RugIntel is a decentralized intelligence market built on Bittensor that specializes in predictive rugpull detection for newly launched and existing Solana tokens.

Website :

https://rugintel.vercel.app/

Core concept :

https://planningasa.notion.site/RugIntel-2fd5481f20dc80a7815dd8a80690467b

Pitchdeck:

Rugintel Pitchdeck

Miners compete to produce accurate risk assessments using a proprietary 12-Layer Signal Fusion architecture. Validators independently verify predictions against 24-hour on-chain ground truth outcomes and assign weights proportional to accuracy. Through Yuma Consensus, TAO emissions are automatically distributed to the most accurate intelligence producers.

RugIntel transforms the $500M+ annual rugpull problem into a continuous, sustainable TAO demand engine — while keeping protection free for retail users.

Project Overview

Solana’s memecoin ecosystem launches over 10,000 new tokens per day. Approximately 87% are abandoned or rugpulled within 30 days, draining an estimated $500M+ annually from retail investors.

Existing tools — RugCheck.xyz, TokenSniffer, DexScreener, GMGN.ai — are fundamentally reactive. They detect damage after it occurs. Even combined, they maintain an estimated 18% false negative rate, meaning nearly 1 in 5 rugpulls go undetected.

The structural problem:

• Static — Detection heuristics lag 2–4 weeks behind evolving scam tactics

• Reactive — They observe collapse rather than predict intent

• Expensive — $500+/month API costs exclude retail access

• Centralized — Single points of failure that can be gamed or shut down

RugIntel redefines rugpull protection as a decentralized intelligence market.

Intelligence is the commodity.
Miners produce it.
Validators verify it.
Yuma Consensus rewards accuracy with TAO.

This is not a security tool with token rewards added on. It is Bittensor’s native paradigm applied directly to DeFi security: a self-improving intelligence market where accuracy determines economic power.

Importantly, 68% of rugpulls occur within 12 minutes of token launch. RugIntel is engineered to detect malicious intent — not just market collapse — within that critical window.

Core Mechanism: Rugpull Proof-of-Intelligence

The subnet operates through an incentive-aligned competitive loop between two actors:

Miners (Intelligence Producers)

Miners run off-chain Python processes executing analysis across 12 non-overlapping intelligence layers. Each miner exposes an Axon endpoint queried through the RugIntelSynapse protocol.

Input:

• Token address

• Launch timestamp

Output (standardized schema):

• Risk score (0.0 – 1.0)

• Confidence interval

• Supporting evidence per layer

• Estimated time-to-rugpull

Intelligence Layers

Layer 1: Social Intelligence — Detects coordinated pump campaigns by identifying >10 newly created Twitter/Telegram accounts posting identical shill messages within 5 minutes. Bot ratio calculation flags artificial hype before organic interest emerges.

Layer 2: Liquidity Intelligence — Monitors real-time liquidity pool dynamics on Raydium and Orca. Flags unlocked liquidity, LP drain patterns exceeding 15% within 2 minutes, and ratio decline signals indicating imminent collapse.

Layer 3: Wallet Intelligence — Analyzes holder concentration and deployer history. Flags tokens where top holder controls >50% supply or dev wallet sells >20% holdings within 5 minutes. Includes fund flow tracing to detect sock puppet wallets.

Layer 4: Market Intelligence — Identifies abnormal volume spikes (>100x baseline within 2 minutes) and wash trading patterns. Cross-references price action with order book depth to distinguish organic pumps from manipulative activity.

Layer 5: Contract Intelligence — Integrates RugCheck.xyz and TokenSniffer APIs for real-time contract forensics. Detects mint authority retention, freeze functions, honeypot mechanisms, and backdoor withdrawal capabilities.

Layer 6: Visual Intelligence — Scans token logos and metadata for AI-generated artifacts using CLIP-ViT similarity analysis. Detects typosquatting attempts mimicking legitimate projects through difflib string matching and color palette cloning.

Layer 7: Temporal Intelligence — Models behavioral economics patterns including FOMO peak detection and quiet period analysis. 68% of rugpulls occur within 12 minutes post-launch; this layer predicts collapse timing based on historical pattern matching.

Layers 8–11: Advanced Detection (Phase 2) — Cross-chain intelligence tracking bridge patterns to Ethereum/Base, MEV/bot detection identifying sandwich attacks, tokenomics forensics uncovering hidden mint functions, and exchange flow analysis monitoring CEX deposit coordination preceding pumps.

Layer 12: Adversarial Learning — Meta-cognition engine that learns from every missed rugpull. Performs post-mortem analysis, automatically rebalances layer weights for novel attack vectors, and flags patterns 85%+ different from historical corpus for validator review.

Read more all about layer parameter on : Rugintel Docs

Weighted Fusion Model

Signals are combined using calibrated predictive weights:

Liquidity 25%
Wallet 20%
Temporal 20%
Contract 15%
Market 10%
Social 7%
Visual 3%

This produces a unified composite risk score.

Validators (Verification & Weight Setting)

Validators independently query multiple miners per token and compare predictions against 24-hour on-chain ground truth using:

• Solana RPC

• RugCheck API

• DexScreener data

Two scoring windows operate:

Short-term:
Immediate weight adjustments

Long-term:
Rolling accuracy reputation using softmax-weighted historical performance

Validators whose scoring deviates from ground truth lose influence through Yuma Consensus.

Accuracy directly determines TAO rewards.
No manual distribution.
No central authority.
Everything enforced by the subtensor blockchain.

Architecture Overview

Technical Innovation

Composite Scoring Formula

composite_score =
accuracy_score * 0.60 +
latency_score * 0.20 +
confidence_score * 0.15 +
format_score * 0.05

Early-Detection Bonus

If a miner predicts a rugpull with ≥0.85 confidence at least 5 minutes before occurrence:

→ 1.3× accuracy multiplier

This economically prioritizes pre-collapse detection.

Adversarial Learning Engine (Layer 12)

Layer 12 creates a network-wide learning loop:

• Post-mortem analysis of missed rugpulls

• Automatic weight rebalancing

• Novel pattern escalation (>85% deviation from historical corpus)

• Validator-assisted feedback integration

This removes the 2–4 week adaptation lag that central tools suffer from.

Miners are financially incentivized to discover new scam patterns first.

Discovery = TAO revenue.

Anti-Gaming Safeguards

• Challenge injection (known-outcome tokens)

• Cross-miner ensemble validation

• Stake-weighted validator slashing

• Penalties for false neutrals on critical risk scores

Market Opportunity

• $500M+ annual rugpull losses (Solana only)

• 4.2M active retail wallets exposed

• Institutional demand for real-time risk APIs

Projected Year 1 MRR (conservative 1% budget capture):

Institutional API: $108k/month
Retail Pro: $50k/month
Subnet Emissions: $13–15k/month
Total: $170k+/month

Business Model

Free Tier:
10 alerts/month, Telegram bot access

Pro Tier ($99/month):
Unlimited alerts, portfolio dashboard, database access

Institutional Tier ($499/month):
Webhooks, auto-cancel integration, validator access

B2B API:
$0.001 per call + $500/month base

White-label SDK:
$5k setup + 15% revenue share

20% of subnet emissions flow into treasury for:

• Miner subsidies

• Bug bounties

• Grants

• Marketing

The subnet is designed to be self-sustaining without VC dependency.

Hackathon Progress

Completed :

• Full subnet architecture using Yuma Consensus

• TwelveLayerFusion engine (parallel execution)

• 7 of 12 intelligence layers fully implemented

• Miner and validator neuron logic

• Full documentation suite

• Next.js landing page with AI-powered chatbot

• Interactive token scan page with 12-layer breakdown

Stage :
Pre-seed / Bootstrapped

Objectives:

• Testnet subnet registration

• Miner bootstrapping

• Expansion to full 12-layer coverage

Target:
$500k seed round for institutional data pipeline and validator expansion.