Most AI hands you a prediction you then have to defend. Kailume hands you the equation behind it. Readable on its own, and the only kind of output regulators, patent attorneys, and peer reviewers will accept at face value.
Built by Timo Lassmann at The Kids Research Institute Australia. Two decades extracting trustworthy insights from large, noisy datasets, on projects that have shaped entire scientific fields.
Not a prediction. An equation.
Readable. Patentable. Defensible.
// what's.hiding
Kailume is a scientific discovery service. Give it your data; it returns transparent, interpretable mathematical relationships your team can read, validate, and run. Real equations, not black boxes.
Most AI gives you one ‘optimal’ model. But optimal for whom? The engineer who needs simplicity? The regulator who needs transparency? The researcher who wants mechanistic insight? Kailume produces a portfolio of valid equations, each representing a different trade-off between accuracy, simplicity, and practicality. Every output is readable, testable, publishable, and ready for regulatory submission. Your team chooses the one that fits.
Your data was expensive to collect. Kailume is for organisations who want to know it's been mined for every defensible insight, and that those insights will outlast the engineer who built the model, the vendor who licensed the tool, and the next round of regulatory scrutiny.
// why.choose
Standard AI optimises for accuracy alone. Kailume explores broadly and brings back equations that are accurate, transparent, and defensible. Same statistical rigour. Different kind of output.
01
Standard explainability tools build a black-box model first, then approximate an explanation afterwards. Kailume outputs are the explanation - every result is a human-readable formula that domain experts, reviewers, and regulators can assess directly. No post-hoc interpretation layer needed.
02
Kailume returns a curated set of high-performing alternatives, each using different variable combinations and a different set of trade-offs. You choose the formula that fits your real-world constraints, whether they are scientific, operational, regulatory, or IP-related.
03
Kailume runs on the structured datasets your team already collects, whether that is lab measurements, process logs, financial records, or sensor streams. It picks out which variables matter, ignores the redundant ones, and returns equations that drop straight into your existing pipeline.
04
Most tools find an accurate model and stop there. Kailume's wider search also rules out variables you cannot measure in practice, and removes redundant variables that just restate each other. The equations you get back use inputs your team already collects, in a form ready to act on.
// the.origins
Kailume was built by Timo Lassmann at The Kids Research Institute Australia, after two decades extracting reliable, interpretable relationships from some of the noisiest datasets in science.
The same problem now faces every organisation working under the EU AI Act, FDA AI/ML guidance, Basel model-risk standards, or peer review. Kailume is his answer.
Equations from biology's hardest data, now from yours. Powered by The Kids Research Institute Australia.
// get.in.touch
Tell us what you're trying to predict or explain, and what your dataset looks like. You'll get back a portfolio of valid equations. Readable, testable, and ready to defend.
Data privacy
Your data is never retained between engagements or used to train shared models.
Turnaround
Most engagements deliver a full equation portfolio within days of receiving the dataset.