Trendence.ai
  • Introduction
    • The AI Fragmentation Problem
    • Core Problems
    • What is Trendence?
  • Vision and Mission
    • Vision
    • Mission
  • Trendence Solution
    • Trendence Solution
  • System Architecture
    • Architecture Approach
    • Application Layer
    • Orchestration Layer
    • Data Layer
    • Execution Layer
  • Terminal
    • Agent Categories
    • Modes
    • Evaluation & Ranking
  • Developer Ecosystem
    • Integration via API/SDK
    • Contribution Mechanisms
    • Quality Control System
  • Token Utility ($TREND)
    • $TREND
  • Business Model
    • Subscription Tiers
    • API/Data Licensing Revenue
    • Agent Monetization Split
  • Governance
    • DAO Governance
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  1. System Architecture

Data Layer

The Data Layer serves as the backbone of Trendence’s intelligence infrastructure, enabling high-quality data ingestion, enrichment, and distribution across all orchestration and agent execution processes. It ensures that every query, prompt, and response is supported by verified, up-to-date, and contextually relevant information.

This layer integrates both off-chain APIs and on-chain blockchain data, standardizing inputs for optimal agent performance and query resolution.

Key Components:

1. Integrated Data Feeds

Trendence connects with leading data providers such as CoinMarketCap, Dexscreener, Etherscan, Tweetscout, Solscan, and others to pull structured, up-to-date data.

Data types include:

  • Token prices, volumes, and market cap

  • Exchange listings, trading pairs, and liquidity metrics

  • Protocol-level metadata and fundraising history

  • Sentiment signals from social platforms

  • Project roadmaps, unlock schedules, and token metrics

Trendence also approaches the integration of basic foundation models to ensure the coverage of the non-specific data for users like:

  • Anthropic Claude 3.7, 3.5 Sonnet

  • Facebook Llama 3.2

  • AWS Nova Pro, Lite

  • GPT-4.5, GPT-4o

  • Gemini 2.5 Pro

  • DeepSeek-V3

All incoming data is normalized and cached, enabling low-latency access for both agents and external API consumers.

2. Historical Storage & Memory Systems

A persistent memory layer stores:

  • User query logs and agent routing paths

  • Aggregated multi-agent outputs for future reuse

  • Performance scoring over time for agents and models

This allows the Mastermind to learn from past executions, optimize agent selection dynamically, and surface historical insights where relevant. It also supports meta-analytics on agent efficiency and reliability over time.

3. On-Chain Data Connectors

Trendence supports blockchain-native analytics through connectors to networks like Ethereum, Solana, Base (with the further developments more chains are to be integrated). This includes:

  • Wallet-level transaction data and behavioral patterns

  • Token minting/burning events

  • Protocol activity, staking flows, and liquidity tracking

  • Cross-chain asset flows and DEX trade data

On-chain data is pre-processed and mapped to token, protocol, and wallet metadata to enable more granular, accurate DeFi and trading analysis.

On-chain data is pre-processed and mapped to token, protocol, and wallet metadata to enable more granular, accurate DeFi and trading analysis. The Data Layer not only feeds the orchestration engine - it amplifies the intelligence of every connected agent, ensures consistency across the ecosystem, and serves as a critical component of Trendence’s long-term memory and learning stack.

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Last updated 1 month ago

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