Technologies behind EagleScout

At EagleScout, our goal is to provide users with clear, data-backed insights into the credibility of Web3 projects. To make that possible, we rely on a combination of modern, scalable, and intelligent technologies — blending traditional data parsing techniques with cutting-edge AI analysis.

Here’s a breakdown of what powers our platform:

API Integrations

We gather real-time and historical data from a wide range of sources, including:

• Twitter APIs — to collect account activity, follower behavior, engagement patterns, posting frequency, and content structure

• Community-sourced services — aggregating public signals, mentions, and sentiment across the broader Web3 space

AI-Powered Post Analysis

Our AI models are trained to read and interpret the way a project communicates. We use:

• Natural Language Processing (NLP) to evaluate tone, urgency, manipulative language, buzzword overload, and emotional triggers

• LLM-powered analysis to simulate expert reviews and produce qualitative assessments of how a project presents itself to users

The result? An expert-level signal distilled from patterns of human behavior and historical scam tactics — all at machine scale.

Custom Parsers & Data Crawlers

We’ve built specialized crawlers to:

• Track mentions, tags, and replies around a project

• Extract hidden or deleted data (when possible) for a more complete picture

Our system is constantly evolving to stay ahead of how scammers adapt their strategies.

Behind the scenes, we’re building:

• A scalable backend that supports rapid data retrieval and processing

• Automated workflows to minimize manual bottlenecks

• Modular architecture so we can quickly plug in new sources or tools as the space evolves