Elucidata

The promise of using artificial intelligence to discover life-saving drugs is one of the most exciting frontiers of our time. We’ve all seen the headlines promising AI-driven pipelines that can design a molecule in days rather than years. But at Hyperplane, we know a fundamental truth about machine learning: an AI model is only as good as the data you feed it.

In the life sciences, that data is overwhelmingly broken.

Every year, billions of dollars are poured into generating public and proprietary biomedical datasets (genomics, transcriptomics, metabolomics, and proteomics). Yet, up to 80% of a data scientist's time in Big Pharma is spent on the exhausting, manual chore of cleaning, formatting, and harmonizing this data before it can even be analyzed. The data exists in massive silos, locked in variable tabular schemas, missing critical metadata annotations, and completely unoptimized for machine learning workflows.

This is the exact bottleneck that Elucidata was built to solve. And it's exactly why Hyperplane backed them.

The Story: Moving From "More Data" to "AI-Ready Data"

Founded by Abhishek Jha (an MIT and IIT alumnus with deep roots at Agios Pharmaceuticals, where he helped bring four first-in-class therapies to the clinic) and Swetabh Pathak (a brilliant systems and data architect from IIT Delhi), Elucidata didn’t just set out to build another analytics tool. They set out to build the foundational data layer for the modern TechBio era.  

Elucidata created Polly, a Biomedical Data Cloud.  

Instead of forcing companies to repeatedly reinvent the wheel with rigid data pipelines, Polly automatically ingests public and proprietary data, standardizes it, and uses advanced Bio-NLP and machine learning to enrich it with uniform metadata. It transforms messy, raw biological information into clean, harmonized, and "ML-ready" OmixAtlases.  

By turning chaotic datasets into structured data assets aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) guidelines, Elucidata has done something remarkable: they have compressed drug target identification processes from a standard 1–2 years down to just a few months.  

Why Hyperplane Invested

Our investment thesis at Hyperplane centers on founders leveraging data architecture and AI to unlock immense industrial value. Elucidata sits squarely in that sweet spot for several key reasons:

1. Solving the Right Problem: While the venture world rushed to fund the flashiest generative AI drug design startups, Elucidata focused on the infrastructure. They are the "picks and shovels" provider for the entire TechBio revolution. Without AI-ready data, the industry's multi-billion-dollar investments in AI models will stall.
2. Immediate Product-Market Fit with Big Pharma: Elucidata’s impact isn’t hypothetical. Their platform is already trusted by 4 of the top 10 pharmaceutical giants (including titans like Pfizer and Genentech) alongside dozens of premier research labs and biopharma partners.  
3. The Interdisciplinary Founder Advantage: True innovation in this space requires a rare combination of deep biological domain expertise and scalable cloud software architecture. Abhishek and Swetabh embody this bridge perfectly. They speak the language of both laboratory researchers and MLOps engineers.  
4. The Road to Biomedical AGI: Elucidata continues to push boundaries. With their recent expansion into AI Labs to tackle out-of-distribution (OOD) problems and partnerships transforming massive commercial biobanks into AI-grade data assets, they are paving the way for reliable, real-world Biomedical Artificial General Intelligence (AGI).  

Looking Ahead

We invested in Elucidata because they are transforming how the life sciences industry interacts with data. By shifting the paradigm from “how much data do we have?” to “how usable is our data?”, Elucidata is shortening drug discovery cycles and ensuring that the next generation of therapeutics reaches patients faster.

We are incredibly proud to partner with Abhishek, Swetabh, and the entire Elucidata team as they build the data infrastructure that will power the future of global healthcare innovation.