate how to use enterprise software or explain a complex product feature.&quot;If you need a longer-form video, then you should go with us,&quot; Erukhimov said. &quot;We can create up to five minutes, consistent video, high quality.&quot;Filev echoed this assessment. &quot;One huge gap in this market is the lack of models that can generate consistent videos over longer sequences — and that&#x27;s extremely important for real-world use,&quot; he said. &quot;If you&#x27;re creating a commercial for your company, a 10-second video, no matter how good it looks, just isn&#x27;t enough. You need 30 seconds, you need two minutes — you need more.&quot;The company anticipates cost savings for customers. Filev suggested that &quot;a small business owner could create content in minutes that previously would have cost $20,000 and taken two months to produce.&quot;CraftStory is also courting creative agencies that produce video content for corporate clients, with the value proposition centered on cost and speed: agencies can record an actor on camera and transform that footage into a finished AI video, rather than managing expensive multi-day shoots.The next major development on CraftStory&#x27;s roadmap is a text-to-video model that would allow users to generate long-form content directly from scripts. The team is also developing support for moving-camera scenarios, including the popular &quot;walk-and-talk&quot; format common in high-end advertising.<h3>Where CraftStory fits in a fragmented competitive landscape</h3>CraftStory enters a crowded and rapidly evolving market. OpenAI&#x27;s <a href="https://openai.com/index/sora-2/">Sora 2</a>, while not yet publicly available, has generated significant buzz. Google&#x27;s <a href="https://deepmind.google/models/veo/">Veo models</a> are advancing quickly. <a href="https://runwayml.com/">Runway</a>, <a href="https://pika.art/login">Pika</a>, and <a href="https://stability.ai/">Stability AI </a>all offer video generation tools with different capabilities.Erukhimov acknowledged the competitive pressure but emphasized that CraftStory serves a distinct niche focused on human-centric videos. He positioned rapid innovation and market capture as the company&#x27;s primary strategy rather than relying on technical moats.Filev sees the market fragmenting into distinct layers, with large tech companies serving as &quot;API providers of powerful, general-purpose generation models&quot; while specialized players like CraftStory focus on specific use cases. &quot;If the big players are building the engines, CraftStory is building the production studio and assembly line on top,&quot; he said.Model 2.0 is available now at app.craftstory.com/model-2.0, with the company offering early access to users and enterprises interested in testing the technology. Whether a lightly funded startup can capture meaningful market share against deep-pocketed incumbents remains uncertain, but Erukhimov is characteristically confident about the opportunity ahead.&quot;AI-generated video will soon become the primary way companies communicate their stories,&quot; he said.

أطلقت CraftStory، وهي شركة ناشئة جديدة في مجال الذكاء الاصطناعي أسسها مبتكرو OpenCV، نظامًا لتوليد الفيديو قادرًا على إنتاج مقاطع فيديو واقعية تركز على الإنسان تصل مدتها إلى خمس دقائق. تتجاوز هذه التكنولوجيا بشكل كبير قدرات المنافسين مثل Sora من OpenAI وVeo من Google، اللذين يقتصران على مقاطع أقصر. حصلت الشركة الناشئة على تمويل بقيمة مليوني دولار لدعم نهجها المبتكر في صناعة الفيديو بالذكاء الاصطناعي.

CraftStory, una nueva startup de IA fundada por los creadores de OpenCV, ha lanzado un sistema de generación de video capaz de producir videos realistas centrados en humanos de hasta cinco minutos de duración. Esta tecnología supera significativamente las capacidades de competidores como Sora de OpenAI y Veo de Google, que están limitados a clips más cortos. La startup ha asegurado 2 millones de dólares en financiamiento para apoyar su enfoque innovador en la industria de video de IA.

CraftStory, une nouvelle startup d'IA fondée par les créateurs d'OpenCV, a lancé un système de génération vidéo capable de produire des vidéos réalistes centrées sur l'humain d'une durée allant jusqu'à cinq minutes. Cette technologie dépasse considérablement les capacités des concurrents tels que Sora d'OpenAI et Veo de Google, qui sont limités à des clips plus courts. La startup a sécurisé 2 millions de dollars de financement pour soutenir son approche innovante dans l'industrie vidéo d'IA.

CraftStory, a new AI startup founded by the creators of OpenCV, has launched a video generation system capable of producing realistic human-centric videos up to five minutes long. This technology significantly surpasses the capabilities of competitors like OpenAI's Sora and Google's Veo, which are limited to shorter clips. The startup has secured $2 million in funding to support its innovative approach in the AI video industry.

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