Side-by-side comparison of AI visibility scores, market position, and capabilities
Toronto, Canada. Acquired by Kroll. Risk management and incident management platform for corporate security, compliance, and government risk programs.
Resolver is a Toronto-based risk management and incident management platform that was acquired by Kroll, the global financial and risk advisory firm, to enhance its technology-enabled risk management services. The company provides software for enterprise risk management, security and incident management, compliance management, and audit management, serving corporate security teams, financial institutions, and government agencies.\n\nThe Resolver platform includes modules for enterprise risk management (ERM) with heat maps and risk registers, security incident and investigation management, compliance program tracking, internal audit workflow management, and IT risk and vendor risk assessment. Its security incident management module is widely used by corporate security professionals to track physical security incidents, conduct investigations, and generate risk reports. The platform's configurable data model allows organizations to adapt it to industry-specific risk frameworks.\n\nResolver targets corporate security directors, chief risk officers, compliance managers, and internal audit teams at financial institutions, utilities, healthcare organizations, and government agencies. It competes with ServiceNow GRC, Riskonnect, and LogicManager. Kroll's acquisition has strengthened Resolver's position in the financial services and government sectors where Kroll has deep advisory relationships, creating a software-plus-services offering for complex risk management engagements.
Serverless GPU cloud platform for AI/ML with Python-native deployment and per-second billing; developer-favorite scaling from zero competing with Replicate and Beam for AI compute.
Modal is a serverless cloud computing platform purpose-built for AI and machine learning workloads — providing on-demand GPU compute that scales instantly from zero with per-second billing, container management, distributed training support, and a Python-native developer experience that makes running ML workloads in the cloud feel as simple as running code locally. Founded in 2021 in New York City and backed by Redpoint Ventures and other investors, Modal has grown rapidly as AI development has accelerated demand for flexible, developer-friendly GPU infrastructure.\n\nModal's developer experience is its primary differentiator — engineers write Python functions decorated with @modal.function() and deploy them to the cloud with a single command, with Modal handling container building, GPU provisioning, auto-scaling, and execution. The platform supports training jobs that need distributed compute across multiple GPUs, model serving endpoints that scale to zero when unused (eliminating idle GPU costs), and batch inference jobs that process large datasets. The per-second billing model means developers pay only for actual compute time, not provisioned instances.\n\nIn 2025, Modal competes in the AI infrastructure market with Replicate, Beam, Banana, and major cloud providers' managed ML services (AWS SageMaker, Google Vertex AI, Azure ML) for serverless GPU compute. The market for AI-specific cloud infrastructure has grown dramatically as the number of ML engineers deploying models to production has expanded — traditional cloud providers require significant DevOps expertise to use GPU instances effectively, while Modal's Python-native approach reduces the barrier to entry. Modal has attracted a strong developer following among AI researchers and ML engineers building production AI applications. The 2025 strategy focuses on growing the developer community, adding enterprise features (dedicated GPU capacity, private networking, compliance), and expanding the hardware options available (H100 GPUs, custom accelerators).
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