Sela Network Solution

Overview: Core Infrastructure for the AI Agent Era

Sela Network is a decentralized web interaction layer designed to enable AI agents to interact with the web. Unlike centralized web scraping solutions, Sela combines globally distributed real browser nodes, semantic data transformation engines, and cryptographic verification systems to provide AI agents with reliable and scalable web access.

Current AI agents can “read” the web but face multiple technical and economic barriers when actually interacting with websites. Sela Network removes these barriers to establish the foundation for an AI agent economy.

Core Solution Architecture

Sela Network comprises three innovative layers, each designed to solve specific problems.

Layer 1: Distributed Browser Network

The Problem

Traditional web scraping solutions run headless browsers (Puppeteer, Playwright) on centralized servers. This creates the following issues:

Vulnerability to Bot Detection: Research demonstrates that headless browsers are blocked by bot detection systems like DataDome at an average rate of 52.93%. This occurs because headless browsers leave distinctive browser fingerprints different from regular user browsers.
Single Point of Failure (SPOF): When central servers fail, entire services are interrupted.
Scalability Limits: The number of concurrent requests that can be processed is limited by server capacity.
Geographic Constraints: Difficult to address content accessible only from specific regions.

Sela’s Solution

Sela Network adopts a DePIN (Decentralized Physical Infrastructure Network) model that leverages real user browsers distributed globally as nodes.

Core Mechanisms:

Authentic Browser Environments
- Node operators install the Sela extension on regular browsers including Chrome, Firefox, and Safari.
- Each browser has a unique fingerprint, making it indistinguishable from actual users.
- Browser fingerprinting research shows that dozens of attributes including Canvas rendering, WebGL, and font lists combine uniquely for each browser.
Behavioral Pattern Emulation
- Sela uses human behavior simulation techniques to naturally reproduce mouse movements, scroll speeds, keyboard input patterns, and other behaviors.
- This bypasses bot detection systems with 98.7% success rate (internal testing).
Geographic Distribution
- Access to region-restricted content through nodes distributed across 100+ countries.
- When requests arrive, the system automatically selects the nearest node or nodes from specific regions.
Elastic Scalability
- Network capacity increases linearly as nodes are added.
- The DePIN market grew 326.3% to $11.8B in 2024, validating this decentralized model’s effectiveness.

Technical Implementation:

[AI Agent Request]
    ↓
[Sela Network Load Balancer]
    ↓
[Node Selection Algorithm]
  - Geographic Location
  - Availability
  - Performance Tier
  - Bot Detection Bypass Rate
    ↓
[Selected Browser Node]
  - Real user browser
  - Unique fingerprint
  - Regional IP address
    ↓
[Target Website]
    ↓
[Response] → [AI Agent]

Layer 2: Semantic Interpretation Engine

The Problem

The web was designed for human eyes and hands. HTML/CSS defines visual layouts, but AI agents require structured data. This creates the following issues:

Inconsistent Parsing: Converting HTML to JSON with LLMs generates different schemas each time.
High Costs: Parsing one page with GPT-4 costs an average of $0.05.
Slow Processing: Requires average processing time of 5-10 seconds.
Hallucination: LLMs may generate non-existent data.

Sela’s Solution

Sela’s Semantic Interpretation Engine (SRE) uses a hybrid approach:

1. DOM Parser (High Speed, Low Cost)

Analyzes HTML structure to identify semantic elements (titles, prices, images, etc.).
Prioritizes structured data standards like Schema.org.
Processing speed: ~200ms per page
Accuracy: 99.2% (simple structured data)
Cost: ~$0.0001 per request

2. Vision Language Model (Complex UI Processing)

Visually recognizes complex UI elements that DOM Parser cannot resolve.
GPT-4o Vision demonstrates 65-80% OCR accuracy, which Sela uses complementarily.
Processing speed: ~2-4 seconds per page
Accuracy: 72-74% (complex multimodal tasks)
Cost: ~$0.01-0.05 per request

3. Hybrid Strategy (Cost Optimization)

Step 1: Attempt DOM Parser (99% of cases)
   ↓
Success → JSON output
   ↓
Failure (1-5% of cases)
   ↓
Step 2: Use LLM
   ↓
JSON output

This strategy achieves 82% cost reduction compared to Browserbase.

4. JSON-LD Standard Utilization

Sela outputs JSON-LD (JSON for Linked Data) format. JSON-LD:

Was adopted as a W3C standard in 2014.
Is the format Google recommends for structured data.
Helps AI systems accurately interpret webpage content.
LLM performance improves exponentially when processing structured data like JSON-LD.

Output Example:

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Apple AirPods Pro (2nd Generation)",
  "offers": {
    "@type": "Offer",
    "price": "249.99",
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock",
    "seller": {
      "@type": "Organization",
      "name": "Amazon"
    }
  },
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.8",
    "reviewCount": "15624"
  },
  "shippingDetails": {
    "@type": "OfferShippingDetails",
    "shippingRate": {
      "@type": "MonetaryAmount",
      "value": "0.00",
      "currency": "USD"
    },
    "deliveryTime": {
      "@type": "ShippingDeliveryTime",
      "handlingTime": {
        "@type": "QuantitativeValue",
        "minValue": 0,
        "maxValue": 1,
        "unitCode": "DAY"
      }
    }
  }
}

5. Self-Healing Selector System

Website UIs change frequently. Existing solutions require manual repairs when CSS selectors break. Sela’s Self-Healing system:

AI-based selector regeneration: Combines LLM and DOM structure analysis.
Fuzzy Matching: Automatically matches elements with 90%+ similarity.
User feedback loop: Learns from manual correction data.

Verified Success Rate (internal testing):

Tracked Amazon, eBay, Walmart for 6 months
98.5% automatic recovery success
Average recovery time: 2.3 hours

Layer 3: Verifiable Proof Layer

The Problem

Data obtained from the current web is unverifiable:

Screenshots can be manipulated.
HTML source code can be modified.
No method exists to cryptographically prove data provenance.

This is a core barrier preventing web data utilization in high-trust domains like finance, legal, and healthcare.

Sela’s Solution: zk-TLS (Zero-Knowledge TLS)

Sela implements zkTLS based on the TLSNotary protocol. zkTLS is an innovative protocol that combines Transport Layer Security (TLS) with Zero-Knowledge Proofs (ZKP) to cryptographically prove web data provenance and integrity while ensuring data privacy.

The Fundamental Problem zkTLS Solves: The Oracle Problem

Traditional blockchain oracles primarily process public information like price data and cannot scalably handle personally identifiable information (PII) or sensitive data. zkTLS solves a different problem: ensuring private data cannot be forged by Prover, Server, or Verifier.

TLS oracles cryptographically verify digital content origins, liberating private data trapped in centralized servers and enabling integration with Web3 smart contracts.

Operating Principle: 3-Phase Protocol

Sela’s zkTLS is based on 3P-TLS (Three-Party TLS) protocol, with three core participants:

S (Server): Trusted data source (e.g., banking website, government portal)
P (Prover): User/client generating proof
V (Verifier): Sela Network’s verification node

Phase 1: TLS Handshake (Multi-Party Computation)

Standard TLS protocol is modified so P and V collaboratively perform the “client” role:

Based on Elliptic Curve Diffie-Hellman (ECDH) protocol
Combines MPC (Multi-Party Computation) and Oblivious Transfer (OT) to prevent cheating
P and V generate a shared session key without either party knowing the complete key
S (Server) performs normal TLS handshake, unaware that P and V are collaborating

Technical Implementation:

P (Prover) + V (Verifier) ↔ S (Server)
         ↓
    ECDH Key Exchange
    - Generate Pre-Master Secret via MPC
    - P and V each hold Secret Share
         ↓
    Derive Session Key
    - Generate AES-128 encryption key
    - Perform encryption operations with Garbled Circuits
         ↓
    Establish TLS 1.2/1.3 Session
    - TLSNotary supports TLS 1.3 (added 2024)

Phase 2: Data Transmission and Commitment

P sends HTTPS request to S (e.g., bank balance inquiry)
S’s response is transmitted to P in encrypted form
V verifies encrypted communication integrity without seeing plaintext data
P generates data commitment

Through Garbled Circuits and Oblivious Transfer technologies, V can ensure communication authenticity without knowing P’s request content or S’s response data.

Phase 3: Zero-Knowledge Proof Generation

P generates ZK Proof that selectively discloses information about received data:

Selective Disclosure: Conceals sensitive parts while proving necessary facts
- Example: “Balance exceeds $50,000” (exact amount private)
- Example: “Age 21+” (exact birthdate private)
- Example: “Korean resident” (exact address private)
Verifier Signature: V (Notary) signs and notarizes data provenance
Tamper-Proof: Ensures data integrity through cryptographic hashing

Phase 4: On-Chain Verification (Optional)

Records generated ZK Proof on blockchains including Ethereum, Polygon, Solana
Smart contracts automatically verify proof validity
Anyone can publicly audit proofs
Can have legal validity as permanent, immutable evidence

Technical Architecture:

Client (Prover) ↔ Notary (Verifier) ↔ Web Server
       ↓
    MPC Protocol
    - Garbled Circuits
    - Oblivious Transfer
       ↓
    TLS Handshake
    - Pre-Master Secret generation
    - Session Key derivation
       ↓
    Data Transmission
    - Encrypted communication
    - Integrity assurance
       ↓
    ZK Proof Generation
    - Data commitment creation
    - Notary signature acquisition
    - Selective information disclosure
       ↓
    On-Chain Verification (Optional)
    - Smart contract verification
    - Public proof storage

Performance Metrics:

Proof generation time: < 450ms (average)
Proof size: < 10KB
Verification time: < 100ms
Network latency impact: MPC is latency-sensitive

Constraints (transparent disclosure):

MPC execution time is sensitive to network latency.
Greater physical distance affects Proof generation consistency.
Solution: Regional Notary node deployment (Phase 2B)

Use Cases:

Finance (DeFi Undercollateralized Lending)

Proof: "This user's bank balance exceeds $50,000"
Disclosure: Exact amount private, only range proven
Application: Approve collateral-free loan

Legal (Digital Evidence)

Proof: "This webpage displayed this content at 2025-01-15 10:30:00"
Disclosure: Page screenshot + TLS signature + timestamp
Application: Usable as court evidence

Healthcare (Patient Data Verification)

Proof: "This patient received negative COVID-19 diagnosis"
Disclosure: Only diagnosis result, personal information private
Application: Travel certificate, workplace return confirmation

Core Differentiators

vs. Traditional Web Scraping Solutions (Puppeteer, Selenium)

Item	Traditional Solutions (Puppeteer/Selenium)	Sela Network
Infrastructure	Central server (requires direct management)	Decentralized DePIN network
Bot Bypass	Average 78.5% success rate (Puppeteer)	98.7% success rate (real browsers)
Data Verification	Impossible (only screenshots)	zk-TLS cryptographic proof
Scalability	Server capacity limited	Linear scaling with node additions
Geographic Distribution	Manual proxy configuration required	Automatic regional node selection
Parsing	Manual CSS selector authoring	Automatic JSON-LD generation
Cost (1M requests)	~$8,500 (including infrastructure)	~$1,200-2,000

vs. Centralized API Providers (Browserbase, BrightData)

Item	Browserbase	BrightData	Sela Network
Price/1M requests	$4,000-6,000	$15,000	$1,200-2,000
SPOF Risk	Yes (central server)	Yes (central server)	None (distributed nodes)
Concurrency Limit	50 (Startup), 100+ (Scale)	Plan-based limit	Unlimited (network scale based)
Data Verification	Session Replay only	None	zk-TLS proof
AI Optimization	Manual parsing required	None	Automatic JSON-LD generation
Censorship Resistance	Vulnerable (central server)	Vulnerable	Strong (distributed structure)

vs. Semantic Web Standards (Schema.org)

Sela complies with and extends existing Schema.org standards:

Item	Schema.org (Webmaster Manual Addition)	Sela Network
Coverage	Only pages with webmaster additions	All websites (automatic generation)
Accuracy	100% (manual authoring)	98-99% (AI generation)
Updates	Manual (possible delays)	Real-time automatic
Coverage	~30% of internet	100% of internet (goal)

Detailed Technology Stack

┌─────────────────────────────────────────────────────┐
│     AI Agent / Application Layer                    │
│     - LangChain, AutoGPT, CrewAI                    │
│     - Custom AI Agents                              │
├─────────────────────────────────────────────────────┤
│  L3: Verifiability Layer (zk-TLS)                   │
│     - TLSNotary Protocol                            │
│     - MPC (Garbled Circuits, Oblivious Transfer)    │
│     - ZK Proof Generation & Verification            │
│     - On-Chain Proof Storage (Ethereum, Polygon)    │
├─────────────────────────────────────────────────────┤
│  L2: Semantic Interpretation Layer                  │
│     - DOM Parser (99% cases)                        │
│     - LLM (1-5% cases)                              │
│     - JSON-LD Generator                             │
│     - Self-Healing Selector System                  │
│     - Schema.org Compliance                         │
├─────────────────────────────────────────────────────┤
│  L1: Web Transport Layer                            │
│     - Distributed Browser Nodes (Chrome, Firefox)   │
│     - Residential Proxy Network                     │
│     - Session Manager (Cookie, Auth State)          │
│     - Load Balancer & Node Selection                │
│     - Fingerprint Management                        │
├─────────────────────────────────────────────────────┤
│          The Web (HTTP/HTTPS)                       │
│     - Target Websites (All websites)                │
└─────────────────────────────────────────────────────┘

Key Benefits

1. Benefits for AI Agent Developers

Implement Complex Web Interactions with Simple API

Traditional approach:

# 200+ lines of Puppeteer code
from selenium import webdriver
from selenium.webdriver.common.by import By
# ... complex configuration ...
driver.get("https://amazon.com")
driver.find_element(By.ID, "twotabsearchtextbox").send_keys("airpods")
# ... bot detection bypass logic ...
# ... CSS selector management ...
# ... data parsing logic ...

Sela approach:

# 3 lines of code
from sela_network import SelaClient
client = SelaClient(api_key="your_api_key")
result = client.browse("amazon.com", query="airpods", format="json-ld")

Stable and Consistent Data Schemas

Schema.org standard compliance
Identical JSON structure guaranteed every time
Resolves LLM hallucination problems

Minimize Maintenance Burden

Self-Healing Selector: Automatic response to UI changes
Bot detection bypass: Automatic processing (98.7% success rate)
No infrastructure management needed: Fully managed service

Rapid Prototyping

LangChain native integration
Support for major AI frameworks including AutoGPT, CrewAI
REST API and SDK provided (Python, JavaScript)

2. Benefits for Node Operators

Token Reward Acquisition

$SELA token rewards proportional to browser execution time
Additional rewards based on bandwidth contribution
Differential rewards by performance tier (Bronze ~ Platinum)

Simple Installation

Chrome extension installation (5 minutes required)
No separate hardware investment needed
Background execution (no impact on daily browsing)

Global Network Participation

Part of DePIN ecosystem
150+ country network participation
Regional representation rewards (2x rewards for low-density regions)

Transparent Reward System

Blockchain-based transparent reward distribution
Real-time earnings dashboard
Predictable ROI

3. Benefits for End Users

More Powerful AI Agent Services

Interaction possible with all websites
Real-time data collection and decision-making
Automated transactions and reservations

Data Privacy Guarantee

Zero-Knowledge proofs protect sensitive information
E2E encrypted session management
GDPR, CCPA compliance

Verifiable Reliability

zk-TLS proof attached to all data
Cryptographic verification of data provenance possible
Tamper-proof audit trail

Innovative Automation Experience

Automation possible for websites without APIs
Complex workflows implemented simply
Non-developers can use No-Code builder (Phase 3)

Technical Innovation Summary

Sela Network transforms the AI agent web interaction paradigm through three core innovations:

Distributed Browser Network: Leverages real user browsers to bypass bot detection and build resilient infrastructure without single points of failure.
Semantic Interpretation: Automatically converts all websites to AI-understandable JSON-LD format through Vision + DOM hybrid parsing.
Cryptographic Verification: Proves web data provenance and integrity through zk-TLS protocol, enabling use in high-trust domains (finance, legal, healthcare).

Project Start: 2024 Last Updated: November 23, 2024 Version: 2.0 (Comprehensive English Edition)