Solutions
The platform
coming soonAmmonixCode
Our architecture-native coding agent, purpose-built to create systems based on the Ammonix architecture. A physician, engineer, scientist or other domain expert provides the knowledge and defines what is correct; AmmonixCode guides the creation of the knowledge universe, the training data, the evaluation, the specialist model, and the user interface.
Use cases
Three Reference Implementations
In benchmarked evaluation across these domains, Ammonix specialist agents demonstrate higher task-specific accuracy, no hallucinations, and vastly reduced compute footprints compared to general-purpose frontier models. In our most recent registered comparison, the Ammonix claims agent matched the strongest GPT-6 configuration on outcomes while using 4.7 times fewer GPT-6 tokens. All three run as live demos you can try yourself.
WaveMedix ECG interpretation and advanced clinical diagnostics
Medical
An agent that reads 12-lead ECGs and explains every finding with the evidence behind it. It works from the features cardiologists already use, places each recording next to the most similar recorded examples, and shows how those examples ended. When a recording is unclear, it says so instead of guessing, and the physician always keeps the final word. It is deployed by WaveMedix, the medical arm of Ammonix.
Healthcare revenue-cycle and claims workflow automation
Administration
An agent that prepares healthcare claims and learns from how payers actually decide them. Every answer a payer gives, paid or held, enters its memory and sharpens the next submission, with dollars collected as the score that matters. Every submission it writes can be traced back to the rules and past examples behind it, so a biller can defend any line it produced.
Waste-to-energy control room decision support
Industrial Operations
An agent that supports control-room operators in real time. It watches the plant's signals, retrieves the most similar past situations, and recommends the next move based on how those situations actually ended. Hard safety rules filter every option before anything is ranked, and the operator always makes the final call.
Digital twins
Experience From Simulation
Many organizations have deep expertise but few recorded examples. Ammonix builds the missing experience by creating digital twins through modeling of the relevant processes: starting from a known answer, a generative model writes realistic examples of it, the way an examiner writes questions for an answer they already know.
This works because the model is never asked to answer a live example, where an invented answer could not be checked. It only writes training examples, and its mistakes show up as visible patterns in a dataset, measurable against real examples and correctable. Real examples enter the same knowledge universe as they happen and override the simulation wherever reality disagrees.
The architecture behind these agents is in our Foundation paper.