InfoPoint Whitepaper - A DID-Native Spatial Discovery and AI Retrieval Infrastructure

Version 0.9 Draft

Executive Summary

InfoPoint is a decentralized spatial discovery and semantic retrieval infrastructure designed for the AI-native internet.

The system transforms physical spaces into machine-readable semantic entities through:

Unlike traditional IoT systems, InfoPoint does not attempt to convert edge hardware into miniature web servers. Instead, InfoPoint adopts a stateless spatial beacon architecture:

This architecture is optimized for:

The system is applicable across:

1. Problem

Modern internet infrastructure indexes webpages rather than physical space.

Discovery of nearby information still depends on:

Physical spaces lack:

Traditional beacon systems failed because they only broadcast URLs and lacked:

The result is a fragmented and largely non-programmable physical environment.

2. Design Principles

InfoPoint follows several architectural principles:

2.1 Minimal Edge Hardware

Edge devices should not host complex applications, AI models, or databases.

The node should only:

2.2 Decentralized Spatial Identity

Each physical space should possess:

2.3 AI-Native Retrieval

The system is designed for semantic spatial retrieval rather than keyword search.

Spatial context becomes directly consumable by AI systems.

2.4 Local Spatial Intelligence

Spatial graphs should primarily exist on user devices rather than centralized cloud services.

This improves:

3. System Architecture

InfoPoint consists of six layers.

4. Hardware Architecture

InfoPoint hardware is intentionally lightweight.

The node is not a web server.

The node does not:

The node only:

4.1 Recommended Hardware

BLE + LoRa Integrated Devices

Recommended development boards:

4.2 Required Capabilities

5. Spatial Discovery

5.1 BLE Discovery

BLE is used exclusively for nearby discovery.

BLE advertisements contain:

Example:

{
  "v":1,
  "id":"cafe001",
  "t":"cafe"
}

The app scans nearby BLE packets and discovers nearby InfoPoint nodes.

5.2 QR Code Discovery

InfoPoint also supports QR-based entry.

QR codes provide:

Example restaurant deployment:

A restaurant may deploy:

Restaurant DID
↓
Table QR Code
↓
Specific Runtime Context
↓
Order Session

This enables:

QR and BLE are complementary:

6. LoRa Spatial Synchronization

LoRa is not used for content delivery.

LoRa is used for:

Example synchronized index:

{
  "did":"did:abt:cafe001",
  "type":"cafe",
  "lat":40.712,
  "lng":-73.99,
  "tags":["quiet","wifi","workspace"]
}

This allows a user to discover many nearby spaces through a single reachable node.

7. DID Identity and Resolution

Each space owns a DID.

Example:

did:abt:cafe001

The DID represents:

InfoPoint uses ArcBlock DID infrastructure.

7.1 DID Resolution Flow

BLE / QR
↓
DID
↓
InfoPoint Resolver
↓
Forge Account Metadata
↓
Runtime Endpoint
↓
Spatial Runtime

7.2 DID Metadata Structure

Example metadata:

{
  "infopoint": {
    "runtime":"https://space.infopoint.ai/cafe001",
    "resolver":"https://resolver.infopoint.ai",
    "spaceType":"cafe",
    "services":[
      "menu",
      "booking",
      "event",
      "ai-guide"
    ]
  }
}

7.3 Resolver Design

The resolver layer is intentionally separated from DID ownership.

Blockchain provides:

The resolver provides:

This separation enables runtime flexibility without modifying chain state.

8. Spatial Runtime

InfoPoint runtimes are implemented as Blocklet applications.

Each physical space corresponds to a programmable runtime.

8.1 Runtime Capabilities

Core runtime functions:

8.2 Plugin System

The runtime uses a plugin architecture rather than fixed industry templates.

Examples:

This enables a single runtime framework to support multiple industries.

9. AI-Native Spatial Retrieval

The spatial graph is primarily maintained on the mobile application side.

The app gradually accumulates:

This architecture aligns with recent Spatial-RAG research.

9.1 Retrieval Pipeline

User Query
↓
Semantic Parsing
↓
Spatial Filtering
↓
Local Spatial Graph
↓
Semantic Ranking
↓
AI Response

9.2 Example Query

User asks:

Nearby quiet cafes suitable for work

The system combines:

This produces contextual semantic recommendations rather than static map results.

10. Local Spatial Intelligence

InfoPoint prioritizes local spatial intelligence over centralized cloud indexing.

Advantages:

The app becomes a local AI-native spatial retrieval engine.

11. Security and Trust

InfoPoint uses DID-based ownership and identity verification.

The system separates:

This minimizes coupling between trust, routing, and application execution.

12. Commercial Architecture

InfoPoint is infrastructure rather than a single-purpose application.

Potential commercial layers include:

The primary long-term value is:

13. Comparison

14. Conclusion

InfoPoint proposes a practical architecture for transforming physical spaces into decentralized semantic infrastructure.

The system combines:

to create a new category of infrastructure:

AI-native spatial discovery and semantic retrieval.

Rather than connecting devices, InfoPoint semanticizes physical space.