Core Interaction Capabilities

Greyhunt functions as a multi-layered signal engine. It continuously ingests token-related data, processes it through a high-throughput analytics stack, and surfaces distilled intelligence within a latency-aware interface. A tailored ensemble of language and graph-based models performs dimensional reduction—transforming complex, unstructured feeds into ranked, explainable signals.

Each of Greyhunt’s three core modules serves a distinct role in eliminating friction—whether from fragmented sources, information overload, or signal lag.

Component	Purpose
Deep-Sense Signal	A unified dashboard that transforms scattered datasets into a structured, visual narrative. Data is not merely aggregated—it is layered, contextualised, and presented in a format that remains accessible to new users without compromising analytical depth for advanced research. It draws from foundational data, on-chain analytics, and social signals—consolidating information typically scattered across dozens of platforms.
Bot-Query	A natural-language interface that enables users to retrieve precise answers from Greyhunt’s data layers. Each response is grounded in structured references and source citations, enabling exploration, validation, and rapid synthesis of unfamiliar tokens or sectors. Access is tiered, with usage quotas based on user subscription or staking level.
Smart Alert	A detection system focused on surfacing notable developments as they emerge—particularly those with potential narrative or market impact. It monitors public discourse, cross-references signal clusters, and delivers context-rich alerts that prioritise timeliness, relevance, and verifiability. The number of tracked tokens is limited by user tier, with higher tiers unlocking broader monitoring capabilities.

Together, these modules form the user-facing layer of Greyhunt—designed to reduce noise, sharpen decision-making, and restore clarity in a fragmented information environment.