Aidence vs SigTuple

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Aidence employs 1-50 people, while SigTuple has 100-500 employees.

SigTuple has raised more in total funding at $20M, compared to Aidence's $20M — a gap of N/A.

SigTuple holds the higher Awaira Score at 60/100, compared to Aidence's 45/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 15-point gap that reflects meaningful differences in scale or traction.

Aidence was founded by Jeroen Vendrig in 2016. SigTuple was founded by Rohit Kumar Pandey in 2015. Visit each company's profile on Awaira for a full founder biography.

Aidence: Aidence develops AI radiology software for lung cancer detection, building CE-marked and FDA-cleared deep learning algorithms that analyse CT scan images to identify and characterise pulmonary nodules requiring clinical follow-up. The Amsterdam company tools assist radiologists in national lung cancer screening programmes and in routine clinical CT reading, providing AI-generated nodule measurements, growth tracking, and malignancy risk scores that reduce reader variability and improve early detection rates.\n\nThe company raised approximately $20 million in venture funding and has deployed its Veye Chest algorithm across multiple European radiology networks and hospital systems participating in national lung cancer screening initiatives. Aidence received CE marking for its software as a medical device and has published clinical validation studies demonstrating performance that is non-inferior to specialist radiologist reads on lung nodule detection tasks.\n\nAidence competes in the AI radiology market alongside Annalise AI, Enlitic, Behold.ai, and Lunit, all of which target chest X-ray and CT reading assistance. The lung cancer screening market has expanded significantly as multiple European countries and the United States have implemented or planned national screening programmes for high-risk smokers, creating a growing workflow automation opportunity for AI lung nodule detection tools. The company was acquired by Coreline Soft, a South Korean medical AI company, as part of a broader consolidation in the AI radiology market. SigTuple: SigTuple develops AI-powered automated pathology solutions that digitize and analyze microscopy slides for blood, urine, sputum, and semen samples, enabling high-throughput diagnostic testing without the constant presence of trained pathologists. The company's AI-Minth platform uses robotic sample preparation, high-resolution imaging, and deep learning analysis to deliver diagnostic-grade results.\n\nThe company raised approximately $20M in Series B funding and has deployed its automated microscopy systems at diagnostic labs, hospitals, and blood banks across India, processing millions of diagnostic samples. SigTuple has received regulatory approvals for its diagnostic AI products and has established partnerships with major diagnostic laboratory chains.\n\nIndia faces a severe shortage of qualified pathologists relative to population, creating an acute need for AI-assisted diagnostic automation. SigTuple's approach of combining hardware, robotics, and AI in an integrated system addresses the complete diagnostic workflow rather than just the software layer, creating higher deployment barriers but also higher switching costs and stronger network effects from accumulated diagnostic data.

SigTuple was founded first in 2015, giving it 1 year of additional market experience. Aidence was founded later in 2016. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Aidence has approximately 1-50 employees, while SigTuple has approximately 100-500. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Aidence and SigTuple 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.