Alphagrid V1 is here!
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Digital Twins for
Precision Oncology
Backed by
From fragmented diagnostics to dynamic, patient-specific cancer care. AlphaGrid creates digital replicas of each patient’s cancer journey - integrating multimodal data into real-time insights
Trusted by top clinicians and early adopters across India
The features
Building for Precision Oncology
Your Multimodal Data, Unified
AlphaGrid integrates imaging (MRI, CT, PET, X-ray), histopathology, genomics, and lab reports into a single pipeline. Every scan and test is decoded with patient-specific context and longitudinal history
Clarity, Foresight, Continuity in Cancer Care
AlphaGrid transforms raw oncology data into structured digital twins that evolve with every input. What once took hours or days now takes minutes - giving oncologists continuous clarity on tumor progression, foresight into treatment response, and confidence in every decision.
Transforming Oncology Care with AlphaGrid
From faster reporting to longitudinal digital twins, AlphaGrid bridges the gap between fragmented diagnostics and true precision oncology.
The Vision Ahead with Digital Twins
Static Scans to Predictive Tumor Models
Our digital twin engine predicts tumor behavior, supports early intervention, and tailors care paths - turning vast data into precision. This isn’t just diagnosis - it’s FORESIGHT
The problem
Diagnosis Is Just the Start. Precision Saves Lives
Every diagnosis begins with a scan - but workflows today are slow, disconnected, and risk-prone
No visibility on tumor progression
Why do scans take weeks – or months – to compare?
How can I reduce errors caused by missed tumor context?
No prioritization for high-risk cancers
Why is there no standardisation in oncology reporting?
How can I ensure follow-up cancer scans are always timely?
No benchmarking across tumor cases
What’s the fastest way to detect tumor changes early?
Why can’t multimodal cancer data talk across systems?
Missed tumor abnormalities
Where do we lose time in cancer care pathways?
Lack of real-time alerts on tumor growth
How can we make oncology reports more actionable for treatment?
No feedback loop on patient response
Siloed imaging and genomic data
