S
Awaira Score
60
Out of 100
Valuation
N/A
Post-money
Total Raised
$20M
All rounds
Awaira Score
60/100
Founded
2015
100-500 employees
What They Build
March 2026SigTuple 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.…
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StageSeries B
Employees100-500
Country🇮🇳 India
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Funding Rounds
Series B · No public funding round data available yet.
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View all alternatives to SigTuple →Frequently Asked Questions
What is SigTuple's valuation?▾
SigTuple's valuation is not publicly disclosed.
Who invested in SigTuple?▾
Investor information for SigTuple is not publicly available at this time.
When did SigTuple last raise funding?▾
No public funding round data is currently available for SigTuple.
How many employees does SigTuple have?▾
SigTuple has approximately 100-500 employees.
What does SigTuple do?▾
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.