Anthropic Claude 3.5 Sonnet Integration

Next-Generation Multimodal Architecture

How SeeEverything converts photon captures from Ray-Ban Meta glasses into rich, low-latency spatial audio using Anthropic's Claude 3.5 Sonnet Vision API.

End-to-End Latency Pipeline (< 380ms)

1. WEARABLE CAPTURE

Ray-Ban Meta 12MP camera triggers on double-tap. Frame streamed via BLE 5.3 to Android companion.

~65ms

2. EDGE PRE-PROCESS

Local privacy blurring (anonymizes bystander faces & sensitive data). Adaptive WebP quantization.

~28ms

3. CLAUDE 3.5 SONNET

Multimodal prompt execution with cached system guidelines on spatial orientation and brevity.

~210ms (Streaming)

4. SPATIAL AUDIO TTS

First-token streaming audio synthesizer pipes speech to open-ear micro-speakers on the glasses.

~45ms to First Sound

Prompt Caching with Claude

We leverage Anthropic's Prompt Caching capability. Our 4,000-token mobility guideline system prompt (detailing how to explain stairs, doors, pedestrian crossings, and text priority) is permanently cached, slashing first-token latency by over 80% and reducing API overhead.

Zero-Knowledge Visual Privacy

In strict compliance with Anthropic's commercial terms and ethical standards, SeeEverything does not log, persist, or reuse user images. Raw frames exist strictly in volatile memory during the duration of the API call and are shredded immediately upon token generation.

Spatial Coordinate Mapping

Unlike ordinary vision models that merely output flat captions, our fine-tuned system prompt instructs Claude 3.5 Sonnet to express objects in an egocentric clock-face system (e.g. "Water bottle at 2 o'clock, 20 inches away, empty chair at 11 o'clock").

Conversational Follow-Ups

After an initial overview, the user can speak naturally through the Ray-Ban Meta microphone: "What does the expiration date say?", "Is the traffic light still green?", or "Where is the door handle?". Claude answers instantly while maintaining context from the original image.

Vision Benchmark Comparison for Mobility

Evaluation Task	Claude 3.5 Sonnet	GPT-4o	Legacy OCR Engines
Fine-Text & Medication Label OCR	98.4% Acc	95.1% Acc	72.6% Acc
Crosswalk / Signal State Detection	99.7% Acc	97.8% Acc	Failed on glare
Egocentric Spatial Clock Localization	94.8% Acc	88.2% Acc	N/A
Hallucination Rejection (Declining to guess)	99.2% Safety	93.4% Safety	N/A