Frekil recently launched!

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^{"Procure and annotate medical data 10x faster."}

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^{TL;DR Frekil is an end-to-end platform to procure and annotate medical imaging datasets like Xrays, CTs, MRIs, etc 10x faster with AI assistance. They’ve partnered with radiology chains for large-scale imaging data, and have a global marketplace of radiologists for rapid, high-quality annotations.}

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https://youtu.be/2MYjUFp3r0Q?feature=shared

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Founded by Nikhil Tiwari & Shivesh Gupta‍

Hey everyone, meet Nikhil & Shivesh, co-founders of Frekil!  👋

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🤝 The Team

Nikhil (pictured left in the photo above) and Shivesh (pictured right) have been friends since their days at IIT Bombay, where they studied and built together. While working on healthcare AI projects, they experienced firsthand how painful annotation can be especially for large, multi-dimensional medical images that demand extreme accuracy.

Nikhil (CEO) - Former software engineer at Stripe, Amazon, and Marsh McLennan, where he worked on infrastructure, performance optimization, and low-level systems. He graduated from IIT Bombay last year, where he led technical initiatives in the student body SARC and built platforms used by 10,000+ students.

Shivesh (CTO) - Former systems software engineer at Sony Japan. He holds an engineering degree from IIT Bombay. During his time there, he worked on healthcare AI research and also led the institute’s web and coding club.

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❗The Problem

AI is transforming healthcare, but the biggest bottleneck is still access to medical images from hospitals and annotations by expert doctors.

Healthcare AI and life sciences companies spend millions collecting medical images and hiring expert radiologists. But annotations? They're still:

📝 Manual.

🐢 Slow.

🔍 Hard to QA.

Finding expert annotators with deep domain knowledge is tough, which means R&D teams waste months and large budgets on a process that should be seamless.

Medical annotation itself is uniquely challenging because it involves:

📦 Gigabyte-scale files.

🧠 Complex, multi-dimensional data that’s sensitive to loss.

🎯 Accuracy that’s absolutely critical.

Yet most teams still use open-source desktop tools like 3D Slicer, built decades ago for solo researchers. These tools:

🚫 Require local machines.

📉 Offer no real-time collaboration.

📊 Force spreadsheet-based coordination.

🔐 Risk compliance and data security.

And the result?

Highly trained radiologists are stuck doing repetitive tasks manually wasting time, slowing innovation, and compromising data quality.

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🧠 The Solution

Frekil transforms the way healthcare AI teams prepare data by accelerating and streamlining the entire annotation pipeline — cutting timelines from months down to days.

Here’s how:

✅ Deliver fully annotated medical datasets tailored for AI research needs.

✅ Provide certified and benchmarked radiologists for annotation and quality assurance.

✅ Offer advanced, browser-based annotation tools for all kinds of medical images—radiology, pathology, histopathology, including X-ray, CT, MRI, ultrasound, etc.

✅ Use AI assistance to make annotators 10x faster.

✅ Enable customizable clinical workflows with multi-stage reviews & annotations.

✅ Ensure FDA-Ready annotation versioning, consensus checks, and full audit trails.

✅ Track annotator performance in cost, time, and accuracy - all built in.

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Learn More

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^{🌐 Visit www.frekil.com to learn more.}

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^{🤝 Know someone building AI in healthcare whether it’s diagnostic models, clinical trial workflows, or robotic surgery? The founders would love to connect. They have special offers for academic research, connect them with professors at your university working in healthcare AI!}

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^{📧 Got questions or want to chat? Reach out anytime via email here! 🗓️ Or book a call directly here.}

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^{👣 Follow Frekil on LinkedIn & X.}

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