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Full stack AI Agents accelerating Biopharma R&D
Enjamb puts agents across the entire drug program, from preclinical research through clinical trials and approval. Our agents synthesize up-to-date clinical evidence from the literature and real-world data to pinpoint the right endpoints and patient populations; draft and audit regulatory documents like INDs, protocols, SAPs, and NDAs; and run the statistical programming behind them, including SDTM, ADaM, TFLs, and QC, all in one auditable workspace that cuts months off the path from preclinical to clinic and clinic to market.
Enjamb is an enterprise AI workspace that supports end-to-end research workflows for biopharma, from literature review to data analysis, methodology design, grant discovery, and drafting. It provides domain-specific AI agents and a collaborative environment to accelerate the full research lifecycle at scale.
Enjamb offers an integrated research environment where domain-specialized AI agents operate within a private workspace to perform literature review, data analysis, methodology design, and grant discovery, producing ready-to-use outputs. Features include: comprehensive literature reviews with structured analyses and built-in reference management; a sandboxed compute environment with pre-installed scientific packages and access to numerous databases; autonomous statistical analysis and advanced bioinformatics pipelines; automated protocol extraction and procurement-ready material specs; intelligent grant discovery with autonomous proposal drafting and submission-ready outputs.
Who it’s for: R&D teams in biopharma and life sciences, including researchers, methodologists, data scientists, and grant-writing staff seeking end-to-end, reproducible research workflows.
Pre-seed funding announced ($650K); mentions of pre-seed and enterprise/academic adoption; hiring/traction inferred from platform features and enterprise focus
Maadhav Deekshitha is an ML researcher working at the intersection of AI and high-performance computing. He was the youngest person to work at the AI Innovation Lab at Dell Technologies, where he developed machine learning models on top of supercomputing infrastructure. He also became one of the youngest contributors in R&D at Broadcom, working on state-of-the-art ASIC systems.
Rayan is an ML and neuroscience researcher who has published first-authored papers and presented at premier conferences, including SOT and the International Society for Computational Biology. His neuroscience work spans five academic labs across UF and FIU, where he focused on developing therapeutics for pesticide poisoning. He also developed DeepImmunoGene, an ML framework for detecting lung cancer immunotherapy biomarkers that outperforms SOTA by 20%.
Enjamb is an agentic platform for biopharma R&D that runs the entire drug program, from synthesizing clinical evidence and designing trials to drafting regulatory documents and statistical programming, cutting months off the path from preclinical to clinic and clinic to market.
Enjamb is an agentic platform that runs entire drug programs from preclinical research through clinical trials using agents to synthesize clinical evidence, draft regulatory documents, and perform statistical programming in a single auditable workspace.
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Agents that accelerate clinical trials.
Agent Platform for Life Sciences
