List of OpenAI o3 Customers
San Francisco, 94158, CA,
United States
Since 2010, our global team of researchers has been studying OpenAI o3 customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased OpenAI o3 for Generative AI Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using OpenAI o3 for Generative AI Platforms include: Model ML, a United States based Banking and Financial Services organisation with 75 employees and revenues of $5.0 million, Outtake, a United States based Professional Services organisation with 12 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using OpenAI o3, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The OpenAI o3 customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Model ML | Banking and Financial Services | 75 | $5M | United States | OpenAI | OpenAI o3 | Generative AI Platforms | 2025 | n/a |
In 2025 Model ML deployed OpenAI o3 within its platform to deliver Generative AI Platforms capabilities for financial services clients, positioning OpenAI o3 as a core model in its agent-driven application. The implementation centers on purpose-built agents and an application layer that automates end-to-end research, analysis, and workflow orchestration for investment and research teams.
The deployment architecture separates an agent layer tuned for finance from an application layer that exposes workflow building, orchestration, and presentation generation. Agents are configured to parse both structured and unstructured financial data, execute multi-step reasoning and code generation, and chain tasks to produce formatted outputs. Model ML uses OpenAI o3 alongside GPT 4.1 and the Agents SDK to enable tool calling, instruction following, large context windows, and advanced reasoning inside those agents.
Integrations are explicitly instrumented into the agent loops, including connectors to SharePoint, Capital IQ, FactSet, Crunchbase, CRM systems, email stores, files, meeting transcripts, and external data vendors, with agents handling retrieval from hundreds of tables and terabytes of data. The technical stack relies on OpenAI s API platform and the Agents SDK plus MCP tooling to manage agent loops, tool integrations, guardrails, and connector maintenance, allowing Model ML to focus on domain logic rather than foundational agent infrastructure.
Operational scope includes automating quarterly earnings summaries, slide generation, and publishing PowerPoint deliverables to SharePoint as part of routine research workflows, and enabling autonomous cyclical and event triggered workflows across investment operations. Model ML provides early consultancy to clients on where to apply agents, and it has restructured internal operating practices toward flatter teams and AI assisted one on ones to accelerate product and governance feedback cycles.
The implementation produces end to end workflow automation that shifts repetitive tasks from days or weeks into minutes or hours, with outputs available continuously and autonomously as agents gather, analyze, and present data. Model ML s use of OpenAI o3 within its Generative AI Platforms implementation emphasizes accuracy, compliance oriented workflows, and finance specific tooling rather than general purpose automation.
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Outtake | Professional Services | 12 | $2M | United States | OpenAI | OpenAI o3 | Generative AI Platforms | 2025 | n/a |
In 2025, Outtake deployed OpenAI o3 as the core inference engine for its agentic cybersecurity product, categorizing the implementation within Generative AI Platforms. The deployment uses API-driven, always-on AI agents built on GPT-4o and GPT-4.1 to provide continuous 24/7 threat coverage and to automate detection and remediation workflows for professional services clients.
The implementation centers on a multi-stage pipeline and customizable AI agents that customers configure with verified whitelists, brand guidelines, intellectual property policies, and enforcement preferences. Agents are trained via natural language instructions, ingest multimodal signals such as screenshots and transcripts, score findings for severity, classify abuse types, and select best-fit model reasoning to determine enforcement criteria. OpenAI o3 powers cross-platform correlation, while function calling automates evidence compilation, drafts resolution notices, and files takedown actions with outputs optimized for platform compliance.
Operational coverage extends to scanning webpages, app store listings, social platforms, and ad inventories, with agents continuously crawling surfaces to map trustworthy and suspicious entities. The architecture consolidates signals across disparate formats so correlated campaigns can be revealed when related spoofed domains, lookalike apps, and fake social accounts emerge. All enforcement actions and agent decisions are logged and auditable via the API layer to support legal and compliance reviews.
Governance is explicit, agents operate under predefined rules while security and legal teams retain the ability to intervene or override edge cases, and customer feedback can be incorporated in real time without model retraining. Outtake reports that internal model evaluations show OpenAI models outperform alternatives on reasoning accuracy, and the implementation has reduced takedown timelines from 60 days to hours and helped enterprise customers avoid millions in fraud losses.
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