ENGO Header

ENGO

The missing layer of chain context for the cognition of tomorrow

Compressed signals, distributions, and baselines tailored for AI agents.

What problem are we solving?

Ethereum is a dark forest. Yet all of the data there is public. How's so?

Because of the thousands of transactions hourly, each of them in hex-aneese - there is no simple way to see and understand what's happening on the chain right now, especially for an AI agent.

It's tricky to see the dominant types of activities, patterns out there; almost impossible to catch trends early, if you don't know where and how to look.

It's all just a messy bouillon of data, forcing you to rely on:

  • •raw node calls glued together with prompt tricks
  • •noisy dashboards & biased influencer narratives
  • •weeks of custom ETL for every new idea

None of these give a true contextual view of what's buzzing on the chain right now.

ENGO is aimed at closing this gap

ENGO transforms live blockchain data into multi-layered context.

  • •650+ curated metrics per block window of different granularities
  • •Baseline comparisons across 4h / 24h / 7d / 30d / 90d + cyclic cadences (hour-of-day, weekday)
  • •Distributions, deviations, cash flows, swap activity, wallet behaviors, and more
  • •
    Exported as:
    • ◦LLM optimized Markdown
    • ◦XGBoost ready vectors
    • ◦JSONs for your own custom logic

Designed to plug directly into LLMs, agents, quant models, and internal tools without rebuilding the data stack each time.

Numbers

Over 13 billions LLM tokens consumed in structured experiments, plus 60+ XGBoost / classical ML runs.

AI Trading Agents (ETH/USDC spot):

  • •~45% monthly performance in trending markets
  • •Up to +7% market outperformance during drawdowns
  • •All signals derived purely from ENGO context

Classical ML Benchmarks:

  • •65–70% directional accuracy for 5-minute ETH/USDC TWAP (XGBoost & NNs)
  • •~60%+ accuracy for gas, volatility shifts, and behavioral metrics
  • •Stable across dozens of independent experiments

Reports with more details are available upon request

Vision

Context is the future interface between LLMs and complex systems.

As cognitive systems evolve, they will not parse raw data feeds or million-token dumps. They will rely on domain-compressed context layers — distilled, structured, deviation-aware representations.

ENGO aims to become the canonical lens through which LLMs perceive on-chain worlds:

  • •enabling agents that reason, not hallucinate
  • •powering LLM copilots for research, trading, and discovery
  • •augmenting quant desks, funds, analytics platforms, agent frameworks, and data infra teams
  • •offering a generic blueprint for how AI systems will understand any dynamic domain

Blockchain is the first environment.
The method extends far beyond it.

Bring cognition into your on-chain stack.