Bringing domain expertise to LLMs.
LLMs are optimized for general textual reasoning, so they often struggle in spatial or niche domains. Decypherly gives a model genuine understanding of the domain itself — so its answers come from real insight, and it can explain them in plain language.
Most tools that help LLMs reason in niche or complex topics just use a harness with prompt engineering and tools.
Decypherly fundamentally instills understanding.
A harness provides tools and prompt engineering to help improve the performance of the model. However, the models are only aided, not truly optimized, and prompt engineering quality is model-specific.
The understanding is built into the model itself, so it is not scaffolding that has to be rewritten for every new model. The reasoning holds up on the cases nobody wrote a prompt for.
One architecture, many domains.
Neural networks have long been black boxes, but with Decypherly, we can start breaking down their reasoning.
Markets move on volatile, complex patterns across hundreds of variables, and a network can learn to predict them without ever explaining why. Decypherly puts an LLM behind that prediction to reason out the case for it.
Faline, a privacy-first litter-box health monitor, reads the earliest signs of illness from a cat. Decypherly powers its analysis, so every health flag comes with reasoning a person can verify.
Simple, hard-coded rules cannot handle every situation a robot encounters, but a traditional neural network turns that decision-making opaque. Decypherly's LLM reasons through the decision in natural language, so it can be as transparent as it is capable.