Bringing domain expertise to LLMs.
Decypherly connects LLMs to two specialized neural networks, adding domain representation so they can reason through specialized tasks without scaling.
A harness provides tools and prompt engineering to help improve the performance of the model.
The LLM connects directly to two specialized neural networks, circumventing its weakness in representation while keeping its advantage in reasoning.
One architecture, many domains.
Neural networks have long been black boxes, but with Decypherly, we can start breaking down their reasoning.
Chess engines identify strong moves, but their scores and lines hardly explain why they work. Decypherly connects the LLM to two specialized neural networks for chess, giving it a stronger representation of the board so it can explain positions in plain language.
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 you can verify.
Simple, hard-coded rules cannot handle every situation a robot encounters, but traditional neural networks can make that decision-making opaque. Decypherly's LLM reasons through the decision in natural language, so it can be as transparent as it is capable.
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