Technical & Data Architecture
Data Ingestion Layer
Greyhunt continuously ingests data across three primary verticals: project fundamentals, on-chain flows, and social narratives. Data is sourced from multiple real-time endpoints, including structured APIs, decentralized feeds, and domain-specific content aggregators. Macro indicators and curated research enrich this base layer, giving users access to broader market context alongside token-level insights.
To ensure speed and integrity, all incoming data flows through a validation and enrichment pipeline. Redundant entries are filtered, anomalies are flagged, and confidence weights are applied based on internal heuristics. A cache layer supports sub-second retrieval, optimizing both interface latency and model responsiveness.
Processing & Signal Engine
Once ingested, data flows through a processing stack combining rule-based filters with ensemble language models.
These models translate raw fundamentals, on-chain anomalies, and social chatter into machine-readable briefs—scored and routed based on signal strength, novelty, and alignment with market narratives.
The result is an adaptive intelligence layer that continuously updates signal salience as context evolves.
Interface & Delivery Layer
Processed signals are surfaced through a latency-aware interface designed for clarity and decisiveness. Each module—Deep-Sense Signal, Bot-Query, and Smart Alert—draws from the same structured intelligence layer, but presents it in different operational modes: visual narrative, conversational retrieval, or real-time push.
By maintaining a unified data spine beneath each user-facing feature, Greyhunt ensures that context remains consistent—even as interaction styles shift. The result is a high-velocity research and monitoring environment where insights are not just delivered, but absorbed and acted upon without delay.
Last updated