Elata Intelligence

Project Overview

The Elata Intelligence platform gives Elata two distinct strategic advantages:

  1. It activates instant situational awareness. Automated monitoring of science, patents, regulation, and DeSci activity is enabled such that tokenholders can respond in real time.

  2. It feeds Elata's broader EOP technology portfolio needs in that it contributes to Elata's model-training pipeline. A continuously labelled corpus that seeds bespoke AI models for diagnosing and treating mental-health conditions is more imminent with Elata Intelligence.

With Elata Intelligence, every scraped article, patent, or clinical trial update that flows through is simultaneously a signal for cybernetic governance (e.g. "open this bounty; vote on that study") as well as a data point for precision psychiatry models (e.g. predictive modeling algorithms, risk-scoring nets, etc.).

As of right now, Elata Intelligence is in its very early days, and is geared towards fulfilling the former of the two distinct strategic advantages mentioned above.

Over the next few sprints the Elata community will continue refreshing the platform to match Elata’s new design system, wire the scraper into additional data streams (non-English journals, FDA filings, ClinicalTrials.gov entries, and global patent dockets) and syndicate the highest-value items to Discord, Warpcast/Farcaster, Reddit, X, and an email digest.

How Elata VSM System 4 currently works: process flow and purpose.

Current Platform: Elata VSM System 4 Reader

The platform acts as the DAO's environmental scanner, using GPT to process information about scientific papers, industry developments, consumer trends, developments in decentralized science, etc. This systematic monitoring allows the DAO to detect emerging opportunities, potential threats, and shifting paradigms that could impact its strategic direction. By transforming this environmental data into actionable intelligence, the system enables informed decision-making by token holders and helps maintain the organization's viability in a complex, fast-moving landscape.

Viable System Model (VSM) flowchart representation

The broader intent of this project is to automate as much of Elata's System 4 processes as possible using Artificial Intelligence.

Component
Role
Key Technologies

Scraping engine

Hourly harvests of PubMed, arXiv, patents, grant feeds, and DeSci forums.

Node/TS, NewsAPI, custom RSS

AI tagger

GPT-4o labels topic, sentiment, molecule/device, ICD-10 tags

OpenAI functions, Zod schemas

Knowledge store

Typed JSON → Neo4j graph (R&D) with GraphQL endpoint

Neo4j

Web UI

Public feed (news.elata.bio)

Next.js 15 SSR

Agent layer (beta)

CrewAI workers broadcast alerts or draft bounties

CrewAI, Discord/X/Farcaster webhooks

Elata Intelligence Roadmap

Title
Description
Timeline

Domain LLM (7 B)

Local Q&A, richer tagging, zero API cost implementation.

Q1, '26

Multimodal Clustering

Identify MDD/PTSD subpopulations from mutli-modal dataset: EEG (neuroimaging) and Psychometric (patient-administered test/assessment)

H1, '26

Risk-Scoring Classifier

Initiate predictive modeling algorithm development - predict treatment-response probability; triage patients.

H2, '26

Elata VSM System 4 GitHub repo

Full technical overview

DeepWiki overview

Last updated