Butterfly Network vs Regard AI

Butterfly Network has a disclosed valuation of $1.5B, while Regard AI's valuation is not publicly available, making a direct size comparison difficult. Butterfly Network employs 500-1000 people.

Butterfly Network has raised more in total funding at $700M, compared to Regard AI's $30M — a gap of $670M.

Butterfly Network holds the higher Awaira Score at 85/100, compared to Regard AI's 55/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 30-point gap that reflects meaningful differences in scale or traction.

Butterfly Network was founded by John Martin in 2011. Regard AI's founder information is not currently available in our database.

Butterfly Network: Butterfly Network produces the Butterfly iQ, a handheld ultrasound device powered by a proprietary semiconductor chip that replaces traditional piezoelectric transducer arrays with a single silicon chip capable of whole-body imaging. The AI software layer provides real-time image interpretation guidance, enabling clinicians with limited ultrasound training to perform diagnostic scans at the bedside.\n\nThe company is publicly traded on the NYSE under the ticker BFLY and raised over 700 million USD prior to and through its public listing. Butterfly has deployed devices across hospitals, emergency rooms, and point-of-care settings in over 50 countries, with particular penetration in resource-limited healthcare settings where traditional ultrasound equipment is prohibitively expensive or unavailable.\n\nPortable AI-assisted diagnostic imaging represents a structural shift in how and where medical imaging is performed. Butterfly Network has created a defensible position through its proprietary chip architecture, which competitors cannot easily replicate, and through its growing library of AI-assisted clinical guidance tools. The transition from hospital-based imaging to point-of-care diagnostics powered by AI guidance is a multi-decade trend that positions Butterfly at the center of a fundamental change in diagnostic medicine. Regard AI: Regard AI builds an AI clinical documentation assistant that integrates with hospital electronic health record systems to automatically identify potential diagnoses, surface relevant clinical evidence from patient records, and generate structured documentation for physician review. The system is designed to reduce the documentation burden on hospitalists and clinical teams while improving diagnostic completeness and coding accuracy.\n\nThe company raised approximately 30 million USD and has deployed across hospital systems focused on reducing physician administrative load, a problem that contributes to clinician burnout and documentation errors with downstream billing and quality implications. Regard operates as a background analysis layer that surfaces findings without requiring clinicians to change their existing EHR workflow.\n\nClinical documentation AI sits at the intersection of physician workflow optimization and revenue cycle management, giving it a dual value proposition that justifies enterprise procurement from both clinical operations and hospital finance departments. Regard competes with Nuance DAX, Abridge, and other ambient documentation tools, but its focus on diagnostic suggestion and coding completeness rather than pure transcription differentiates its clinical and financial value story.

Butterfly Network was founded first in 2011, giving it 6 years of additional market experience. Regard AI was founded later in 2017. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Butterfly Network has approximately 500-1000 employees, while Regard AI has approximately 50-200. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Butterfly Network and Regard AI are direct competitors — both operate in the AI Healthcare space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.