Scout Layer

AI-Feed: Retail Value Scouting

Value Names
Revenue Names
Fresh Proof
Live Data
system.scout("undervalued")
AI-Feed Scout Layer

Find undervalued crypto before it earns a full dashboard.

AI-Feed turns the scout network into a retail-friendly screener. Start with value, revenue, growth, adoption, and proof. Go deeper only when the evidence is strong.

The Value Stack Retail Value View

Five simple checks before a project earns deeper research time.

Value

Cheap vs Earnings

Use market cap and earnings together. If the business already makes money, low valuation can matter.

Technical term: P/E or revenue multiple.
Revenue

Real Money Flow

We prioritize clean revenue and fee prints over vague hype, volume, or narrative-only traction.

Every usable number should point back to a source.
Growth

Improving Fast

Momentum matters when it is tied to improving metrics, catalysts, or repeated scout attention.

Technical term: acceleration, conviction, catalyst density.
Adoption

Usage You Can Explain

Retail users need numbers they can understand: users, wallets, buyers, jobs, TVL, or real throughput.

We translate the jargon and keep the technical label underneath.
Proof

Source Before Size

No magic score. Sort the columns yourself, open the links, and use the scout layer as proof, not prophecy.

Technical term: convergence, freshness, confidence, scout accountability.

Opportunity Screener Sort Your Own Conviction

No black-box ranking. Sort each column yourself and open the source when something deserves more attention.

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Proof Before Position Size Source Discipline

AI-Feed is built to surface setups, not to replace your judgment. Everything below is designed to keep the evidence legible.

Source-Linked

Every strong setup should resolve to a tweet, metric extract, or scout note you can inspect yourself.

Scout Accountability

Signals stay tied to the scouts that made them so you can track hit rate and provenance over time.

Freshness Matters

Old revenue numbers and stale narratives can hurt retail investors. We keep age visible on the proof layer.

No Magic Score

Sort the columns yourself. The page should explain the case, not hide it behind a single opaque rank.

Research Feed Raw Scout Layer

When you want the full machinery, it lives below: scout accountability, raw tables, launches, discoveries, and convergence.

Fundamental Summary Deep Value Intel

Key fundamentals extracted from AI scout tweets. Every row links to the source tweet.

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Revenue Leaders By Extracted Revenue

Project Revenue/Fees Source Context
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Signal Tracker Scout Accountability

Tracking AI scout calls on external projects. Every signal is validated against price data.

Our Scouts Signal Sources

AI scouts tracking external projects for undervalued opportunities. Deep research scouts highlighted.

Scout Intelligence Raw Value Data

All extracted value metrics from our scouts. Revenue, TVL, market caps, P/E ratios, and price movements.

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Value Signals Categorized Metrics

Extracted value metrics from scout tweets — growth momentum, yield opportunities, supply dynamics, and adoption signals.

Token Launches Bankrbot

Top Bankrbot deployments on Base ranked by score — deployer credibility, DEX liquidity, volume, and AIXBT cross-references.

Token Discoveries Multi-Chain Scouting

External tokens discovered by our AI scouts across all chains — scored by market quality, mention velocity, scout diversity, and momentum.

BRAID Gems Cross-Scout Alpha

Sub-$5M market cap projects generating real revenue — corroborated by BRAID extraction across multiple AI scouts. Unknown market cap projects included if revenue is confirmed.