Our Clients
The pharmaceutical industry comes across significant data availability challenges because of siloed and fragmented sources of data, data privacy concerns, and regulatory constraints. Genomic data, real-word evidence, and clinical trial data are stored often in disparate systems, which interrupts seamless integration.
Data sharing across institutions and borders is often challenging while abiding by regulatory requirements, like GDPR and HIPAA. Moreover, lack of standardized data formats and proprietary data ownership hinder interoperability. These issues prevent making the most of AI and big data, delaying the drug discovery and development process. We are experts in data science and artificial intelligence in pharmaceutical industry, offering advanced data integration technologies, harmonized standards, and collaborative frameworks to mitigate these challenges.
Data in the life sciences industry is highly heterogeneous, originating from diverse sources such as electronic health records, clinical trials, medical imaging, and genomic sequences. It’s challenging to integrate these different data types into a cohesive dataset for analysis by AI.
To enable the integration of diverse datasets, we implement standards such as the Fast Healthcare Interoperability Resources (FHIR). Furthermore, we employ platforms specializing in integrating and harmonizing clinical and multi-omics data for streamlining the process. We leverage AI tools, which can natively handle various data types, such as tools using deep learning for handling multimodal data.
Life sciences data, patient data in particular, often consists of sensitive personal information. Regulations like GDPR in Europe and HIPAA in the U.S. establish strict requirements on how such data can be stored, processed, and shared. This makes it difficult to access large, diverse datasets.
We employ federated learning, which allows the training of AI models across various decentralized data sources without the need to transfer the data to a central server. It facilitates training models on sensitive data, while maintaining the privacy of the data itself. Moreover, we use techniques such as the creation of synthetic datasets or data anonymization to protect individual identities while still supplying valuable data for the training of AI.
We have extensive experience in building custom AI models tailored for the life sciences and pharma industries. In this domain, our expertise ranges over clinical data analysis, proteomics, and genomics, facilitating precise biomarker discovery, personalized medicine, and drug development. Using NLP and machine learning, we provide you with actionable insights to improve patient outcomes and fast track innovation.
Our team is proficient in building custom Large Language Models (LLMs) for the life sciences and pharma industries. Our area of expertise includes regulatory compliance, clinical trial analysis, optimizing for jobs like drug discovery, and model training on specialized biomedical texts. By integrating advanced NLP techniques with domain-specific knowledge, we come up with cutting-edge solutions, which propel innovation and improve decision-making in healthcare.
Over the years, we have achieved expertise in managing DataOps and MLOps for the life sciences and pharma industry. We design solutions to streamline data pipelines, which ensures flawless integration, governance, and transformation of complex biomedical datasets. Deploying scalable MLOps frameworks, we help to automate model development, monitoring, and deployment. This enables rapid iteration and compliance with the industry standards.
Our practical experience in running clinical trials and conducting life sciences research has equipped us with the expertise to generate high-quality data tailored for life sciences and pharma. Our expertise ranges from developing synthetic biomedical datasets, improving real-world data with advanced AI techniques to simulating clinical trials. By ensuring diversity, accuracy, and compliance of data, we empower researchers and companies to fuel innovation in healthcare, improve patient outcomes, and accelerate drug discovery. Furthermore, our data solutions facilitate pharma sales projection, allowing strategic decision-making and more accurate forecasting in a highly competitive market.
The journey from conducting research to creating a market-ready pharma product is long and expensive, which is often because of clinical trial failures. It’s equally arduous to identify drug candidates before trials. Our team has expertise in data science in pharma R&D, leveraging which we can help to automate the screening of millions of compounds, accelerating drug discovery and manufacturing significantly.
Avoiding expensive, suboptimal clinical trials is something all pharma companies intend to do. By leveraging our expertise in data science in clinical trials and pharmacovigilance, we help you attain the right patient mix, predict potential side effects, and analyze historical data. Additionally, our team uses machine learning to better the trial and approval phases, fostering faster and more efficient drug development, while ensuring continuous safety monitoring.
We optimize life sciences R&D operations and the pharma supply chain through cutting-edge AI and data science solutions. Our expertise spans automating data analysis, streamlining drug discovery processes, and enhancing predictive modeling. With our solutions, our clients are empowered to make their R&D endeavors more efficient, innovate faster, and optimize their pharma supply chain to achieve better management of resources and their distribution.
Our proficiency in big data technologies enables us to process huge amounts of data from different sources and create personalized medication plans. When merged with medical records, medical sensor data, and genomic sequencing, these technologies facilitate the creation of precise treatment plans. Analyzing the treatment progress continuously refines these plans further, enabling pharma experts to adjust the dosing as required for improved outcomes.
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