Pharma
Europe
1 Project Manager, 1 Developer, 1 AI Engineer
1.5 months
Our client is a pharmaceutical and biotech giant delivering life-changing medicines globally. They‘ve already adopted AI to redefine medical science and look for better ways to discover, test, and accelerate potential drugs. Their new goal was to speed up the time to market for medicines, better document treatment outcomes, and decrease trial costs.
Deploy AI to enhance knowledge access and accelerate insight generation across the organization by:
Improve collaboration through centralized information.
Increase efficiency via automated analysis.
Derive deeper insights from complex datasets.
The solution must process both structured data formats such as CSV files and SQL databases, as well as unstructured formats including PDFs, handwritten notes, and EHRs. Through API connectors, the system can integrate with internal R&D databases, clinical trial management systems, and even external public or private datasets.
At the core of the solution we suggested an AI-powered knowledge engine built on a large language model (LLM) specifically fine-tuned for biomedical, pharmaceutical, and regulatory domains. This engine enables the contextual summarization of complex documents such as clinical trial reports, lab results, and scientific literature, turning them into concise, deep insights.
The platform includes advanced analytics tools capable of identifying meaningful patterns in patient outcomes, adverse side effects, and overall drug efficacy. Predictive modeling techniques are used to estimate time-to-market for candidate drugs and forecast the likelihood of clinical trial success.
We are still finetuning the algorithm to get better results based on historical data. The pilot tests showed decent results and improved workflow for users.
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