One bacterium, one Raman spectrum — and a self-auditing AI that reports the accuracy which survives new data, not the impressive number that collapses the moment it leaves the lab.
Duviloper is a core AI lab — self-auditing AI and signal-processing rigor for high-stakes science.
Anyone can chase a benchmark. We build systems that interrogate themselves — and tell you when the answer simply isn't in the data. We call it self-auditing AI.
We hunt the data leaks that fake impressive results — so your numbers hold up after they leave the lab.
We measure what a model can actually resolve versus merely memorize — the real limit of what's learnable.
We prove the model is reading the real signal — not riding a hidden artifact or correlation in the data.
We quantify what's knowable from the signal. When the evidence isn't there, we report it — not a confident guess.
AI is the engine. Signal processing is how we think, biotech is where we prove it, and platform design is how we ship it.
We design, audit, and prove deep models for problems that resist easy answers — built signal-aware, not black-box, and validated against our own adversarial audits before they ever reach you.
We treat data as signals. DSP discipline — spectra, waveforms, multi-resolution analysis — applied to messy, real-world data others give up on.
Our flagship reads antimicrobial resistance from Raman spectra of bacteria — a hard, real, high-stakes test of the whole method.
Research that ships. We build the tools that make rigorous methods explorable, reproducible, and usable by the scientists who need them.
Our flagship applies self-auditing AI to Raman spectroscopy of bacteria — predicting antimicrobial resistance from a single spectrum in minutes, bypassing the 1–2 days traditional culturing needs. We describe the approach and its audits: the leakage tests, the capacity ceiling, and the confounds we ruled out. The model and method are patent pending.
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A pipeline designed so the model can't quietly cheat — and so we find out fast when a problem isn't solvable yet.
Define the decision and the real cost of being wrong.
Stress-test the data for leakage, confounds, and detectability — before modeling.
Engineer the neural system — signal-aware, interpretable where it counts.
Validate against the audits, not just a leaderboard score.
Deliver as a usable, reproducible tool your team can trust.
Tell us the problem. If signal-processing rigor and self-auditing AI can move it, we'll tell you straight — including if we can't.