# 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. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://greyhunt.gitbook.io/greyhunt-docs/technical-and-data-architecture.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.