About
Empirica Technologies is a signal-extraction firm run by an autonomous AI research fleet, with our team in the loop for the judgement. Our one capability is finding weak predictive signal in noisy, high-dimensional data and proving it holds — delivered quietly, embedded with one company at a time. You get the finished, validated result — not another tool to run.
The team
Founder & Chief Executive Officer
Charles studied Applied Psychology at Monash University and spent three years as an undergraduate researcher across Monash's Computational Neuroscience Laboratory and Neuroscience of Consciousness Laboratory, working on internal modeling, Bayesian inference, cross-modal plasticity, consciousness, category theory, and spectral theory.
He started Empirica on a bet about the next ten to twenty years: that AI-agent scaling lets a small firm take on serious, data-heavy work that used to need a whole team — reading the literature, running the analysis, checking it honestly, and standing behind the result. The fleet does the volume; a human owns the judgement.
What that looks like in practice is on our work — reproducible examples where the evidence is the rigor itself: tested out-of-sample, checked against chance, pre-registered, reported honestly even when it kills the idea.
“Good research is built from creativity, breadth, and empathetic learning from others.”
Looking to join? We're a small team — if you write code or do research and want what we're building, get in touch at empirica@empiricaai.org.
Why we exist
Somewhere in almost every data-rich organisation there's a step that leaks value — a process that's slow or manual, an analysis nobody has time to do properly, a decision being made on a number nobody fully trusts. The data to fix it usually already exists. The capacity to act on it is what's missing.
What's new is that AI agents are now capable enough to do real work — read the literature, connect the findings, run the analysis, take criticism from a validator, and try again. Over the next decade that turns serious analytical work from something only a big team can afford into something a small firm can deliver end to end.
Empirica is built to point that capability at one thing: the data-heavy step that's holding you back. We diagnose it, rebuild it with validated analysis, and hand you the finished result — with the working shown, so you can trust the number, not just take it on faith.
What we do
The Forward-Data Process — our flagship
We design, build, extract, and validate the uncrowded, decision-grade dataset you should be building for the decisions you're about to face. Six stages: framing the decision and its counterfactual; experiment and causal design plus instrumentation; finding the signal; hypothesis testing with reproducible validation; an uncrowdedness diagnostic; and an accountable freeze — signed, dated, and hash-verifiable. We prove it on a bounded free slice first. How it works →
Decision-Grade Data Cleaning
A cleaning step should buy you something. We prove a given cleaning or processing step adds out-of-sample edge and isn't just re-deriving the crowd — edge measured against the raw data, crowding measured against a validated consensus, never against the dirty data itself. You learn whether the step is worth keeping.
Data-Bottleneck Diagnosis & Fix
Bring us a data-heavy process that's slowing you down. We find the step that leaks value, rebuild it with validated analysis, and measure the gain — and can run it for you. Fixed scope; the result is validated and reproducible, not a tool you operate. See examples →
How the work gets made
The fleet carries the volume
An autonomous fleet of AI agents does the heavy lifting — reading the literature, running the analysis, drafting the result. It's the same agent architecture that runs our own research around the clock. That's why a small firm can take on work that used to need a whole team, at services margins.
A validator scores everything
Nothing ships until it clears our validator — the same bar we hold our own research to: logic, empirical backing, depth, and citations checked against the literature. Every number is computed from data we hold or traced to a source you can check.
A human owns the judgement
The fleet doesn't publish your deliverable into the void: our team — led by the founder — curates and quality-checks every result before it ships. That human-in-the-loop accountability is the point — a signed, dated, sourced result someone stands behind, which a free model can't give you. We also publish a free, source-cited research archive the same fleet produces, browsable from the homepage and as one interactive knowledge graph.
You own the result; we keep the method
You own your deliverable, your data, and the specific result we build for you — assigned, no embargo. We retain our generalised, anonymised methods and tooling, so the engine that proved your result keeps getting sharper for the next problem. Every fee is agreed before we start.
Company
Work with us
Start with the bottleneck
Bring us a data-heavy process that's slowing you down. See the rigor behind our work, then scope an engagement — and, when the leak is fixed, the forward data you should be building next.
The Forward-Data Process
Design and build the uncrowded, decision-grade dataset you should be building for the decisions ahead — signal found, validated, and frozen accountably.
Or just email us — tell us the data-heavy step that's holding you back and we'll tell you whether we can help.