Allumbra
Allumbra offers a new kind of liquid biopsy that detects cancer sooner by reading molecular signals in blood and saliva using a proprietary quantum sensing instrument combined with AI. The platform applies Raman spectroscopy and machine learning to identify cancer biomarkers without tissue biopsies, with peer-reviewed validation for head and neck cancer and early-stage extracellular vesicle detection.
- Healthcare Technology
- Medical Devices
- Biotechnology
- Artificial Intelligence
- Hardware
Funding
Raised to date
$120KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Cancer often goes undetected until a tumor is visible through conventional imaging, delaying treatment and reducing survival odds. Traditional diagnostic methods can be invasive, costly, and insufficiently sensitive for early-stage screening.
Solution
Allumbra provides a new kind of liquid biopsy that detects cancer sooner by reading molecular signals in blood that have always been present but previously unreadable. The company combines a proprietary quantum sensing instrument with AI to extract rich molecular information from blood and saliva samples. Raman spectroscopy signals are analyzed through machine learning models trained to identify cancer biomarkers with high accuracy. The approach is validated by peer-reviewed research, including studies demonstrating high diagnostic accuracy for head and neck cancer and early-stage liquid biopsy detection using extracellular vesicles.
Target Audience
Allumbra serves clinical diagnostics companies, cancer screening programs, and research institutions seeking non-invasive, high-accuracy blood-based cancer detection tools for early diagnosis and monitoring.
Features
- Quantum sensing technology paired with AI for high-sensitivity molecular analysis of blood and saliva
- Raman spectroscopy-based detection of cancer biomarkers without the need for tissue biopsies
- Machine learning models designed for diagnostic and screening applications, including self-supervised learning approaches for generalized cancer screening
- Validated through peer-reviewed publications, including a 2022 Scientific Reports study on head and neck cancer diagnostics and a 2024 AACR study on extracellular vesicle analysis
- Label-free Raman spectroscopy capability for extracellular vesicle analysis, enabling early-stage liquid biopsy detection