Quris AI

Quris-AI is a pioneering Bio-AI company tackling one of the most impactful challenges in modern medicine: predicting which drug candidates will safely work in humans. With 89% of drugs ultimately failing in clinical trials, Quris addresses a trillion-dollar problem that has remained unsolved by other AI-pharma companies. The platform serves the pharmaceutical industry by combining cutting-edge machine learning with miniaturized biological testing systems to fundamentally transform how drugs are developed and evaluated for human safety.

Headquartered in Boston and Tel-Aviv, Quris-AI brings together a multidisciplinary team of pioneers in machine learning, big data, genomics, technology, and medical device development. The company is backed by notable advisors and board members including Moderna co-founder Prof. Robert Langer, Nobel Laureate Prof. Aaron Ciechanover, former Pfizer CEO Dr. Henry McKinnell, and former Biogen CEO Michel Vounatsos. Quris has raised $28 million in seed funding from top institutional investors and has expanded its collaboration with Merck KGaA, Darmstadt, Germany.

Core Platform and Technology

• Pharma's first AI safety prediction platform — Quris has developed the first platform specifically designed to predict drug safety outcomes in humans, addressing a critical gap that other unicorn AI-pharma companies have not resolved.
• Patients-on-a-Chip — The platform uses miniaturized "Patients-on-a-Chip" devices to test drug candidates in a biologically relevant environment, generating rich experimental data that directly informs the machine-learning models.
• Novel hybrid machine-learning approach — Quris's unique methodology generates automatically-tagged data from Patients-on-a-Chip testing, which is then used to train classification algorithms, creating a tightly integrated loop between biological experimentation and AI model development.
• Clinical prediction capabilities — The Bio-AI platform is designed to make clinical-stage predictions about drug safety, helping pharmaceutical companies identify which candidates are most likely to succeed before costly human trials.

Key Capabilities and Approach

• Integration of machine learning, big data, and genomics to power drug safety predictions at scale.
• Reduction of reliance on animal testing by substituting miniaturized chip-based patient models for preclinical evaluation.
• Automated data tagging and model training pipeline that continuously improves predictive accuracy.
• A hybrid Bio-AI approach that bridges the gap between in vitro biological data and real-world human clinical outcomes.

Team, Leadership, and Advisory

• Founded and led by Isaac Bentwich, MD (Founder & CEO) and Yossi Haran (Co-Founder & CTO), with deep expertise across AI, biology, and technology.
• Scientific and business advisory boards include luminaries such as Nobel Laureate Prof. Aaron Ciechanover, Moderna co-founder Prof. Robert Langer, former Pfizer CEO Dr. Henry McKinnell, and former Biogen CEO Michel Vounatsos.
• A diverse, cross-disciplinary team spanning AI and data science, biology and R&D, software and systems engineering, business development, and operations — based across Boston and Tel-Aviv.
• Leadership includes a VP of AI, SVP of Biology, VP of Business Development & US GM, and a Head of Alliance Management, reflecting the company's integrated scientific and commercial focus.

Recognition, Partnerships, and ESG

• Featured in major industry and media outlets covering the intersection of AI and biopharma innovation.
• Recognized as a key player in the 2023 biopharma inflection point for AI-driven drug development.
• Expanded collaboration with Merck KGaA, Darmstadt, Germany, underscoring growing industry validation of the platform.
• Committed to strong ESG standards, employee well-being, and making a positive societal impact alongside its scientific mission.

Quris-AI represents a new generation of Bio-AI innovation, uniquely positioned at the intersection of biological engineering and artificial intelligence to make drug development safer, faster, and more predictable — ultimately benefiting patients worldwide.