KALA BIO Building On-Premises AI

Biopharmaceutical company KALA BIO announced a strategic initiative to build an on-premises AI infrastructure platform for the biotech industry. The platform, named "Researgency," is designed to be deployed directly within client environments to help life sciences companies leverage their proprietary data.

This move into AI marks a significant pivot for KALA BIO, a clinical-stage company previously focused on developing therapies for rare eye diseases. In July 2022, the company sold its commercial ophthalmic products, EYSUVIS and INVELTYS, to Alcon Inc. to concentrate on its biologics pipeline. The company's lead candidate was KPI-012, based on a mesenchymal stem cell secretome (MSC-S) platform for treating persistent corneal epithelial defects. However, the AI initiative follows a workforce reduction and a stock price that has fallen nearly 95% over the past year. This strategic shift comes with a recent change in leadership. In February 2026, board member Avi Minkowitz was appointed as the new CEO and CFO, replacing David Lazar who remains on the board. CEO Avi Minkowitz has stated they are building the "opposite" of centralized AI platforms, emphasizing that a client's proprietary data will never leave their own servers. The "Researgency" platform is being developed via an exclusive licensing agreement with a company operating as Younet AI. KALA BIO will first deploy the platform internally, using it on its own biological datasets before licensing it to other biotech and pharmaceutical companies under a recurring subscription model. On-premises AI is gaining traction in the biotech sector as it allows companies to maintain strict control over sensitive and proprietary information, such as clinical trial data and genomic sequences. This approach avoids sending intellectual property to third-party clouds, addressing major security and compliance concerns. The platform will include purpose-built AI agents for biomedical research, capable of functions like analyzing protein interactions and modeling clinical trial outcomes. This aligns with a broader industry trend of using specialized AI to accelerate drug discovery and streamline research, which can take years off development timelines.

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