NYSE: AI

Enterprise AI Software Solutions

We have built a solution that enables our customers to rapidly develop, deploy, and operate large-scale Enterprise AI applications. Customers can deploy C3 AI software on major public cloud infrastructures, private cloud or hybrid environments, or directly on their servers and processors. We provide our customers and partners with an antidote to AI vendor lock-in.

We have five core product areas, which we collectively refer to as our “C3 AI Software”:

•The C3 Agentic AI Platform, our core technology, is a comprehensive, end-to-end application development and runtime environment that is designed to allow our customers to rapidly design, develop, and deploy Enterprise AI applications. The C3 Agentic AI Platform enables the creation of enterprise-grade AI agents that can autonomously perceive data, reason over complex systems, and take action to achieve defined business goals. These agents operate within secure, governed workflows and integrate seamlessly across the enterprise, delivering trusted, high-impact outcomes at scale.

•C3 AI Studio, is the integrated development environment in the C3 Agentic AI Platform that enables engineers, data scientists, and increasingly business analysts, to design, build, test, and deploy AI applications.

•C3 AI Applications, built using the C3 Agentic AI Platform, is a portfolio of pre-built, extensible, industry-specific and application-specific SaaS Enterprise AI applications that can be rapidly installed and deployed.

•C3 Generative AI, combines the utility of LLMs, agentic AI, generative AI, reinforcement learning, natural language processing, and the C3 Agentic AI Platform to reflect, collaborate, and execute complex workflows.

•C3 Code, enables users to build, configure, and deploy a complete, production-grade Enterprise AI applications automatically — including data pipelines, AI models, business logic, security controls, and user interfaces — using natural language instructions. C3 Code orchestrates multiple AI agents working in parallel against an organization's enterprise data, designed to significantly reduce the time and specialized resources required to develop and deploy enterprise AI applications.

How the Products Work Together

•The C3 Agentic AI Platform is the infrastructure layer; all other products run on it and inherit its security, scalability, and data integration.

•C3 AI Studio is the creation layer where engineers and analysts build, customize, and maintain applications.

•C3 Code is the acceleration layer that replaces large amounts of manual work in Studio with automatically generated applications.

•C3 AI Applications are the ready-made solutions layer, encoding C3.ai's accumulated 15 years of domain knowledge.

•C3 Generative AI is the access layer — protected by our awarded AI Agents patent — letting any employee extract value from all the layers.

Together, these five layers represent C3.ai's answer to the central challenge of Enterprise AI: how a large organization moves from raw data to reliable, governed, scalable AI applications that improve business outcomes — quickly, safely, and without building from scratch.

C3 Agentic AI Platform

C3 Agentic AI Platform is the core technology on which every C3 AI product runs — a Platform-as-a-Service analogous to the operating system of a smartphone: every application depends on it, but most users never see it directly. Its central innovation is the patented C3 AI Type System, a model-driven architecture. Rather than writing custom code to connect each enterprise data source, a developer describes the business objects they care about — an Asset, an Alert, a Work Order — and the platform generates all the integration plumbing automatically. We measure this to reduce application development time by roughly 25× versus building from scratch.

The C3 Agentic AI Platform uses a patented model-driven architecture to accelerate delivery and reduce the complexities of developing Enterprise AI applications. The C3 AI model-driven architecture provides an “abstraction layer,” that allows developers to build Enterprise AI applications by using conceptual models of all the elements an application requires, instead of writing lengthy code. We believe this provides significant benefits, including:

Scale AI Across the Business. Customers can use AI applications and models that optimize processes for every product, asset, customer, or transaction across all regions and businesses;

Deliver Results Faster. Customers can deploy AI applications and see results within two quarters and rapidly roll out additional applications and new capabilities;

Generate Meaningful Value. Customers can unlock sustained value, up to hundreds of millions to billions of dollars per year, from reduced costs, increased revenue, and higher margins; and

Govern AI with Confidence. Customers can ensure systematic, enterprise-wide governance of AI with our unified platform that offers data lineage and model governance.

The C3 Agentic AI Platform enables us and our customers to develop Enterprise AI applications by using conceptual models of all the elements required by the application — including data objects (e.g., customer, order, contract), computing resources (e.g., database, storage, messaging), data processing services (e.g., stream processing, batch processing), AI and ML services (e.g., model training, model pipeline management) — instead of having to write complex, lengthy code. This approach vastly reduces technical complexity for developers and the amount of code they need to write. The C3 Agentic AI Platform provides comprehensive capabilities to rapidly develop, deploy, and operate Enterprise AI applications at scale, including:

•Data unification across hundreds of enterprise systems and industrial sensors simultaneously.

•AI/ML lifecycle management for training, testing, deploying, monitoring, and retraining models at industrial scale.

•Agentic AI orchestration coordinating multiple autonomous software agents through multi-step workflows.

•Cloud-agnostic deployment on Azure, AWS, GCP, or on-premises FedRAMP-authorized government clouds.

•Enterprise-grade governance with role-based access, audit trails, and model explainability for regulated industries.

C3 AI Studio

C3 AI Studio is the integrated development environment engineers, data scientists, and increasingly business analysts use to design, build, test, and deploy applications on the platform. It is the equivalent of Microsoft Word for writing documents, except here the document is an enterprise AI application. By enabling less-technical staff to build applications, and by embedding a generative AI assistant for professional developers, Studio compresses time from business problem to deployed solution - a key driver of customer satisfaction and renewal rates. Key components of C3 AI Studio include:

Visual Studio Code Extension. A dedicated Visual Studio Code extension that enables developers and data scientists to work within a familiar integrated development environment (IDE), enriched with C3.ai-specific tooling and autocomplete.

Application Canvas. Low-code drag-and-drop design surface allowing non-engineers to build applications.

C3 Generative AI. Generative AI capabilities that allow users to describe desired application behaviors in natural language.

Data Studio and Auto Machine Learning (ML). Integrated data profiling, cleansing, and connectivity tools, together with automated machine learning capabilities designed to accelerate the development and deployment of predictive models.

C3 AI Applications

C3 AI Applications are pre-packaged, industry-specific AI products that customers license and deploy without building AI capabilities from scratch — analogous to buying a professionally designed home rather than building from raw materials. Every application runs on the platform and is maintained in Studio, so platform improvements automatically flow to all applications and new generative AI capabilities can be added rapidly.

Asset Performance

C3 AI applications for asset performance improve asset reliability, reduce downtime, and increase process efficiency. C3 AI customers use these applications to identify and predict asset performance risks, intervene before downtime occurs, and maximize asset performance.

These application offer a flexible and scalable AI approach with better precision than alternatives. C3 AI’s value proposition within reliability emphasizes its (1) complementary approach to existing asset management and data historian systems, (2) detailed asset hierarchy modeling, including asset templates and failure mode libraries, (3) flexible AI pipelines that leverage best-in-class ML frameworks with AI explainability, and (4) comprehensive user workflows to action AI recommendations, with bidirectional integrations to work management and operations systems.

C3 AI offers five discrete applications for asset performance:

•C3 AI Reliability increases operations, process, and equipment uptime by anticipating equipment risks and failures.

•C3 AI Process Optimization improves production rate and product quality with AI-optimized process control parameters in complex batch, semi-batch, or process manufacturing.

•C3 AI Energy Management helps operations teams achieve targets for energy cost, GHG emissions, water consumption, and waste reduction. The application models energy efficiency and emissions at every level of industrial processes from the individual equipment up to the facility as well as SKU-level product carbon footprints.

•C3 AI Asset Connect enables industrial and manufacturing teams to rapidly create digital representations of physical assets and contextualize data to build scalable AI foundation.

•C3 AI Field Services helps field teams to restore and maintain critical assets safely and efficiently in complex industrial environments in a mobile-first AI module.

Supply Chain

C3 AI applications for supply chain significantly improve resiliency and efficiency with proactive risk mitigation and advanced optimization. C3 AI’s value proposition in supply chain emphasizes its (1) strength in data unification for enterprise and external data to enable near-real-time global visibility of all goods, orders, and transportation, (2) detailed part-level tracking across the supply chain, (3) advanced AI to preemptively detect and mitigate risks, optimize processes, and avoid disruptions, and (4) fully complementary approach with enterprise resource planning, or ERP, systems (e.g., SAP ERP) and supply chain planning tools (e.g., SAP IBP, Logility).

Customers rely on these applications to rapidly improve business outcomes while providing flexibility in how they manage their entire supply chain software ecosystem. One large global manufacturing C3 AI customer uses C3 AI’s production scheduling software to support facilities using mainframe systems, continuing to use the same C3 AI Software while the underlying systems are upgraded to SAP ERP, highlighting the versatility and future-proofing of the model-driven architecture.

C3 AI offers six applications for supply chain:

•C3 AI Supply Network Risk identifies emerging inbound and outbound risks across the network.

•C3 AI Inventory Optimization analyzes variability across demand, supply, and production and optimizes inventory levels of all goods to eliminate excess inventory.

•C3 AI Demand Forecasting provides AI-based demand segmentation and granular, precision demand forecasts by capturing all high-value demand signals from enterprise and external data sources.

•C3 AI Production Schedule Optimization improves production efficiency using a holistic view of demand, supply, manufacturing, and distribution.

•C3 AI Sourcing Optimization reduces sourcing costs by detecting pricing anomalies and proactively monitoring all sourcing activity, based on feedback and other information provided from our customers.

•C3 AI Supply Chain Orchestration provides supply chain planners and leaders with real-time, end-to-end visibility and advanced decision support.

Defense & Intelligence

C3 AI application for Defense & Intelligence help maximize mission capabilities. C3 AI customers span the U.S. Department of War, or the DoW, (including branches such as the U.S. Air Force), the Missile Defense Agency, and the DoW’s Chief Digital and Artificial Intelligence Office. C3 AI offers a core set of products adaptable for each agency’s needs.

C3 AI differentiates on scalability of AI/ML and user workflows to solve critical missions. C3 AI’s defense & intelligence customers solve the following core needs with C3 AI: (1) rapid, multi-source data ingestion (e.g., structured, image, video, text), (2) efficient and scalable application of AI, ML, and deep learning techniques to provide novel insights, and (3) user-driven workflows that support investigative analyses, collaboration, and what-if scenario management.

C3 AI offers four applications for defense and intelligence:

•C3 AI Readiness, today configured across over 15 aircraft platforms, applies AI and advanced ML to help reduce unscheduled maintenance, preposition spare parts, and increase mission capability.

•C3 AI Intelligence Analysis accelerates investigative timelines with encrypted, obfuscated, federated search on people and relationships leveraging near real time, configurable machine learning pipelines for entities and sentiments.

•C3 AI Decision Advantage improves domain awareness and force management by synthesizing multiple intelligence sources in near real-time and enabling commanders and other decision makers with AI insights.

•C3 AI Contested Logistics ensures supply network resilience and availability in contested environments with extensive planning tools, near real-time monitoring and mission support, and AI assisted risk mitigation and contingency planning.

Property Appraisal

C3 AI Property Appraisal brings the power of Enterprise AI to state and local governments, helping maximize tax revenues by providing highly precise property appraisals. C3 AI Residential Property Appraisal and C3 AI Commercial Property Appraisal provide data unification across numerous disparate systems and create highly defensible property valuations, reducing the cost and time of real property appraisal.

C3 Generative AI and C3 Agentic AI

C3 Generative AI brings ChatGPT-style conversational intelligence to the enterprise, with the critical difference that answers are grounded in the customer's own proprietary data, not just general internet knowledge. A refinery engineer can ask why a pump is behaving erratically and receive a coherent, source-cited answer in seconds, synthesized from sensor readings, work orders, OEM manuals, and inspection reports — a task that previously required hours of expert manual data gathering.

C3 Generative AI is a unified knowledge source that enables enterprise users to rapidly locate, retrieve, and act on enterprise data and insights through an intuitive search and chat interface. By combining state-of-the-art foundational large language models (LLMs), deep learning retrieval models, and the C3 Agentic AI Platform, C3 Generative AI serves as a deep domain knowledge source to support information retrieval across disparate datasets and improve decision-making.

C3 Generative AI helps users orchestrate AI agents to retrieve data, analyze information, surface insights, and initiate workflows for demanding, high value enterprise use cases that require accurate and reliable performance. C3 AI provides over 30 Generative AI applications across industries, business processes, and enterprise systems.

C3 Generative AI is built natively into the C3 Agentic AI Platform and can be embedded into any C3 AI Application, enabling enterprise users to interact with their data, applications, and workflows through natural language search, chat, and conversational interfaces.

C3 Generative AI is built to support demanding enterprise requirements. Unlike the conventional approach of fine-tuning LLMs with enterprise data, by separating the LLM from enterprise data, C3 Generative AI can provide deterministic responses with full traceability to the exact source, support granular enterprise access control requirements, and reduce the risk of LLM-caused information leakage.

C3 Generative AI works in concert with C3 Agentic AI to extend these capabilities from information retrieval to autonomous action. C3 AI Agentic Process Automation provides intelligent, reliable automation for enterprise processes with minimal human intervention. Going beyond scripted rules, C3 AI Agentic Process Automation combines rules-based automation with the reasoning capabilities of AI agents to encapsulate entire business and operational processes. Agentic workflows can be declared using natural language, with no code required, and can be executed on a schedule, on-demand, or on-event.

Powered by C3 AI’s patented agent orchestration technology, C3 Generative AI enables autonomous agents to reflect, collaborate, and execute complex, multi-step workflows across disparate enterprise and external data sources. The C3 AI Dynamic Planning Agent can be deployed into any application context to coordinate with other agents and solve complex tasks, while specialized agents — such as the C3 AI Deep Research Agent for long-form work like document generation — orchestrate effort across multiple agents to accelerate high-value outcomes.

We believe C3 Generative AI is unique in the market, offering:

•LLM-agnostic architecture: Customers can choose or switch among OpenAI, Google, and other models without rebuilding applications.

•Retrieval-Augmented Generation (RAG): C3 AI's proprietary technique that grounds every answer in source documents with citations, preventing hallucination.

•AI Agents (Patented): Autonomous software workers that plan and execute multi-step tasks. The awarded patent protects the orchestration architecture.

•Enterprise-grade guardrails: Role-based access controls ensure users see only authorized data, even through a conversational interface.

•Omni-modal parsing, extracts high-quality content and metadata from wide array of unstructured formats — including presentations, spreadsheets, rich text, audio, and video — transforming them into a structured knowledge graph.

•The C3 AI Dynamic Planning Agent with multi-agent collaboration allows the C3 AI’s planning agent to perform multi-step reasoning across all data types, coordinate with other agents to solve complex tasks and workflows.

•Easy agent and tool authoring streamlines developer experience, enables users to rapidly create or enhance agents by integrating new tools in minutes, without the need for system upgrades.

•On-the-fly Custom Visualizations generates context specific visualizations from natural language queries.

•Streamlined omni-modal data integration through a visual administrative interface, allowing queries across multiple sources such as Snowflake, Oracle, Databricks, and others, and documents in Amazon S3, Google Cloud, and others.

•Proprietary, fine-tuned foundation models for more capable, accurate, and faster structured data queries.

•Automatic support for questions and answers in over 130 languages.

•Dynamic configuration of LLMs and retrievers with no code, including automatic updates to related retrieval configurations, baseline prompts, and orchestration parameters.

•Advanced support for querying images and data tables embedded in documents.

•Enhanced accuracy with automated topic modeling and extraction of metadata from data contained in documents.

•Availability of all core product features in air-gapped environments.

•Advanced orchestration for complex queries involving multiple LLMs and retrieval tools.

•Improved C3 Generative AI co-pilot LLM to accelerate the productivity of developers and data scientists on the C3 Agentic AI Platform.

•Full extensibility to meet the unique requirement of the enterprise.

C3 Code

C3 Code is the most dramatic leap forward in our product history. C3 Code is the Company’s agentic development environment embedded within the C3 AI Studio on the C3 Agentic AI Platform. It enables users to build, configure, and deploy a complete, production-grade Enterprise AI applications automatically — including data pipelines, AI models, business logic, security controls, and user interfaces — using natural language instructions. C3 Code orchestrates multiple AI agents working in parallel against an organization's enterprise data, designed to significantly reduce the time and specialized resources required to develop and deploy enterprise AI applications.

C3 Code is built on C3 AI’s patented agent-orchestration technology — the same foundational intellectual property that underpins C3 Generative AI — providing defensible protection around the autonomous, multi-agent capabilities common to both products. C3 Code works as follows:

•The user describes the application needed (e.g., "Analyze my global in-process inventory and show me where my most critical shortages are").

•C3 Code's agents interpret the request against the customer's enterprise data context — ERP, supply chain, logistics feeds.

•Multiple agents work simultaneously: one builds the data model, one configures the ML pipeline, one builds the user interface, one sets up security and access controls.

•Within hours a production-grade application is generated — with live data integration, dashboards, AI scoring, and role-based access — ready for technical review and deployment.

C3 Code differs from general-purpose AI coding tools like GitHub Copilot in two ways. First, it starts from pre-built Application Packages encoding deep knowledge of how real enterprises operate in manufacturing, energy, and defense — so generated code reflects industrial workflows, not generic software patterns. Second, the applications it produces are deployed through C3.ai's enterprise pipelines with security, audit trails, and governance built in from the start; the generated code is fully portable and customers are not locked to any specific AI model or cloud provider.

Lighthouse Customers

Our market-entry strategy has been to establish high-value customer engagements with large global early adopters, or lighthouse customers, across Europe, Asia, and the United States spanning a range of industries. These lighthouse customers serve as proof points for other potential customers in their respective industries. We have established strategic relationships with large multinational corporations and government entities and commonly enter into enterprise-wide agreements spanning multiple operating units or divisions.

The core of this strategy is to deliver high-value outcomes at scale across multiple industries, including manufacturing, defense, oil and gas, utilities, chemicals, healthcare, life sciences, and government. We structure these engagements as initial production deployments, or IPDs, to initiate and expand customer relationships across sectors. The U.S. Federal government, including defense, intelligence community, and civilian agency customers, has emerged as a significant and growing segment of our business. Our partner ecosystem, including Microsoft, Amazon Web Services, McKinsey & Company, Baker Hughes, Booz Allen Hamilton, and more, serves as a primary channel for customer acquisition and expansion.

High-Value Outcomes

We are enabling the digital transformation of many of the world’s leading organizations and, in the process, helping them to attain short time-to-value and exceptionally high economic returns. At some companies, based on feedback and other information provided from our customers, we estimate our solutions have helped return billions of dollars in economic benefit. We have focused on executing a more selective number of IPD agreements, reflecting our strategic focus on engagements with a higher probability of delivering targeted economic value to customers and a greater likelihood of conversion into production contracts.

Rapid Time to Value

The key to our market success and our primary competitive differentiator is our ability to leverage the C3 Agentic AI Platform, C3 AI Applications, and C3 Generative AI to bring high-value Enterprise AI applications into production use rapidly. With C3 Code and its agentic development capabilities, users can now describe a use case, feature, or application in natural language and have production-grade Enterprise AI applications designed, built, configured, and deployed in a fraction of the time previously required – in some cases within hours rather than weeks.

C3 AI Sales Cycle

Our typical sales cycle begins with one or more product and technical presentations about C3 AI, leading to a mapping of our capabilities to customer use cases. This process is increasingly initiated and advanced through our partner ecosystem. Following use case mapping, we typically sign a paid IPD agreement for the C3 Agentic AI Platform, a C3 AI Application or C3 Generative AI, and C3 AI Center of Excellence (COE) including support services, generally lasting up to six months. During that period, we work with the customer to deploy a production-level C3 AI Application. Our customer base includes large organizations across oil and gas, power and utilities, aerospace and defense, industrial products, life sciences, financial services, and government sectors, including U.S. Federal agencies.

Following a successful IPD, customers may continue to license the C3 AI Application and the C3 Agentic AI Platform for a consumption-based fee arrangement or enter into a time-certain multi-period commitment that may include consumption charges. Over time, our customers typically expand usage by adding users, expanding their use of the initial application to another use cases, purchasing additional C3 AI Applications for a subscription fee and by developing their own AI applications on the C3 Agentic AI Platform, which increases consumption-based fees as usage scales. C3 AI continues to support our customers through software updates and COE support services, as needed.

Partner Ecosystem

C3 AI’s Enterprise AI expertise and technology combined with our partners’ deep domain expertise enhances our solutions to joint customers. We have made significant progress establishing and extending productive partnerships. Our partner ecosystem is effective at opening new doors with new customers and expanding product offerings with existing customers.

C3 AI and Microsoft maintain a multi-year global alliance to accelerate Enterprise AI adoption. The partnership encompasses deep collaboration across product innovation and integration, marketing and sales, and customer success, with a shared focus on critical industries including manufacturing, healthcare, energy, and defense. C3 AI and Microsoft have aligned their global sales teams and field organizations to streamline enterprise engagements, enable joint account planning and co-selling, and expand adoption at scale. C3 AI products are integrated into Microsoft’s commercial marketplace incentive programs, allowing Azure sellers to receive quota retirement and compensation when transacting C3 AI solutions. All C3 AI Applications are available in the Azure Marketplace.

C3 AI and AWS maintain a multi-year strategic collaboration agreement focused on accelerating Enterprise AI solution delivery and expanding go-to-market efforts across a variety of industries.

C3 AI and Google Cloud maintain a global partnership that encompasses joint selling activities and integration between C3 AI Applications and Google Cloud services. All C3 AI Applications have been optimized to run in the Google Cloud Platform environment and are available in the Google Cloud Marketplace.

C3 AI and McKinsey & Company maintain a strategic collaboration to accelerate Enterprise AI transformations at scale. The alliance combines McKinsey's AI practice, QuantumBlack, with C3 AI's Enterprise AI software to help clients realize operational improvements and unlock new growth opportunities through agentic AI.

C3 AI and Booz Allen maintain a strategic partnership focused on delivering AI solutions to the government, defense, and intelligence sectors. The companies jointly go to market with the C3 Agentic AI Platform and suite of pre-built C3 AI Applications, and have closed multiple engagements with the Department of War's Chief Digital and Artificial Intelligence Office and other federal organizations.

C3 AI and Baker Hughes maintain a multi-year strategic partnership under which Baker Hughes serves as reseller of C3 AI software in the oil and gas industry and a non-exclusive reseller in other industries. The partnership was most recently renewed and expanded in April 2025, reinforcing joint co-selling efforts, co-investment in AI solutions, and scaled deployments focused on improving production efficiency, reducing downtime, and increasing operational visibility across assets.

C3 AI and Fractal maintain a services and go-to-market partnership encompassing customer service engagements, co-marketing, and co-selling activities. Fractal has committed to building a dedicated C3 AI practice of trained engineers and data scientists. The partnership was most recently extended in April 2026, continuing joint efforts to implement, extend, and scale C3 AI solutions for customers.

C3 AI and Cathexis maintain a partnership focused on accelerating the delivery of AI applications for the U.S. Intelligence Community, with an expanding team of dedicated Cathexis staff supporting joint selling and delivery efforts. The partnership was most recently extended in April 2026, continuing joint efforts to implement, extend, and scale C3 AI solutions for customers.

C3 AI and Capgemini maintain a partnership under which Capgemini has established a dedicated global C3 AI practice to deliver scalable Enterprise AI solutions for joint clients across industries.

C3 AI and PwC maintain a strategic alliance to deploy AI-powered business transformation at enterprise scale. The alliance combines PwC's consulting and implementation capabilities with C3 AI's prebuilt AI applications, with an initial focus on manufacturing, energy, and financial services.

C3 AI and Cognizant maintain a strategic alliance to deploy and scale Enterprise AI across industries, with an initial focus on healthcare and financial services.

Sales Model

Our sales organization is organized both geographically and in vertical market sales units that cooperate to sell to and service customers. We have a highly leveraged go-to-market model comprised of a global field sales force combined with significant alliance partnerships. Each of our strategic partners — including Microsoft, AWS, Google Cloud, McKinsey & Company, and Baker Hughes — has a large installed customer base with strong, established relationships, and a large global sales force that vastly extends our market coverage. We form specific sales targets and goals with each partner, enabling us to quickly and efficiently engage in customer accounts.

Early on, we focused on the oil and gas, federal, aerospace and defense, energy and utilities, manufacturing, and financial services sectors, as those industries were early adopters in Enterprise AI. We have since expanded into state and local governments, agriculture and forestry, pharmaceuticals, chemicals, professional services, telecommunications, logistics and transportation, and hospitals and healthcare and are seeing increased industry diversity in our sales pipeline and initial production deployment engagements. Our goal is to rapidly move down-market in the coming years to serve the small and medium business segments of each industry.

Revenue Model

Our revenue consists of software subscription and professional services revenue. The substantial majority of our revenue is generated from subscriptions to our software.

Subscriptions

Our subscription revenue is primarily comprised of software licenses, software-as-a-service offerings, stand-ready COE support services, initial production deployments of our C3 AI Applications or Generative AI, and runtime and hosting fees. Licensing of our software grants our customers the right to use our software, either on C3-hosted environments, on their own cloud instance or their internal hardware infrastructure, over the contractual term. We offer a premium stand-ready service through our COE. Sales of our software-as-a-service offerings include a right to use our software over the contractual term. Customers may pay a usage-based runtime fee for our C3 AI Software for specified levels of capacity. Our subscriptions also include our maintenance and support services, which include critical and continuous updates to the software that are integral to maintaining the intended utility of the software over the contractual term. We also provide software licenses that do not require maintenance and support services, for which revenue is recognized when the control of the software is transferred to the customer.

Within subscription revenue, we include revenue from contracts with customers that are based on our consumption-based pricing model. C3 AI generally engages with customers through a paid “Initial Production Deployment” phase of generally up-to six months that may include developer access to the C3 Agentic AI Platform, one or more C3 AI Applications or C3 Generative AI and COE support services with typically unlimited runtime. Following the initial production deployment period, customers either pay a monthly fee and consumption charges using vCPU and vGPU hours as the metric to calculate payment or enter into a time-certain multi-period commitment that may include consumption charges. We also charge the customer any hosting fees incurred by us for hosting in our own cloud instance.

Professional Services

Our professional services primarily include prioritized engineering services, paid implementation services, consulting and training. We maintain a professional services organization that offers resources, methodologies, and experience to help customers develop and deploy enterprise-scale AI applications. Our services are complemented by those of our partners. Our professional services strategy is to quickly train our customers to develop, customize, and deploy applications independently of us, rapidly making them self-sufficient.

Prioritized engineering services are undertaken at the request of customers to accelerate the development of software features in C3 AI Software products.

C3 AI consulting and implementation services help ensure successful customer outcomes throughout the application development and deployment phases, including setup and configuration, ML model development and tuning, and integration of multiple complex source systems.

In instances where a large or continuing professional services presence may be desired or necessary, we generally rely upon our partner ecosystem to provide those services. This enables us to maintain high gross margins and allows us the flexibility to rapidly deploy trained professional services personnel at large scale throughout the world.

Marketing

Our multichannel marketing function is focused on market education, thought leadership, account-based marketing, and demand generation. We engage the market through digital, print, and social media, virtual and physical events, including C3 Transform, our annual international user, executive, and AI thought leadership conference, and other livestreamed events featuring C3 AI customers, C3 AI partners, and C3 AI experts in AI, ML, and data science. Our Chief Executive Officer and Chairman, Tom Siebel — a recognized technology thought leader and author of the 2019 Wall Street Journal best seller, Digital Transformation: Survive and Thrive in an Era of Mass Extinction —has been a frequent industry keynote speaker and is often interviewed by leading media, including The Wall Street Journal, The Financial Times, The Economist, Fortune, Forbes, CNBC, BloombergTV, and Yahoo! Finance.

Rich Human Capital

Our strongest asset is unquestionably the human capital that we have been able to attract, retain, and motivate. We attract exceptionally talented, highly educated, experienced, motivated employees.

We have built a culture of high performance based on four core values:

•Drive and Innovation Propelling Growth. We self-select for people who love to work hard, think with rigor, speak with purpose, and act to achieve great things.

•Natural Curiosity to Solve the Impossible. We are self-learners, always seeking knowledge to accelerate innovation.

•Professional Integrity Governing All Endeavors. We comport ourselves with unwavering ethical integrity, respect, and courtesy.

•Collective Intelligence. We believe the unity of our team is substantially greater than the sum of its parts.

As of April 30, 2026, we had 764 full-time employees, with 582 based in the United States and 182 in our international locations.

Our Culture of High Performance

We are dedicated to achieving our mission to accelerate digital transformation of organizations globally by enabling the deployment of Enterprise AI at scale. Our people are domain experts in their respective fields. We are individuals with exceptional education and professional backgrounds. We are uncompromising in the quality of our work product. We build relationships with our customers grounded upon the highest levels of business ethics and professionalism, with a laser focus on customer success. We execute with precision.

Recognized Enterprise AI Industry Leadership

We believe we are broadly recognized as a leader in Enterprise AI with many other industry recognitions, including Verdantix (2025), The Financial Times’ The Americas’ Fastest Growing Companies (2025), and have been named to the Constellation ShortListTM for Cloud-Based Data Science & Machine Learning Platforms (2025), the Constellation ShortListTM for Cloud-Based Data Science & Machine Learning Platforms (2024), Constellation ShortList™ for Artificial Intelligence & Machine Learning Cloud Platforms (2024), Constellation ShortList™ for Artificial Intelligence & Machine Learning best-of-Breed Platforms (2024), a Leader in the Forrester WaveTM: AI/ML Platforms (2024), Fortune 50 AI Innovators (2023), IDC MarketScape: Solutions for Industrial Platforms and Applications in Energy (2021) CNBC Disruptor 50 (2020), BloombergNEF Pioneer (2020), and Forbes Cloud 100 (2020).

Sustainable Competitive Advantage: C3 AI Model-Driven Architecture

Our core technology is a cohesive family of integrated software services developed over a decade, engineered with a proprietary model-driven architecture, that provides all the software services and microservices necessary and sufficient to rapidly develop and deploy Enterprise AI applications.

AI applications developed with the C3 Agentic AI Platform can leverage any open-source software solutions and all of the cloud services of AWS, Microsoft Azure, Google Cloud, and can operate on any of these cloud platforms, on-premises, or in a hybrid cloud.

Compared to the structured programming approach that most organizations typically attempt, our model-driven architecture with declarative programming accelerates development by a factor of 26, while reducing the amount of code that must be written by up to 99%.

The big data and application demands of enterprise-scale AI applications require numerous underlying interdependent elements. These include enterprise data, extraprise data, sensor data, data persistence services, data streaming services, messaging services, analytics services, ML services, security services, data visualization, application development services, application monitoring services, and scores to hundreds more. With a traditional structured programming approach, developers spend significant time and effort to write extensive code to define, manage, connect, and control each element. This often results in overwhelming complexity and highly brittle applications that can break any time an underlying element is changed or updated — we believe this is a primary reason why the vast majority of AI efforts have not been deployed into production at enterprise scale.

By contrast, our model-driven architecture provides an abstraction layer, that allows our partners and our customers, as well as our internal C3 AI developers, to build or customize Enterprise AI applications by using conceptual models of all the elements an application requires. C3 AI provides a library of tens of thousands of prebuilt conceptual models that can be easily modified and extended, and developers can efficiently create their own models as well. These prebuilt, extensible models encompass a vast range of business objects (e.g., customer, order, contract), physical systems and subsystems (e.g., engine, boiler, chiller, compressor), computing resources and services (e.g., database, stream processing) — virtually anything an application requires can be represented as a model in our model-driven architecture. To ensure ongoing operability of our thousands of prebuilt and extensible models on different underlying infrastructure (e.g., AWS, Google Cloud, Microsoft Azure), our automated testing continuously executes approximately 60,000 tests and security scans with each change or update we make to our software or infrastructure.

Leveraging this model-driven architecture, application developers and data scientists can focus on delivering immediate value, without the need to manage the complex interdependencies of the underlying elements. These conceptual models can be reused by many applications, thereby accelerating development of new applications.

We believe our model-driven architecture and declarative programming approach provides significant competitive advantage both by enabling our customers and partners to successfully develop and deploy Enterprise AI applications faster, and by providing the foundation for C3 AI to rapidly extend our portfolio of cross-industry and industry-specific applications.

The significance of this architectural advantage is best understood against the backdrop of how enterprise computing has evolved. Over the last four decades, the IT industry has transitioned from mainframe computing to handheld computing. The software industry has transitioned from custom applications based on mainframe standards to applications developed on a relational database foundation, to enterprise application software, to SaaS and mobile apps, and now to the agentic AI-enabled enterprise, in which autonomous AI agents increasingly initiate and execute work across the organization.

The challenges that must be addressed to enable today’s Enterprise AI applications are nontrivial, as are the array of capabilities and services necessary for building and operating these applications at scale. To develop an effective Enterprise AI application, it is necessary to ingest and aggregate data from a variety of enterprise information systems, sensors, markets, and products to provide a complete view of the enterprise. In addition, the data need to be processed at the rate they arrive, in a highly secure and resilient system that addresses persistence, event processing, ML, and visualization. This requires a massive, horizontally scalable elastic distributed processing capability offered only by modern cloud platforms and supercomputer systems. The resultant data persistence requirements are staggering.

To understand this challenge, consider just a few of the requirements needed to support Enterprise AI applications:

•Data Integration. A prerequisite to AI at industrial scale is the availability of a unified, federated image of all the data contained in the multitude of (1) internal data, including enterprise information systems (e.g., ERP, CRM, SCADA, HR, MRP) and sensor IoT networks; and (2) external data, including weather, terrain, satellite imagery, social media, trade data, biometrics, pricing, and market data.

•Data Persistence. The data aggregated and processed includes every type of structured and unstructured data imaginable, including personally identifiable information, images, text, video, telemetry, voice, and network topologies. As there is no one size fits all database optimized for all these data types, there is a need for a multiple database technologies.

•Platform Services. A myriad of sophisticated platform services are necessary for any Enterprise AI or IoT application. Examples include access control, data encryption in motion, encryption at rest, ETL, queuing, pipeline management, autoscaling, multitenancy, authentication, authorization, cybersecurity, time-series services, normalization, data privacy, GDPR privacy compliance, NERC-CIP compliance, and SOC2 compliance.

•Analytics Processing. The volumes and velocity of data acquisition in such systems are blinding and the types of data and analytics requirements are highly divergent, requiring a range of analytics processing services. These include continuous analytics processing, MapReduce, batch processing, stream processing, and recursive processing.

•Machine Learning Services. The whole point of these systems is to enable data scientists to develop and deploy ML models. There is a range of tools necessary to enable that, including Jupyter Notebooks, R Studio, Azure ML, Amazon Sagemaker, and Google Vertex AI. Increasingly important is an extensible curation of ML libraries such as PyTorch, TensorFlow, Keras, Hugging Face transformers, and XGBoost. An effective AI and IoT platform needs to support them all.

•Data Visualization Tools. Any viable AI architecture needs to enable a rich and varied set of data visualization tools including Microsoft Power BI, Google Data Studio, Looker, Tableau, and others.

•Developer Tools and UI Frameworks. An organization’s IT development and data science teams each have adopted and become comfortable with a set of application development frameworks and user interface development tools. An AI and IoT platform must support all of these tools — including Visual Studio, Jupyter Lab, JetBrains IDEs, React, Angular, and VueJS — or it will be rejected as unusable by the IT development teams.

•Open, Extensible, Future-Proof. The current pace of software and algorithm innovation is accelerating. An AI and IoT platform architecture must provide the capability to replace any components with next-generation improvements; in the era of generative AI, that means the constant stream of newly released and ever-more-powerful LLMs. Moreover, the platform must enable the incorporation of any new open source or proprietary software innovations without adversely affecting the functionality or performance of an organization’s existing applications. This is a level-zero requirement.

The C3 Agentic AI Platform — built with model-driven architecture — has been refined, tested, and proven in some of the most demanding industries and production environments from electric utilities and manufacturing to oil and gas and defense, comprising petabyte-scale datasets from thousands of vastly disparate source systems, massive volumes of high-frequency time series data from millions of devices, and hundreds of thousands of ML models.

Strategic Competitive IP Advantage

We enjoy a rich patent portfolio that is a substantial competitive advantage, both offensive and defensive, in the Enterprise AI market - most notably, U.S. patents (No. 10,817,530, No. 10,824,634 and No. 11,954,112) which were granted for systems and methods for data processing and enterprise AI applications. C3 AI was also awarded a foundational U.S. patent (US 12,111,859) for generative AI agents. Key patented technologies include AI Orchestrator, Autonomy, Multimodal Model Integration, Natural Language Summarization, and Traceability and Security.

Our patent portfolio covers the key capabilities of our model-driven architecture that are the foundation of our highly differentiated technology. This includes methods, systems, and devices for data aggregation and unification, times-series data processing, data abstraction, ML implementation, generative AI and much more.

As of April 30, 2026, our technology is currently protected by a broad patent portfolio, with 33 issued patents in the United States, 30 issued counterpart patents in a number of international jurisdictions, over 31 patent applications pending in the United States, and 74 patent applications pending internationally. Our issued patents expire beginning in 2031 through 2043. We continually review our development efforts to assess the existence and patentability of new intellectual property.

Intellectual property is important to the success of our business. We rely on a combination of patent, copyright, trademark, and trade secret laws in the United States and other jurisdictions, as well as license agreements, confidentiality procedures, non-disclosure agreements with third parties, and other contractual protections, to protect our intellectual property rights, including our proprietary technology, software, know-how, and brand. However, we believe that factors such as the technological and creative skills of our personnel, creation of new services, features and functionality, and frequent enhancements to our platform are more essential to establishing and maintaining our technology leadership position. See the section titled “Risk Factors - Risks Related to Our Intellectual Property” in Part I, Item 1A in this Annual Report on Form 10-K for a discussion of the risks associated with our intellectual property.

Awash in “AI Platforms”

Today the market is awash in AI solutions that provide component parts to design, develop, provision, and operate Enterprise AI applications, including Cassandra, Cloudera, DataStax, AWS IoT, and Hadoop. AWS, Microsoft Azure, and Google Cloud each offer an elastic cloud computing platform and an increasingly innovative library of microservices that can be used for data aggregation, ETL, queuing, data streaming, MapReduce, continuous analytics processing, ML services, and data visualization. An array of open-source software offerings cater to data management, machine learning services, and analytics. While these products are useful, we believe that none offers the scope of utility necessary and sufficient to rapidly design, develop and deploy Enterprise AI applications, nor the model-driven agentic foundation required to orchestrate and operate autonomous AI agents at enterprise scale.

“Do It Yourself” Enterprise AI?

Early in any software innovation cycle, companies often try to build the new technology themselves. As with the introduction of ERP and CRM software in prior cycles, many IT organizations first attempt to internally develop a general-purpose Enterprise AI and IoT platform, using open source software combined with microservices from cloud providers like AWS and Google Cloud.

The organization then assembles hundreds to thousands of programmers, often distributed worldwide, who use structured programming and APIs to stitch these disparate programs, data sources, sensors, ML models, development tools, and user interfaces into a unified platform for building and deploying enterprise-scale AI and IoT applications.

The complexity of such a system is much greater than developing a CRM or ERP system. There are a number of problems with this approach:

•Complexity. Using structured programming, the number of software API connections that one needs to establish, harden, test, and verify for a complex system can, in our estimation, approach the order of 1013. The developers of the system need to individually and collectively grasp that level of complexity to get it to work.

We believe the number of programmers capable of dealing with that level of complexity is quite small. Aside from the platform developers, the application developers and data scientists also need to understand the complexity of the architecture and all the underlying data and process dependencies in order to develop any application.

•Brittleness. Spaghetti-code applications of this nature are highly dependent upon each and every component working properly. If one developer introduces a bug into any one of the open source components, all applications developed with that platform may cease to function.

•Future Proof. As new libraries, faster databases, and new ML techniques become available, those new utilities need to be available within the platform. Consequently, every application that was built on the platform will likely need to be reprogrammed in order to function correctly. This may take months to years.

•Data Integration. An integrated, federated common object data model is essential, yet a structured, API-driven approach can require hundreds of person-years to build one for a large corporation. This is the primary reason tens to hundreds of millions of dollars are spent and, years later, no applications are deployed — a pattern of failure common across the Fortune 500.

These challenges only multiply in the agentic era, where building and operating autonomous AI agents adds orchestration, governance, and traceability demands that a do-it-yourself architecture cannot sustain. Data security compounds the difficulty: enterprise-grade access control, encryption, data isolation, and compliance controls must be applied consistently across every component, yet in a do-it-yourself architecture they are bolted on after the fact and only as strong as the weakest integration — a risk magnified as autonomous agents act on sensitive data with limited human oversight.

The C3 Agentic AI Platform: Model-Driven Architecture

Model-driven architecture emerged in the early 21st century in response to the growing complexity of enterprise application development. Although structured programming remains the state of the art for many applications, it breaks down at the complexity and scale of modern Enterprise AI and IoT applications. The C3 Agentic AI Platform is designed and built with a model-driven architecture to overcome that limitation.

Central to the architecture is the “model,” an abstraction layer that simplifies the programming problem. Rather than manage the data types, interconnections, and processes associated with a given entity (such as a customer, tractor, doctor, or fuel type), the developer simply addresses that entity’s model, which abstracts all of the underlying data, interrelationships, APIs, associations, and processes used to manipulate it.

Virtually anything can be represented as a model, including databases, natural language processing engines, and image recognition systems. Models also support inheritance: a “relational database” model can stand in for any relational system such as Oracle, Postgres, Aurora, Spanner, or SQL Server, while a “key-value store” model can incorporate Cassandra, HBase, Cosmos DB, or DynamoDB.

The platform delivers workflow-enabled AI solutions with advanced data fusion, governance, and scalable AI/ML operations. Unlike legacy stacks that bolt on AI capabilities, it has been purpose-built and refined over 15 years to rapidly develop and deploy enterprise-grade AI applications, leveraging the full AI technology stack, including silicon, cloud infrastructure services, and foundation models, for performance and flexibility.

C3 AI Reduces Complexity, Simplifies Development

Through its model-driven architecture, the C3 Agentic AI Platform provides an abstraction layer and semantics to represent the application, freeing the programmer from data mapping, API syntax, and the mechanics of computational processes such as ETL, queuing, pipeline management, and encryption.

An effective object model uses abstract models as placeholders to which a programmer links an appropriate implementation: a relational database model to Postgres, a report writer model to MicroStrategy, or a data visualization model to Tableau. As new open source or proprietary solutions become available, the object model library can simply be extended to incorporate them.

The model-driven architecture also future-proofs applications built on the platform: its modular design lets new, upgraded, or enhanced services be integrated readily, so organizations can take advantage of improved offerings as they become available.

Platform Independence: Multi-Cloud and Polyglot Cloud Deployment

Enterprises today often have a multi-cloud strategy. While corporate leaders are eagerly embracing the cloud, they are also very concerned about cloud vendor lock-in. They want to be able to continually negotiate. They want to deploy different applications in clouds from different vendors, and they want to be free to move applications from one cloud vendor to another.

Multi-cloud deployment is therefore an additional requirement of a modern model-driven software platform that is fully supported by the C3 Agentic AI Platform. Applications developed with the C3 Agentic AI Platform can run without modification on any cloud and on bare metal behind the firewall in a hybrid cloud environment.

A requirement for the new AI technology stack — that the C3 Agentic AI Platform delivers — is polyglot cloud deployment capability: the ability to mix various services from multiple cloud providers and to easily swap and replace those services. The cloud vendors provide the market a great service by enabling instant access to virtually unlimited horizontally scalable computing capacity and effectively infinite storage capacity at exceptionally low cost. As the cloud vendors aggressively compete with one another on price, the cost of cloud computing and storage is consistently decreasing.

C3 Agentic AI Platform: A Tested, Proven, and Patented AI Suite

The model-driven approach to developing Enterprise AI applications using the C3 Agentic AI Platform has been tested and proven in dozens of large-scale, real-world deployments at some of the world’s largest organizations.

The platform enables these and other leading organizations to develop and operate Enterprise AI applications at scale, with a fraction of the effort and resources other approaches require. Applications built on it are flexible, easily upgraded, and portable across cloud platforms with little or no modification, future-proofing customers’ investment in Enterprise AI and IoT development.

C3 AI was the first to file and receive a core patent for Agentic AI and its orchestration in enterprise applications. The platform supports high-accuracy, omni-model agentic capabilities for retrieval, reasoning, and decision-making, including our Dynamic Planning Agent, which performs multi-step data retrieval, reasoning, visualization, and action execution across diverse data types. This orchestration is powered by C3 AI’s metadata-based Object Models, which provide semantic understanding of enterprise data. Once data are integrated into C3 AI Objects, customers can use pre-built agents and tools to interact with, reason over, and act on the data, or integrate their own agents and tools within our framework.

The platform is LLM-agnostic and seamlessly supports both commercial and open-source LLMs, offering customers the flexibility to choose models that best meet their needs.

Competition

Our main sources of current and potential competition fall into several categories:

•corporate IT organizations that attempt to develop internal solutions for their enterprises;

•commercial enterprise and point solution software providers;

•open-source software providers with data management, ML, and analytics offerings;

•public cloud providers offering discrete tools and micro-services with data management, ML, and analytics functionality;

•system integrators that develop and provide custom software solutions;

•legacy data management product providers; and

•strategic and technology partners who may also offer our competitors’ technology or otherwise partner with them, including our strategic partners who may offer a substantially similar solution based on a competitor’s technology or internally developed technology that is competitive with ours.

Our primary competition is largely do-it-yourself, company-specific AI platforms built by internal IT organizations, typically by integrating internally developed tools, open source solutions, point solutions from independent software vendors, and components from AWS, Microsoft Azure, or Google Cloud, often managed as professional service projects by firms such as Accenture, Capgemini, Lockheed Martin, Booz Allen Hamilton, among others. These projects are costly and time-consuming, frequently fail, and, when successful, usually take years to realize economic return. Increasingly, these do-it-yourself efforts also incorporate frontier AI coding tools, which generate application code from natural-language descriptions but do not by themselves provide an end-to-end foundation for enterprise-scale Enterprise AI. Most of our customers have tried and failed at one or more such bespoke efforts, sometimes at great expense, before turning to C3 AI.

We believe no platform is directly competitive with the full end-to-end, model-driven scope of the C3 Agentic AI Platform, although a number of providers offer overlapping capabilities. The offerings formerly positioned as functionally equivalent, GE Predix and IBM Watson, were multibillion-dollar efforts backed by massive promotional campaigns, yet we no longer encounter them in competitive situations

Growth Strategy

We are investing in the expansion of our direct enterprise sales and service organization both geographically and across vertical markets to expand the use of C3 AI solutions within existing customers and establish new customer relationships.

The consumption-based pricing model helps us better meet the needs of our customers by making it easier and less costly to adopt our products and services. With the consumption-based pricing model, customers start with initial production deployments which provide developers access to the C3 Agentic AI Platform and C3 AI Applications, together with COE support services, for up to six months. After completing a successful initial production deployment, our customers may continue to license the C3 AI Application and the C3 Agentic AI Platform for a consumption-based fee or enter into a time-certain multi-period commitment that may include consumption charges.

After we help our customers solve their initial use cases, they frequently identify incremental opportunities within their operations and expand their use of our products. The increased engagement is measured by a combination of increased vCPU/vGPU usage, increased C3 AI Software subscriptions and subscriptions to the C3 Agentic AI Platform for in-house AI application development.

Demand for agentic AI is a key part of our growth strategy. Customers are increasingly adopting the C3 Agentic AI Platform and its agentic capabilities to build and operate autonomous AI agents across their operations, and we are working through a substantial pipeline of related opportunities. Our solutions are available on the Microsoft Azure, AWS, and Google Cloud marketplaces.

We continue to invest heavily in research and development to maintain technology leadership. Our product roadmap includes a wide range of new functions and products to be released in the coming years that we expect to contribute to revenue growth with both new and existing customers.

The Evolution of C3 AI

Like many of the world’s leading technology companies, C3 AI has changed and expanded its branding and product portfolios to achieve market leadership.

In January of 2009, we founded C3, Inc with the purpose of developing and marketing a software platform and family of software products that would enable companies to exploit the power of elastic cloud computing, big data, IoT, and predictive analytics.

When we founded C3 AI, we believed the market for elastic cloud computing, IoT, big data, and predictive analytics software was destined to be large. That proved true. However, in 2009 the market was nascent, and the specific applications and markets were unknown. Based on Forrester’s report on the Public Cloud Market Outlook, in 2008, the global public cloud market was less than $20 billion; in 2023, it was expected to approach $500 billion. In 2008, there were less than 1 billion IoT devices worldwide;1 in 2023, that number was expected to exceed 55 billion based on the IDC report published in 2023. In 2008, AI software - as we think about it today - did not exist. This year the AI software market is expected to exceed $450 billion based on the IDC report. We believe that by any standard that constitutes explosive growth.

When we consider mega-market developments like the internet, the smartphone, and AI, it is impossible to anticipate a priori exactly how these markets will develop. With the advent of the Mosaic internet browser in 1993, who could have anticipated Amazon and Google? With the founding of Apple Computer Company in 1976, who could have anticipated the iPhone? The Apple Store? Apple TV? iTunes? These mega-markets develop in unanticipated ways.

We believe that Enterprise AI is a mega-market event. As this market has developed, C3 AI has expanded its market offerings and continually expanding its market position to address the ever-expanding opportunity.

C3: 2009 - 2012

We founded C3 in January of 2009 and developed some of the core components of what is now the C3 AI Platform within the first year. There was much discussion and interest in the 2008 – 2011 timeframe about what we now consider sustainability initiatives, including clean tech, energy management, LEED certification, and cap and trade vs. carbon offsets; and as a result, we decided to focus our first use case on energy management. That proved to be a good decision.

In 2010 we released our first product, C3 Energy Management.

From 2010 – 2012, we closed several large agreements with a large global industrial company, one of the world’s largest chemical companies, two large utilities, and one of the world’s largest high-tech companies.

C3 Energy: 2013-2015

In 2012, C3 engaged McKinsey & Co. to conduct a study and make recommendations for maximizing growth including optimal company positioning and an associated pricing and product strategy. In the first two decades of the 21st century, utility companies were in the process of spending $2 trillion globally to upgrade their grid infrastructures with IoT devices, enabling the advent of the smart grid. Utilities were early adopters of IoT.

The McKinsey analysis recommended that there was a significant opportunity for C3 to expand its business by applying its energy management and energy efficiency solutions to utilities at grid scale in addition to selling to enterprises.

Adopting the McKinsey recommendations, C3 expanded its market position, rebranded as C3 Energy, and in addition to its prior solutions, C3 Energy offered a family of predictive analytics solutions - which were reliant on emerging AI techniques including machine learning, supervised learning, and unsupervised learning. Built to address the utility value chains of power generation, transmission, distribution, and consumption, these solutions optimize the operation of large and complex power grid infrastructures. The C3 Energy utility software products expanded to include C3 AMI Operations, C3 Revenue Protection, C3 Predictive Analytics, C3 Revenue Production, and C3 Reliability.

Many customers also licensed our core C3 Platform that they could use to develop their own predictive analytics application and/or to develop derivative works of the C3 Energy applications.

It was during this period that the company formed its data science division to develop and apply AI techniques to our applications including machine learning, predictive analytics, supervised learning, and unsupervised learning.

During this period, the company began to offer its products to the oil and gas industry including its AI Predictive Maintenance application for oil pumps, offshore oil rigs, LNG production facilities, etc. The company continued to offer its products for energy management and energy efficiency to utility companies based on per customer pricing and to enterprises based on expected value pricing.

1 https://www.statista.com/statistics/764026/number-of-iot-devices-in-use-worldwide/

This expansion into energy markets proved successful as the company booked approximately $83 million in contracts and recognized $63.9 million in revenue during this period.

C3 IoT: 2016 – 2018

By 2016, we were seeing significant expansion in the cloud computing market and the proliferation of IoT sensors was expanding dramatically across many industries. We were increasingly approached by manufacturing companies, financial services companies, oil and gas companies, and the U.S. Department of War to deploy the same types of AI applications that we had successfully deployed in enterprises and utilities including AI Predictive Maintenance, AI Fraud Detection, AI Inventory Optimization, and C3 Energy Management.

At that time, the common expression for these types of applications was “IoT,” and we appropriately rebranded the company as C3 IoT to communicate to the market that we were again expanding our market offerings from primarily one vertical market (energy) to a broadening range of markets.

In response to this increased demand, the company tailored its core applications to meet the needs of those industries. As such, in addition to the C3 Platform, we offered market-specific versions of all our applications including AI Predictive Maintenance, AI Inventory Optimization, and AI Energy Management for the utility, oil and gas, defense, and financial services industries. In addition, the company introduced the concept of 4-to-16-week product trials as part of the sales process.

This market and product line expansion again proved successful as the company booked2 approximately $203 million in contracts and recognized $120.4 million in revenue from 2016 – 2018.

C3 AI: 2019 – Present

As the market for cloud computing, big data, IoT, and predictive analytics continued to expand, the market perception of IoT - as expressed in the literature, technical conferences, the academy, and in customer expectations - changed. While IoT had previously been considered at the confluence of sensor devices and AI applications, it was clear that IoT was becoming a concept increasingly centered on the devices - the IoT sensors themselves - with the AI applications considered a separate category. As this developed, the C3 IoT brand became confusing to the market, as many customers had the impression that the company was primarily in the business of manufacturing IoT sensors and devices.

To eliminate this market confusion, we rebranded the company C3 AI, clearly communicating that we were in the computer software business.

In addition to the products and services that the company offered since its inception, C3 AI again expanded its product offerings to include industry-specific production applications such as utility, oil & gas, state and local governments, financial services, manufacturing, health, and communications, and U.S. defense and intelligence, among others. Across industries, we introduced a number of AI application products that serve all vertical markets including C3 AI Asset Performance, C3 AI Supply Chain, C3 AI Defense and Intelligence, C3 Generative AI, among others.

Again, this market expansion proved successful, enabling C3 AI to book over $2.0 billion in additional contracts and recognize $1.9 billion in revenue from 2019 – 2026.

C3 AI was well ahead of its time in predicting the scale of the opportunity in enterprise AI applications. We began when the market was nascent, and as the market has developed and expanded, we have expanded our branding and our market offerings to meet market expectations.

Government Regulation

Our business activities are subject to various federal, state, local, and foreign laws, rules, and regulations. Compliance with these laws, rules, and regulations has not had, and is not expected to have, a material effect on our capital expenditures, results of operations and competitive position as compared to prior periods. Nevertheless, compliance with existing or future governmental regulations, including, but not limited to, those pertaining to global trade, consumer and data protection, and taxes, could have a material impact on our business in subsequent periods. For more information on the potential impacts of government regulations affecting our business, see the section titled “Risk Factors” contained in Part I, Item 1A of this Annual Report on Form 10-K.

Available Information

Our website address is located at www.c3.ai, and our investor relations website is located at ir.c3.ai. We file electronically with the SEC our annual reports on Form 10-K, quarterly reports on Form 10-Q, current reports on Form 8-K, and amendments to those reports filed or furnished pursuant to Section 13(a) or 15(d) of the Exchange Act. We make available on our investor relations website, free of charge, copies of these reports and other information as soon as reasonably practicable after we electronically file such material with, or furnish it to, the SEC. These filings with the SEC are also available on the SEC’s website located at www.sec.gov.

We announce material information to the public through a variety of means, including filings with the SEC, press releases, public conference calls, our website (c3.ai), the investor relations section of our website (ir.c3.ai), X (formerly Twitter) (@C3_AI), and LinkedIn (@C3-AI-Enterprise-AI) accounts. We use these channels to communicate with investors and the public about our company, our products and services and other matters. Therefore, we encourage investors, the media and others interested in our company to review the information we make public in these locations, as such information could be deemed to be material information. Further, corporate governance information, including our corporate governance guidelines, code of business conduct and ethics, and committee charters, is also available on our investor relations website.

The content of or accessible through our websites or our social media channels are not incorporated by reference into this Annual Report on Form 10-K or in any other report or document we file with the SEC, and any references to our websites or social media channels are inactive textual references only.