Agiloft vs IBM
Side-by-side comparison of AI visibility scores, market position, and capabilities
Enterprise Contract Lifecycle Management Platform
Enterprise CLM with no-code configuration; Gartner CLM Magic Quadrant leader; raised $45M; configurable without custom development for legal, procurement, and sales contract workflows.
About
Agiloft is an enterprise contract lifecycle management (CLM) platform that provides contract authoring, negotiation, approval workflows, repository management, and post-execution obligation tracking in a highly configurable no-code platform that can be tailored to complex enterprise contracting requirements without custom development. Founded in 1991 and headquartered in Redwood City, California, Agiloft has raised approximately $45 million and built a strong position in the enterprise CLM market, recognized as a leader in Gartner's CLM Magic Quadrant and serving hundreds of large enterprises across legal, procurement, and sales contracting use cases.\n\nAgiloft's no-code configuration model is a key differentiator — the platform can be configured by business analysts and administrators to model any contracting workflow, approval chain, clause library, or reporting requirement without writing code, making it adaptable to the unique legal and procurement policies of different enterprise customers. Contract AI capabilities include clause extraction, obligation identification, and risk scoring of contracts being reviewed or imported into the repository. The platform handles inbound third-party paper review as well as outbound contract generation from approved templates, covering both sides of enterprise contract negotiation.\n\nAgiloft competes with Ironclad, DocuSign CLM, Icertis (which focuses on enterprise procurement contracts), and Conga in the CLM market. Its deep configurability and enterprise heritage make it particularly attractive to legal operations and procurement teams managing complex contracting environments with many contract types and specialized approval and obligation management requirements. Agiloft's longevity (30+ years) and Gartner leadership recognition provide credibility with risk-averse enterprise buyers in the legal technology space.
Armonk NY hybrid cloud and enterprise AI (NYSE: IBM) at $62.8B revenue; $6B+ generative AI bookings, record $12.7B free cash flow 2024, DataStax acquisition for watsonx vector database competing with Microsoft Azure for enterprise AI.
About
International Business Machines Corporation (IBM) is an Armonk, New York-based global technology and consulting company — publicly traded on the New York Stock Exchange (NYSE: IBM) as an S&P 500 component — providing hybrid cloud infrastructure, artificial intelligence software, and enterprise IT consulting through approximately 270,300 employees in 170 countries with $62.8 billion in annual revenue. Founded on June 16, 1911, as Computing-Tabulating-Recording Company through a merger orchestrated by financier Charles Ranlett Flint, renamed IBM in 1924 under Thomas Watson Sr., IBM has undergone multiple strategic transformations over its 110+ year history: building the System/360 mainframe platform (1964), launching the IBM PC (1981), selling the PC division to Lenovo (2005, $1.75B), and completing the $34 billion Red Hat acquisition (2019) that repositioned IBM as a hybrid cloud platform company. CEO Arvind Krishna (appointed April 2020) has focused IBM's strategy on three areas: hybrid cloud (powered by Red Hat OpenShift, the enterprise Kubernetes platform), AI (the watsonx platform for enterprise AI model development and deployment), and enterprise consulting. Under Krishna, IBM recorded $12.7 billion in free cash flow in 2024 (a company record), surpassed $6 billion in generative AI bookings since June 2023, and saw the stock price double — trading at all-time highs through 2024-2025. IBM announced the DataStax acquisition in 2025 to deepen watsonx's data layer with AstraDB (vector database for AI applications), DataStax Enterprise (Apache Cassandra), and Langflow (low-code AI agent development).
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