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We've worked with 130 life science companies, preclinical through phase 3, to deploy AI securely inside the systems they already work in.
Pharma runs on decades of closed software like Benchling, Veeva, Medidata, and SAS. None of it has an AI layer or speaks MCP, so agents cannot reach the systems where the work lives.
Enjamb is that layer. Our agents run on top of existing systems and data, with no migration, and they reach the lab systems and internal tools no other AI touches. They learn how a team works, then do the work in Slack, in Teams, and in the systems themselves, across preclinical research, clinical evidence, trial design, statistical programming, and regulatory submissions.
This is AI tailored for how medicine gets made.
Previously Enjamb Labs Inc, Enjamb
ML and biology researcher. At 17, I published a first-author cancer ML paper in JMIR that beat SOTA models by 20%. Researched drug-therapeutics for pesticide poisoning (30% more effective) and presented at top bio conferences (SOT, ISCB).
Maadhav is an AI researcher. He was the youngest engineer at Dell's AI Lab, shipping AI CPU optimization on supercomputing infra,. He also worked at Broadcom R&D developing SOTA AI ASICs. For fun, he built a Rocket League AI trained on 1M+ sessions that beats the top 1% of players.
Founded 2025 · Source
Source-based assessment · automated evidence rules
The retained text does not establish product availability or customer use.
Maturity is an evidence-based assessment, not a success rating. It may understate the current stage where evidence is limited. Coverage records documented areas, not a complete or independently audited company history.
Our autonomous biodefense capabilities ensure validated point-of-care diagnostics for emerging biological threats are developed in days, not 12+ months.
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