TigerEye provides a comprehensive suite of tools aimed at simplifying and optimizing go-to-market planning and execution. Utilizing advanced AI and data analytics, TigerEye helps businesses simulate various scenarios, streamline quota and territory assignments, and manage pipelines effectively. Ideal for both strategists and operations teams, its intuitive interface makes complex planning tasks easy and actionable.

Algorithmics provides an array of financial risk management solutions designed to help institutions measure and manage risk more effectively. Leveraging cutting-edge technology, the platform offers comprehensive pricing, simulation, and scenario generation tools. These advanced features support the construction and aggregation of complex financial models, enabling real-time analytics and enhanced decision-making processes for better risk mitigation.

Coval helps companies simulate thousands of scenarios from a few test cases, allowing them to test their voice and chat agents comprehensively. Built by experts in autonomous testing, Coval offers features like customizable voice simulations, built-in metrics for evaluations, and performance tracking. It is designed for developers and businesses looking to deploy reliable AI agents faster.

GAMA Genstar Plugin adds generative AI capabilities to the GAMA platform by providing connectors to OpenAI, local LLMs, and custom model endpoints. Users define prompts and pipelines in GAML to generate agent decisions, environment descriptions, or scenario parameters on the fly. The plugin supports synchronous and asynchronous API calls, caching of responses, and parameter tuning. It simplifies the integration of natural language models into large-scale simulations, reducing manual scripting and fostering richer, adaptive agent behaviors.

GridWorldEnvs offers a comprehensive suite of grid-world environments to support the design, testing, and benchmarking of reinforcement learning and multi-agent systems. Users can easily configure grid dimensions, agent start positions, goal locations, obstacles, reward structures, and action spaces. The library includes ready-to-use templates such as classic grid navigation, obstacle avoidance, and cooperative tasks, while also allowing custom scenario definitions via JSON or Python classes. Seamless integration with the OpenAI Gym API means that standard RL algorithms can be applied directly. Additionally, GridWorldEnvs supports single-agent and multi-agent experiments, logging, and visualization utilities for tracking agent performance.