Howl Street

Howl Street is a role-based AI company research platform built for fast company analysis. Instead of forcing one generic model to do everything, the app lets the user choose the exact analyst that fits their goal. - The Cartographer for market context - The Stormcaller for risk - The Blade for competition - The Ledger for financial health Each analyst has a distinct research lens and returns a focused brief for the same company, making the experience feel more like consulting a specialist than using a standard chatbot. To keep the experience fast enough for the demo, only the user-selected analyst runs at first. This reduces unnecessary model calls and keeps token usage lower than a full fan-out multi-agent workflow, while still preserving the unique “choose your analyst” product idea. The prompts are intentionally concise so the app can produce a focused result without long orchestration delays. The Werewolf acts as the Arbiter. After the chosen specialist produces a report, Werewolf can review the output for weak evidence, overconfidence, or missing balance, then provide a sharper final verdict and confidence score. This makes the system feel like a research council with distinct personalities, while staying lightweight enough to be practical in a live demo. We want the idea for the project to be a strategy board game Werewolf themed, which aligns with the theme of AI agents. Built with CrewAI-style role-based orchestration and Fireworks serverless inference, the idea turns into a simple company-name input into a fast, specialized research workflow that is easier to control, easier to explain, and more engaging than a one-size-fits-all assistant. Finally, we managed to grasp the concept where strategic intelligence meets “Werewolf Gameplay”, an interactive dashboard for retail investors and self-directed traders who want a quick, structured research on a company instead of digging through multiple sources.