Embracing Emerging Technology

Our Core Bio-Sensing Technology Consists of Three Major Components:

RAMAN
SPECTROMETER:

Using raman technology to digitize bodily fluid.

SMART
SOFTWARE:

Molecular fingerprinting to quantify results.

CALIBRATED
TEST RESULTS:

Proprietary machine learning algorithms.

Our Basic Process

Kaligia Biosciences’s Point of Care Technology

Kaligia Biosciences’s technology is based on non-invasive and minimally invasive point-of-care devices, machine learning software and analytics.

Kaligia Biosciences’s core bio-sensing technology sets us apart from the competition. Lasers and spectrometers provide a fingerprint that can identify various molecules. Unique data acquisition and machine learning algorithms provide rapid results.
Kaligia Biosciences’s KBS measurement devices allow for non-invasive testing of Interstitial Fluid (ISF) that lies in the skin.
The Kaligia Bioflector (KBS-1) – Non-Invasive Glucose Skin Test is in Pre FDA approval data collection for submission/clinical trials.
Kaligia’s RBA measurement devices allow for testing with various bodily fluids.
The Rapid Blood Analyzer (RBA) is in Pre FDA approval data collection for submission/clinical trials.
Kaligia Biosciences’s COVID Rapid Screener (RBA-2) is in the FDA approval process.

Product and Technology Development

Kaligia’s products are in various stages of research and approval.

Rapid Biofluid Analyzer (RBA)

RBA is a measurement device (IN-VITRO) which is minimally invasive for blood application and non-invasive for other bodily fluids such as saliva, urine and sweat.

Products:

Benefit:

  • Portable Lab
  • Sterile – Minimal Cont

Kaligia BioFlector (KBS)

KBS is a device that takes non-invasive measurements (IN-VIVO) of analytes found in Interstitial Fluid (ISF) which lies in the skin.

Product:

Benefits:

  • No pricking fingers
  • No test strips
  • Immediate results
Never Stop Learning

Kaligia Biosciences’ Acquisition Software and Analytics

Kaligia Biosciences employs Machine Learning within our test analysis. The proprietary algorithms continue to learn and sharpen the accuracy of the results.