[Music] The world runs on documents. I'm Jerry Lou, co-founder and CEO of Llama Index. Over the past 2 years, I've watched generative AI applications mature.From early RAG demos with a single question box to orchestrated multi-step contextaware agents. At Llama Index, we've been at the center of that journey. From jumpstarting rag with five lines of code to a multi-agent framework powering millions of production workflows with over 4 million downloads a month to the millions of AI builders who've been with ...

What are you most excited about. Because there's a raft of things you're unveiling, whether it's about extracting data, whether it's about managing the raft of agents we're about to face, how are your consumers going to adopt it. Yeah.So I think the thing we're most excited about is that box. We help companies manage their unstructured data. So if you think about 90% of data in the enterprise are things like financial documents, contracts, invoices, research materials, all of that data traditionally you've ...

AI Platform Evolution - Box transitioned to an agentic-first design for metadata extraction to enhance its AI platform [1] - The shift to agentic architecture was driven by the limitations of pre-generative AI data extraction and challenges with a pure LLM approach [1] - Agentic architecture unlocks advantages in data extraction [1] Technical Architecture - Box's AI agent reasoning framework supports the agentic routine for data extraction [1] - The agentic architecture addresses the challenge of unstructured data in enterprises [1] Key Lessons - Building agentic architecture early is a key lesson learned [1]

Core Insights - The rapid adoption of Agentic AI is increasing the demand for unstructured data storage, with Western Digital Corporation (WDC) leveraging this technology to enhance product development and efficiency [1][2] - The demand for scalable storage solutions is rising as data becomes crucial for AI-driven innovation, with HDDs providing unmatched cost efficiency and reliability [2] - WDC's product demand is growing, with significant shipments of high-capacity drives, reflecting a strong market position [3][5] Company Performance - WDC reported a 30% year-over-year revenue increase to $2.61 billion, driven by high-capacity HDD storage for cloud and generative AI workloads [5][10] - The company projects a 22% year-over-year revenue growth for the fiscal first quarter, estimating revenues of $2.7 billion (+/- $100 million) [5][10] - Shipments of PMR drives exceeding 26 terabytes more than doubled sequentially, surpassing 1.7 million units in the June quarter [3][10] Competitive Landscape - WDC competes with major players like Seagate Technology and Pure Storage in the storage and data market [6] - Seagate reported a 30% year-over-year revenue increase to $2.44 billion, driven by demand from cloud, AI, and edge computing [8] - Pure Storage focuses on software-defined all-flash solutions, enhancing performance for unstructured data workloads [9] Market Outlook - WDC's platforms business is accelerating due to the growth of AI, positioning the company to serve infrastructure providers and AI companies [4] - Despite macroeconomic uncertainties, WDC expects revenues to grow 11% to $3.5 billion for fiscal 2026, supported by strong demand for its product offerings [11] - WDC shares have gained 18.4% over the past year, outperforming the Zacks Computer-Storage Devices industry [12]

RT Avi Chawla (@_avichawla)Get RAG-ready data from any unstructured file!@tensorlake transforms unstructured docs into RAG-ready data in a few lines of code. It returns the document layout, structured extraction, bounding boxes, etc.Works on any complex layout, handwritten docs and multilingual data. https://t.co/lZoNWZb2ip ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Get RAG-ready data from any unstructured file!@tensorlake transforms unstructured docs into RAG-ready data in a few lines of code. It returns the document layout, structured extraction, bounding boxes, etc.Works on any complex layout, handwritten docs and multilingual data. https://t.co/lZoNWZb2ip ...

Product Overview - Tensorlake transforms unstructured documents into RAG-ready data with a few lines of code [1] - The solution provides document layout, structured extraction, and bounding boxes [1] - It supports complex layouts, handwritten documents, and multilingual data [1] Technology Focus - The company focuses on enabling RAG (Retrieval-Augmented Generation) applications [1] - The technology extracts structured information from unstructured files [1]

Company Overview - Box is a B2B company operating as an unstructured data platform, serving large enterprises including Fortune 500 companies [1][2] - Box has over 115,000 companies as customers, tens of millions of users, and manages over 1 exabyte of data [2] - Box is often the first AI deployed within large enterprises due to existing trust relationships [3] Data Extraction Evolution - Box initially used a straightforward architecture for data extraction involving pre-processing, OCR, and large language models [8] - The initial AI deployment processed 10 million pages, but encountered challenges with complex documents, OCR accuracy, language variations, and the need for confidence scores [9][10][11] - The company experienced a "trough of disillusionment" as the initial AI solution proved insufficient for diverse customer needs [12] Agentic Approach Implementation - Box re-architected its data extraction process using a multi-agent approach, separating problems into sub-agents [12] - The agentic system intelligently groups related fields, dynamically determines data extraction methods, and incorporates a quality feedback loop for continuous improvement [13] - This approach allows for easier updates and specialization, enabling the company to quickly adapt to new document types and customer requirements [13] Engineering and Customer Impact - Building agentic systems helps engineers think about AI and agentic workflows, leading to better understanding of customer needs [13] - This approach facilitates the development of tools that integrate with customer-built agents, enhancing the overall ecosystem [13] - The company advises building agentic systems early when developing intelligent features [14]

So robot body process automation is literally if human had to do something you basically open some browser or whatever take some data put into some other system click some button and all that stuff. So it records that human clicks on that desktop and tries to keep repeating it. So you kind of like get that automated and the hard part that they had is you can't do robotic process for unstructured data because it's not fixed they change it.So anything will be very very brutal. The bet that we are taking is th ...

Financial Data and Key Metrics Changes - Total Annual Recurring Revenue (ARR) growth was 26%, or 28% when adjusted for foreign exchange, both representing accelerations compared to Q4 [15][65] - Revenue growth was 25%, or 27% on a constant currency basis, with record growth in net new ARR [15][65] - Non-GAAP operating margin in Q1 was approximately 14.5%, with expectations to maintain similar margins for the year [16][65] Business Line Data and Key Metrics Changes - The company has three major suites: resilience, control, and modernization, designed to manage unstructured data throughout its lifecycle [4][8] - The resilience suite focuses on data continuity and lifecycle management, while the control suite implements access controls and user lifecycle management [5][6] - The modernization suite facilitates data migration from legacy systems, retaining data fidelity [8][57] Market Data and Key Metrics Changes - Approximately 11% of the company's ARR is tied to the federal government globally, with only about 2% of total ARR exposed to agencies affected by the Doge situation [18][19] - The demand environment remains healthy, with organizations prioritizing spending in areas that drive value, despite macroeconomic uncertainties [12][66] Company Strategy and Development Direction - The company aims to help organizations with unstructured data management and security, emphasizing the importance of data governance in the context of AI capabilities [3][22] - The strategy includes expanding into the Google Cloud ecosystem and enhancing partnerships with managed service providers (MSPs) [41][45] - The company is focused on geographic expansion, strategic mergers and acquisitions, and scaling its channel strategy to support long-term growth targets [49] Management's Comments on Operating Environment and Future Outlook - Management acknowledges ongoing macroeconomic uncertainty but believes the company is well-positioned to address high-priority issues for clients [12][14] - The pipeline for new business remains positive, indicating continued momentum in performance [14][66] - The company is optimistic about the upcoming EU data regulation and its implications for data governance and privacy [28][78] Other Important Information - The company has built over 30 connectors to legacy systems and other cloud sources, enhancing its ability to manage data across multiple environments [8][57] - The company is actively working on integrating its platform with first-party products like Microsoft Purview and Google Workspace to enhance data governance [24][75] Q&A Session Summary Question: What exposure does the company have to the public sector and the Doge situation? - The company has about 11% of its ARR tied to the federal government globally, with only about 2% of total ARR exposed to agencies affected by Doge, which has been accounted for in their guidance [18][19][68] Question: How does the company differentiate itself from Microsoft's governance tools? - The company's system adds an additional dimension of controls that augment Microsoft's capabilities, addressing specific business scenarios and regulatory requirements [32][83] Question: What is the company's pricing model? - The primary pricing model is per seat, with some cases of capacity or consumption-based pricing as the company transitions to new models [34]