Elucidata has launched the AI Lab, a new initiative aimed at advancing Artificial General Intelligence in biomedical research and development. The lab will initially focus on one of the most difficult challenges for real-world AI: handling out-of-distribution data. This type of data arises when inputs differ significantly from the data AI systems were trained on, a common problem in biomedical research where rare subpopulations, unexpected responses, and unusual signals are critical.
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Abhishek Jha, Co‑founder and CEO of Elucidata, explained, “Traditional AI assumes that production data resembles training data. In biomedical R&D, this assumption fails exactly where it matters. By reliably detecting and explaining out-of-distribution observations, we can build AI that performs reliably in real-world scenarios. AI Lab represents our commitment to addressing this challenge.”
Swetabh Pathak, Co‑founder and CTO, added, “Our scientific data often carries the most important signals outside the normal range. Our goal is to create AI systems that can detect these edge-case signals, clean them, and put them to practical use rather than discarding them.”
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The AI Lab operates from research hubs in San Francisco, Boston, and India. It brings together scientists, machine learning engineers, product leaders, designers, and testers who collaborate directly with customers to define real-world problems. The team combines high-quality data infrastructure with Agentic AI to develop actionable solutions.
Initially, the AI Lab will create out-of-distribution aware AI tools for pharmaceutical, biotechnology, agricultural technology, healthcare, and diagnostic companies. These solutions will be built on Polly, Elucidata’s platform currently used by more than 100 pharmaceutical and diagnostic organizations and supporting over 40 programs at the IND or later stages.
The AI Lab will expand Polly’s capabilities, including Scout and Xtract modules, to connect, extract, and standardize multimodal biomedical data. The lab also plans to develop foundation models, virtual-cell systems, curated knowledge graphs, and workflow-ready tools that automate routine analyses and enable mapping of EHR data with large language models in regulated environments. By assembling a team with expertise in biology, chemistry, drug discovery, software engineering, mathematics, and business, Elucidata aims to turn these technologies into real-world solutions that have measurable impact in biomedical research and development.
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