Lapu AI
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Lapu AI is a desktop AI agent for macOS and Windows that utilizes advanced language models to organize and perform multi-step processes directly on your computer. It accesses local files, executes terminal commands, and coordinates actions across applications like Google Workspace, Notion, GitHub, and Figma while ensuring permission-based, privacy-focused operations. By transforming plain-language objectives into approved, repeatable automations, it manages repetitive tasks like document processing, data extraction, file management, and cross-application workflows, delivering outcomes instead of merely answers.
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Skillsize is a platform that allows consultants, advisors, and professional services teams to transform their methodologies, decision-making processes, and expert insights into structured AI skills. These skills follow established reasoning sequences, include human review checkpoints, and generate traceable and documented results. The developed skills can be used within teams, integrated into client projects, or exported to ChatGPT, Claude, Microsoft Copilot, and other AI environments. Skillsize is designed to assist firms in commercializing their intellectual property and delivering expert work consistently and at scale without the need to increase staff numbers.
Elvex is a versatile AI platform for enterprises that allows organizations to create, execute, and share context-aware AI agents and workflows integrating company data and tools. It leverages AI planning, sub-agent orchestration, and contextual comprehension to deliver tangible outputs like reports and presentations, recommends automations, and assists non-expert employees. It also ensures centralized governance, security, and model management, enabling IT to safely expand AI use across the business.
Reflexio is a platform for behavioral learning that transforms AI agents into systems capable of self-improvement. It does this by learning from actual user interactions and deriving practical lessons. The platform records corrections, unsuccessful strategies, and successful outcomes, transforming them into understandable behavioral rules. These rules are then applied by agents in future interactions without the need for retraining. Each learned behavior is compared against a control response and can be audited and reversed if it proves ineffective. Reflexio can be integrated with existing agents based on large language models using a lightweight SDK, Python, REST, or CLI. It supports various deployment models, including fully managed, bring-your-own-key, bring-your-own-database, and fully self-hosted options.