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]

Llama Index Overview - Llama Index has observed the maturation of generative AI applications over the past 2 years [2] - Llama Index provides tools from basic RAG to multi-agent frameworks, supporting millions of production workflows [2] - Llama Index has reached over 4 million downloads per month [2] Llama Cloud Platform - Llama Cloud is positioned as a complete Agentic document automation platform, integrating parsing, extraction, and indexing [5] - Llama Cloud experienced over 700% growth in self-served revenue within one year [5] - Llama Cloud enables parsing complex documents into markdown, extracting information into normalized schemas, and indexing document repositories [6] - Llama Cloud facilitates the creation of end-to-end agentic workflows for research and business process automation [7] Use Cases and Applications - Llama Cloud is used to build research co-pilots that can access and extract insights from enterprise knowledge bases, reducing compilation time from weeks to a shorter timeframe [8] - Llama Cloud enables high-accuracy automated workflows such as invoice processing and Excel transformation [9] Call to Action - Llama Index invites new users to join the millions already building with the platform [10] - Llama Index offers 10,000 in credits for new users to sign up and provide feedback [10]

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]

Technology & Data Solutions - 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]

Product Overview - Tensorlake transforms unstructured documents into RAG-ready data with a few lines of code [1] - It returns document layout, structured extraction, and bounding boxes [1] - The solution works on complex layouts, handwritten documents, and multilingual data [1] Target Audience - The information is relevant for individuals interested in Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) [1]

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]

AI and Automation in Unstructured Data Processing - AI is expected to significantly drive automation, potentially replacing Robotic Process Automation (RPA) [2][56] - The industry is moving towards decentralized, federated AI execution for automation [2][55][56] - Enterprises are exploring AI-driven automation for end-to-end workflows, potentially replacing RPA [62] Challenges and Solutions for Unstructured Data - Unstructured data is defined as anything that cannot be put into a nice database table for SQL queries, such as PDF documents or images [3][4] - Traditional techniques for processing unstructured data, like templates and rule-based systems, are brittle and unreliable [7][8] - A key challenge is ensuring reliability, completeness, and accuracy when using Large Language Models (LLMs) for unstructured data processing, especially in critical decision-making processes [18][19][24][25] - The industry emphasizes the need for complex, explainable, and auditable workflows to guarantee accuracy when using LLMs with unstructured data [24] Enterprise Adoption and Requirements - Enterprises prioritize data safety, security, auditability, and predictability when adopting AI solutions [42][43] - Predictability of errors is more important than achieving 100% accuracy; enterprises need to know which cases require human review [28][30][31] - Enterprises are adapting their acceptance criteria for AI, focusing on improvements over human performance rather than absolute perfection [27] The Role of AI Agents - AI agents can assist during the build or compile time by generating initial drafts of workflows, but runtime execution should remain deterministic and auditable [48][49][50][65][66] - The industry views autonomous agents as a compile-time phenomenon, where they aid in creating artifacts for deterministic runtime execution [49] Transforming Customer Experience - AI is enabling new, conversational customer interactions, such as lending over WhatsApp [36] - AI can transform processes like insurance claims and account openings, making them more interactive and user-friendly [37][38][39]