Cubescale Research Labs
Applied research in machine learning for health and energy: retinal and digital biomarkers, embedding search, and energy-system simulation.
Cubescale
Research to product,
at scale.
Cubescale is an applied-AI company. Our research lab turns hard data — medical imaging, biosignals, energy systems — into new methods, and our technology arm turns those methods into products people use.
Two companies, one pipeline
From the lab to the people who use it.
Cubescale Technologies
Builds and runs the products: the cloud platform, data pipelines and applications that take research from prototype to production.
Portfolio
What we've built.
Products and platforms across health AI, quantum readiness, data infrastructure and energy.
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Quantum AI · Incubation prototype
CubeQpath
Tests whether an AI pipeline can gain from quantum hardware, which platform fits and what a pilot would cost.
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Health AI live
ScaleiQ Image Bank
Contributor-owned medical-imaging bank with an audit-trial pipeline that scores scans for machine-learning readiness.
imagebank.scaleiqai.com ↗ -
Health AI live
Institution Portal
A dedicated portal for partner institutions to see and manage their Image Bank contributions.
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Research prototype
BioSignal
Turns paired fundus and OCT retinal scans into a queryable vector database of interpretable biomarkers.
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Data infrastructure beta
VectorSight
Analytics over embedding indexes: index-level insight plus an acquisition cost model.
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Data infrastructure prototype
VisionStream
Audit-trail image pipeline: ingest datasets from cloud storage and make them searchable by similarity.
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Energy beta
EnergyHub
A community hub for energy: share ML models, datasets and images, and discuss the future of energy.
energyhubco.com ↗ -
Energy prototype
Solar Distribution Lab
Bifacial solar PV and battery dispatch simulation for comparing distribution options across sites.
Cubescale Research Labs
Where we're working.
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1
Retinal & digital biomarkers
Non-invasive signals tracked over time, correlated with outcomes to surface what conventional methods miss.
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2
Embedding search at scale
Vector indexes over imaging and records, and the cost models that decide what data is worth acquiring.
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3
Energy systems
Simulation and ML for generation, storage and distribution, from solar dispatch to open energy datasets.
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4
Hybrid Quantum Research Algorithms
Hybrid classical–quantum methods for optimization, machine-learning kernels and simulation, benchmarked against strong classical baselines.
Library
Papers and notes.
White papers, technical notes and preprints from our research labs. Papers are shared on request: ask for a copy and we'll email it to you. info@cubescale.com
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2026-10
Request a copy
White paper
Infonomics and Data Monetization
Treating information as a balance-sheet asset: how to value data, why store-everything-analyze-later erodes ROI, and what generative AI changes for data monetization.
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2026-10
Request a copy
White paper
A Validation Framework for Quantum-Readiness of AI Pipelines
A five-stage method for deciding whether an AI pipeline belongs on quantum hardware, on which platform and at what cost.
Team
The people behind it.
Leadership
Contact
Work with Cubescale.
Partnerships, research collaborations, investment or careers. Send us a note and we'll get back to you.