Veritone Conference Call Summary Company Overview - **Company**: Veritone (NasdaqGM:VERI) - **Founded**: 2014 by two serial entrepreneur brothers - **Employees**: Over 400 - **Global Presence**: Offices in the U.S., UK, Germany, France, Australia, India, and Israel - **Customers**: Approximately 3,000 - **Public Listing**: Went public in 2017 - **Core Business**: AI-driven platform for processing unstructured data, known as aiWARE [3][4][7] Industry Insights - **AI Sector**: Veritone operates within the AI space, focusing on unstructured data processing, which includes video, audio, text, and images [3][4] - **Market Growth**: The market for AI-driven data processing is projected to grow from $3 billion to $17 billion by 2032 [12] Financial Performance - **Recent Revenue**: $24 million in the last quarter, flat year-over-year; however, core software revenue grew over 45% year-over-year [13][14] - **Projected Growth**: Anticipated growth of 30% in the upcoming quarter [13] - **Annual Revenue Projection**: Expected to be between $108 million and $115 million for the year, up from $92 million last year [17] - **Gross Margins**: North of 60%, with a focus on cost management [18] - **Annual Recurring Revenue (ARR)**: Over $62 million, indicating strong customer retention [14][15] Product and Service Offerings - **aiWARE Platform**: Features over 850 unique AI models and 26 levels of cognition, capable of processing vast amounts of unstructured data [5][6] - **Key Applications**: - Assists ESPN in programming SportsCenter and managing content [8] - Provides services for NCAA digital content monetization [9] - Supports public safety initiatives for law enforcement and the Department of Defense [10][11] - **New Product Launch**: Veritone Data Refinery (VDR) launched to digitize and index large volumes of content for training AI models [11][12] Strategic Initiatives - **Loyalty Programs**: Engaging with sororities and universities to drive product sales and enhance customer loyalty [1][2] - **Debt Management**: Plans to use recent capital raises to improve liquidity and pay down debt [16][27] - **Market Positioning**: Competes with companies like Palantir and Axon, focusing more on the commercial sector while growing in the public sector [24] Risks and Challenges - **Legal Concerns**: Addressing privacy and copyright issues as the company navigates the complexities of AI and data usage [18] - **Market Competition**: Competing against larger firms in the AI space while maintaining a focus on core competencies [24] Additional Notes - **Customer Engagement**: High customer retention rates in the high 90th percentile, indicating strong product satisfaction [15] - **Future Outlook**: Anticipation of significant growth in the upcoming quarters, with a focus on expanding the software business [23]

[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 ...

AI & Unstructured Data Management - Box is focused on helping companies manage their unstructured data, which accounts for approximately 90% of enterprise data, including financial documents, contracts, and research materials [2] - The company is introducing new capabilities with agents to enable users to tap into unstructured data and automate workflows [3] - Box is launching a new workflow automation capability called Box Automate, allowing users to design end-to-end business processes and integrate agents at various steps [3] - These capabilities can be applied to various industries, such as client onboarding in banking, contract review in law firms, and healthcare data management [4] Competitive Advantage & Market Position - Box's approach of integrating AI into existing data and security infrastructure leads to higher success rates compared to companies building their own AI technology [6][7] - The company emphasizes adapting to the changing software landscape and continuously innovating to maintain its market position [11][12][13] Revenue & Growth Strategy - Box introduced a new plan called Enterprise Advanced, which includes advanced AI capabilities and workflow automation, driving revenue growth [9] - The Enterprise Advanced plan is designed to facilitate customer upgrades and seamless adoption of advanced capabilities [9] - The company's recent financial performance, exceeding guidance and consensus, is attributed to the momentum of Enterprise Advanced [10] - Box is a $1 billion revenue per year company [8]

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 ...