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Galen – a structured layer between humans and AI

Galen – a structured layer between humans and AI

by drgeorgealex·Apr 22, 2026·2 points·0 comments

AI Analysis

●●SolidBig BrainBold Bet

Intermediate representation layer for AI instructions could reduce tokens, but product-market fit remains unproven.

Strengths
  • Structured instruction format works across different AI models and domains
  • Specific token reduction claims (30-70%) are measurable and testable
  • Patent-pending approach suggests genuine architectural novelty
Weaknesses
  • Early prototype stage with unclear path to production product
  • Competes with existing prompt engineering frameworks and structured prompting patterns
Category
Target Audience

AI developers, enterprise teams building AI workflows

Similar To

LangChain · DSPy · Prompt templates

Post Description

I’m a surgeon and have been experimenting with the idea that AI may need a more structured interface than raw natural language.

I built an early prototype called GALEN.

Instead of sending long-form speech or text directly to an AI model, GALEN converts it into a compact, structured instruction format first:

speech/text → structured representation → AI

The aim is not only to reduce token usage, but also to improve consistency, determinism and reliability across different AI systems.

So far the prototype appears to: - reduce AI input overhead by roughly 30–70% - work across multiple domains (healthcare, legal, finance, travel, etc.) - allow the same structured instruction to be sent to different models

Still very early and patent pending. I’m trying to work out whether there is genuinely a useful product here beyond the prototype.

Demo: https://galenvoice.com

Would really appreciate any thoughts or criticism.

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