Mindverse Computing is building the agentic quantum digital twin learned from BCI data. We narrowed to that deliberately, from a broader remit.
Where we are today
We are at the research-to-product stage: the Quantum Virtual Mind (QVM) — the quantum–classical core — is built and benchmarked, manuscripts are in preparation, and the applications are moving into development.
Benchmarked in house against a matched classical baseline on public EEG data (PhysioNet EEGMMIDB), with a mixed verdict reported in full.
In preparation: the foundation model, the healthy-variation atlas, and the longitudinal clinical record.
Superlearning, restoration, and clinical research — three applications drawing on one core rather than three separate builds.
Research tools, education, wellbeing, clinical research — sequenced by difficulty, all built on the same state reading.
The QVM is constructed so it cannot produce physically impossible states. A structural property rather than a benchmark — the part of our approach least dependent on results still to come.
Three manuscripts in preparation, and an early benchmark we report in full — the measures we lead on and the ones conventional methods still lead on. Both are in preparation.
Both proposed directions would use the same recordings, preprocessing, model, and agents. An architectural advantage, with the shared machinery already built into the design.
Research tools first, then education, then clinical research — ordered by how long each takes to validate, not by how large each looks. Research groups already hold the recordings and can evaluate a tool in months; clinical work needs years of longitudinal data before anything can be claimed. Entering in that order means revenue and evidence arrive before the expensive validation begins.
BUSINESS MODEL
We do not intend to sell headsets or run a consumer app. The plan is to be the layer between a customer’s recordings and whatever they want to do with them: their data lands on our platform, a state model is fitted to each of their subjects, and they reach it through an API. One core, billed three ways.
Three endpoints, three buyers
Learning platforms and training programmes call it for readiness and pacing. They keep their content and their user relationship; we are the layer behind it.
Meditation, sleep, and stress apps call it to select and pace a session from measured state instead of a preset track. Highest call volume, lowest price per call.
Research groups call it for longitudinal trajectories against a subject’s own baseline. Lowest volume, longest contracts, and the strongest reason to stay.
None of this is generating revenue yet — the model is built, the agent layer is in development, and the applications are proposals. This is the structure we are building toward, not a description of current operations. Pricing and projections are in the investor materials.
Where things stand
The state model is built and has had one early internal test on public data. The agent layer is specified and being built. All three applications are at proposal stage, specified in draft manuscripts and moving into build.
What you would be backing
We would rather tell you which parts exist than imply the whole stack is finished. If you are evaluating this, you are backing a technical approach and a team at the point where the science is proving out.
Consumer and research BCI hardware has improved steadily, and the number of recordings being collected grows every year. What has not kept pace is the ability to say what those recordings mean for a specific person. That gap is where we sit, and it widens as acquisition gets cheaper.
Reliable tool-using AI agents are recent enough that wrapping a scientific model in an agent layer was not a realistic architecture three years ago. The model and the agents are each ordinary now; the combination applied to brain dynamics is not yet crowded.
Regulators increasingly require that a model’s behaviour can be explained and audited. A model built from named components starts from that position.
Materials
Detailed materials — technical roadmap, market sizing, financials, and the current manuscript drafts — are shared with qualified investors on request, under NDA where appropriate. We are happy to walk through the research in depth, including the results that did not go our way.