GreyCollar AI enables organizations to design, train, and deploy AI-powered digital workers through a user-friendly, no-code interface. By ingesting documents, knowledge bases, and APIs, these agents understand company protocols and workflows. They seamlessly integrate with communication platforms like Slack and Microsoft Teams, handling tasks such as answering FAQs, processing IT tickets, and routing service requests. Built-in memory systems allow agents to recall past interactions, ensuring coherent and personalized responses. Administrators can monitor performance metrics, adjust workflows, and scale agents across teams. Whether improving customer service, streamlining HR onboarding, or automating sales outreach, GreyCollar AI transforms manual processes into efficient, automated workflows that drive productivity and reduce operational costs.

ADK-Golang is an open-source Agent Development Kit for the Go ecosystem. It provides a modular framework to register and manage tools (APIs, databases, external services), build dynamic prompt templates, and maintain conversation memory for multi-turn interactions. With built-in orchestration patterns and logging support, developers can easily configure, test, and deploy AI agents that perform tasks such as data retrieval, automated workflows, and contextual chat. ADK-Golang abstracts low-level API calls and streamlines end-to-end agent lifecycles—from initialization and planning to execution and response handling—entirely in Go.

Agentic Framework is a versatile, open-source framework designed to streamline the creation of autonomous AI agents that leverage large language models and MongoDB. It equips developers with modular components for managing agent memory, defining toolsets, orchestrating multi-step workflows, and templating prompts. The integrated MongoDB-backed memory store enables agents to maintain persistent context across sessions, while pluggable tool interfaces allow seamless interaction with external APIs and data sources. Built on Node.js, the framework includes logging, monitoring hooks, and deployment examples to rapidly prototype and scale intelligent agents. With customizable configuration, developers can tailor agents for tasks such as knowledge retrieval, automated customer support, data analysis, and process automation, reducing development overhead and accelerating time-to-production.