# AI Retrieval

# AI Retrieval

AIEP exists because AI systems are increasingly used to find information. Search is being replaced by conversation. But conversation is only safe when it can anchor itself to reliable knowledge.

AIEP enables a new pattern: **evidence-backed knowledge retrieval**.

## What retrieval means in AIEP

An AI system does not start by ranking pages. It starts by discovering a publisher’s machine interface. It retrieves artefacts from the source, then validates structure, integrity, and policy signals.

A typical AIEP retrieval sequence looks like this:

1. Discover `/.well-known/aiep/index.json`  
2. Read `/.well-known/aiep/metadata.json`  
3. Follow surfaces to indexes, schemas, ledgers, and artefacts  
4. Validate artefacts against schemas  
5. Check hashes where available  
6. Separate consensus from outliers  
7. Synthesise an answer with evidence references

## Why this improves safety

The core risk with AI retrieval today is that models improvise around missing ground truth. AIEP reduces that risk by giving models a predictable way to retrieve supporting artefacts from publishers who choose to publish them.

AIEP does not remove judgment. It improves the quality of what judgment is based on.

## Dissent and plausibility

AIEP treats dissent as a structural feature. Retrieval can intentionally surface:
- the consensus view
- competing interpretations
- outliers and radical outliers

This supports scientific discovery and prevents premature collapse into a single narrative.

## The goal

AIEP aims to make it normal for AI systems to rely on published evidence rather than probabilistic guesswork.

**The future of information retrieval is not search — it is evidence-backed knowledge retrieval.**