ECR-RAG v2.1

Next-Generation RAG.

Entropy-Controlled Retrieval — the only path to zero-hallucination systems.

GitHub Prototype How it works

99%

Accuracy

40%

Savings

∞

Sources

ECR Retrieval Loop

Problem

The "Confetti Effect" Kills Accuracy.

Traditional RAG systems slice documents into fixed chunks (e.g., 512 tokens), ignoring semantic boundaries. This destroys the logical structure of knowledge.

Facts from different sections get mixed up
Context cuts off mid-thought
Impossible to trace the source

Legacy Chunking

chunk_01:

"...plan costs $29/mo. Premium plan offe..."

chunk_02: "...rs unlimited API. Company founded..."

LLM Result (Hallucination)

"Basic plan offers unlimited API..."

Legacy RAG vs ECR-RAG

Key Metrics

Parameter	Legacy	ECR-RAG
Fact Accuracy	60-80%	99%+
Context	Noise + Repetitions	Optimal (-40%)
Structure	Flat Chunks	Content Envelope
Routing	Linear	6 Intent Strategies

ECR-RAG transforms search from a 'guessing game' into a verifiable system.

In the Legacy approach, answer quality depends on which text chunks accidentally land in the context. In ECR-RAG, the answer is built from a coherent envelope: facts are tied to anchors, routing selects the correct layer, and context remains short and precise.

The table on the left isn't just 'marketing percentages'—it's a fundamental difference in mechanics: less noise, more structure, and controlled context assembly.

Architecture

How ECR-RAG Works

Instead of blind vector search — intelligent routing of queries to the correct Content Envelope layer.

Query Path

Query

"What is the price of the Premium plan?"

Intent Classifier

FACT_EXTRACTION

Extraction Router

Structure → Anchor → Narrative

Content Envelope

Assembly from the correct layer — zero noise

Layer 01: Structure

Typed Entities

{ price: $29, anchor: "#pricing" }

Layer 02: Narrative

Clean Markdown

Semantically correct sections with deep context.

Layer 03: Index

Stable Anchors

Persistent IDs for context assembly.

Key Difference

Traditional RAG

query → vector_search → random_chunks → LLM

ECR-RAG

query → classification → routing → coherent_envelopes → LLM

Routing

6 Strategies for 6 Intent Types

Each query type requires a distinct approach. ECR automatically classifies the incoming stream.

Query Type

Select an intent — plan appears on the right

Input Layers

Structure

Narrative

Index

Extraction Route

Context

Returns

Algorithm

Example

Integration

One-Call Integration

The entire pipeline is a single function. Minimum code, maximum accuracy.

ingestion_pipeline.py

from aio_core.pipeline import AIOPipeline

pipeline = AIOPipeline()

# Process any content → Content Envelope
result = pipeline.process_with_report(
    html_content,
    source_url="https://example.com"
)

envelope = result["envelope"]
print(f"Noise: {result['report']['noise_score']:.0%}")

intent_retrieval.py

from aio_core.retrieval import IntentClassifier

classifier = IntentClassifier()

query = "What's the price of Premium?"
result = classifier.classify(query)

print(result.intent)      # FACT_EXTRACTION
print(result.strategy)    # structure_first
print(result.confidence)  # 0.98

Technical Standard

Anatomy of Content Envelope

The ECR pipeline output is a universal JSON container with three layers of data linked via anchors. AI agents verify facts instantly without re-reading.

Source Layer

Source metadata, URI, data type, and verification hash.

Narrative Layer

Clean text, split into semantic sections with unique IDs.

Structure Layer

Typed entities (JSON-LD) with references to Narrative anchors.

content-envelope.aio.json

{
  "header": {
    "version": "2.1",
    "source_uri": "https://example.com/pricing",
    "content_hash": "sha256:a1b2c3..."
  },
  "narrative": {
    "sections": [
      {
        "id": "#pricing",
        "content": "Premium plan costs $29/mo..."
      }
    ]
  },
  "structure": {
    "entities": [
      {
        "@type": "PriceSpecification",
        "price": 29,
        "anchor_ref": "#pricing"
      }
    ]
  }
}

Ecosystem

Out-of-the-box Integrations

No-Code Platforms

Ready-made modules for automation without coding.

n8n Ready

Make.com Webhook

AI Frameworks

Drop-in Retriever replacement for popular RAG libraries.

LangChain Retriever

LlamaIndex QueryEngine

Custom Code

SDK in 5 languages + full compatibility with any Vector DB.

Python Stable

Node.js Beta

Any Vector Database

ECR uses an abstract IndexInterface — connect any backend.

Pinecone

Milvus

Weaviate

Qdrant

Use Cases

ECR-RAG Implementation Across Industries

From fintech to medicine: where accuracy and structured approach are critical.

Fintech

→Precise tariff search
→Risk analysis (KYC/AML)

Jurisprudence

→Precedent search
→Contract generation

Medicine

→Doctor's assistant (CDSS)
→Medical record analysis

E-commerce

→Smart product search
→Personalization

Tech Support

→L1 Auto-replies
→Ticket classification

Education

→Test generation
→Assignment grading

HR

→Resume screening
→Onboarding

Media

→Fact-checking
→News summarization

Business Value

Not just accuracy. Time and money.

ECR is a technology that saves resources from day one.

-40%

API Costs

Fewer tokens = lower bills

-90%

Manual Verification

99% accuracy = minimal moderation

-70%

Development Time

Ready-made routing strategies

6×

Faster AI

Less compute on inference

Enterprise Scale (1M+ requests/day)

$50k - $200k / year

Projected savings due to context optimization and hallucination elimination.

Standard RAG High Costs

AIO RAG -70% costs

Scientific Foundation

Based on Cognitive Vulnerability Theory

ECR is the practical implementation of the G-model from the Theory of Stupidity. Routing optimizes attention ($A$), noise stripping minimizes entropy ($D$).

\[G_{machine} = \alpha_1 \frac{B_{err}}{I} + \alpha_2 \frac{D_{eff}}{A}\]

$G_{machine}$ ↓ | $D_{eff}$ ↓ | $A$ ↑

Theory of Stupidity ECIA Research