Built by Clinicians. Grounded in Data.
AGRI — Algos Graded Response Index. Named for the Greek word for pain, our index is designed to respond to neural signal, not patient recall.
The Problem Preceded the Device
Anesthesiologists and pain specialists have long made critical dosing decisions based on a patient pointing to a number. Our founders — clinicians and EEG researchers — experienced the downstream consequences: over-sedation, undertreated pain, inconsistent outcomes.
AGRI was founded with a single clinical mandate: produce a continuous, standardized pain index derived from EEG signal, not patient recall, not nurse observation, not a 0-to-10 scale.
Pain data should be as objective as vital signs
Heart rate, blood oxygen, intracranial pressure — all measured continuously and objectively. Pain remained the exception. AGRI's mission is to end that exception with EEG-derived, standardized scoring.
Who We Are
The people behind the index:
Elaheh Abdollahisis BSN
Founder and Clinical Lead
Elaheh is an ICU nurse with five years of bedside experience caring for critically ill patients across diverse patient populations. Working directly at the point of care, she observed the persistent gap between what patients feel and what clinicians can objectively document — a gap that became the clinical foundation for the AGRI Index. At AGRI, Elaheh shapes the clinical requirements and validation strategy for the AGRI Index.
Mehdi Khantan, Ph.D.
Co-founder & Chief Technology Officer
Mehdi is an electrical engineer and data scientist with 15 years of experience in electronics and circuit design and six years specializing in neurotechnology, brain-computer interfaces, and EEG signal processing. His published work spans assistive neurotechnology, spinal cord stimulation, and closed-loop neural systems. At AGRI, Mehdi leads the development of the EEG acquisition hardware and the recurrent indexing algorithm behind the AGRI score.
Three disciplines. One organization.
Clinical Medicine
EEG Signal Processing
FDA Regulatory Affairs
Clinical expertise defining the real-world requirements and target patient populations for the AGRI index.
Neurotechnology and biomedical engineering expertise driving the design of the recurrent indexing algorithm and EEG acquisition system.
Regulatory strategy informing AGRI's device classification pathway and future clinical study protocols.
Clinical partnerships and investor inquiries are reviewed directly by our leadership team.