Aiimi
Aiimi’s Workplace AI platform is an enterprise-scale AI and data management solution that connects all structured and unstructured data across an organization through a single Virtual Data Layer, enabling secure, scalable AI-powered search, analysis, automation, and actionable insights. It uses AI, machine learning, and Retrieval Augmented Generation (RAG) to discover, classify, enrich, and govern data at scale, turning fragmented information into trusted, “AI-ready” datasets that support natural language search, contextual chat and assistant features, advanced Q&A, and visualizations like knowledge graphs and timelines. It automates complex processes such as data governance, compliance monitoring, data quality improvement, DSAR/disclosure handling, and cloud/legacy system migration, while preserving access controls, permissions, and audit trails.
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GraphAware
GraphAware offers Hume, a connected data analytics and intelligence analysis platform powered by graph technology that transforms siloed structured and unstructured data into an interconnected network for deeper insight and decision-making. At its core, Hume uses knowledge graph and graph database principles to ingest, unify, and represent data as networks of nodes and relationships, enabling analysts and data scientists to intuitively navigate, query, and visualize multi-hop connections and hidden patterns without needing to learn complex query languages. It delivers a single view of truth across disparate data sources, accelerates discovery of hidden relationships and behavior patterns, and supports advanced graph data science, including node influence analysis, link prediction, community detection, and automated alerting through integrated machine learning and large language model (LLM) features.
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Contextually
Contextually is an enterprise AI platform designed to help organizations build and deploy production-ready AI agents that can reason over complex, domain-specific data using advanced context engineering. It provides a unified context layer that connects AI models to large volumes of enterprise knowledge, including documents, databases, and multimodal data, enabling agents to deliver accurate, grounded, and relevant outputs. It allows users to define and configure agents quickly through prebuilt templates, natural language prompts, or a visual drag-and-drop interface, supporting both dynamic agents and structured workflows tailored to specific use cases. It includes tools for ingesting and processing massive datasets from multiple sources, transforming unstructured and structured information into retrievable knowledge with intelligent parsing, metadata generation, and continuous updates.
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Arena Autonomy OS
Arena empowers businesses across industries to make high-frequency, critical path decisions fully autonomous. Autopilot for high-frequency business decisions. Similar to a physical robot, Autonomy OS is composed of three components, the sensor, the brain, and the arm. The sensor measures, the brain makes decisions, and the arm takes action. The whole system operates automatically and in real time. Autonomy OS ingests and encodes heterogeneous data with different latency profiles, from streaming real-time and structured time series, to unstructured data like images and text, into features that train machine learning models. Autonomy OS also augments data with contextual data from Arena’s Demand Graph, a daily updating index of factors that affect consumer demand and supply, from product prices and availability by location, to demand proxies from social media platforms. Customer preferences and behaviors change, supply routes are unexpectedly disrupted, and competitors alter strategy.
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