AI Driven
Drug Research &
Manufacturing
AI-Powered Research and Development Optimization for a Pharma Manufacturing Company in Jordan
TECH STACK
CLIENT
Drug Research
SERVICE PROVIDED
Application Design, Application Development
YEAR
2025
AI-Powered Research and Development Optimization for a Pharma Manufacturing Company in Jordan
Problem Statement:
- Security concerns arise from heavy reliance on external AI models.
- Inefficiency in R&D stems from unstructured knowledge retrieval, slowing research analysis.
- Integration challenges make it difficult to incorporate AI insights into research workflows.
- High costs of proprietary AI models and cloud services increase financial burdens.
- Data control issues arise when proprietary research data is exposed to third parties.
Technical Methodology:
- Knowledge retrieval optimization: Implemented RAG with vector search to improve retrieval from pharmaceutical research databases.
- AI-powered search: Indexed research documents into a vector database for faster, contextually relevant searches.
- AI-driven insights: Integrated Llama 3.1B on AWS Bedrock to generate research insights and literature analysis.
- Customization & security: Enabled fine-tuning and prompt engineering to align AI responses with pharmaceutical requirements.
- Seamless integration: Ensured API connectivity with existing R&D workflows and knowledge management systems.
- Scalability & performance: Deployed containerized AI workloads on AWS for optimal efficiency.
Our Solutions
Retrieval-Augmented Generation (RAG)
Implemented a Retrieval-Augmented Generation (RAG) solution with vector databases for efficient knowledge retrieval.
high-performance AI operations
Deployed Llama 3.1B on AWS Bedrock to enable high-performance AI operations within AWS infrastructure.
Accelerated research analysis
Integrated AI-powered document search and summarization to accelerate research analysis.
Leveraged open-source models
To maintain full control over proprietary research data and address security risks.
eliminating external dependencies
Designed an architecture where all AI and data processing remain within AWS, eliminating external dependencies.
cost-effective AI framework
Provided a scalable and cost-effective AI framework for ongoing R&D enhancement.
AI-Powered Research and Development Optimization
Development Process
01
Discover
User Research /
In-Depth interviews
02
Design
Product Hypothesis /
User Stories
03
Develop
Style Guide /
Hi-Fi Wireframe
04
Deliver
User Interface /
Adaptive Design
Conclusion
The AI-Assisted Creative Content Production Platform redefines pre-production by combining AI-driven scriptwriting, storyboarding, and VFX generation. By automating time-intensive tasks, reducing costs, and enhancing collaboration, it empowers creative teams to bring their ideas to life faster and more efficiently.
Results
Improvement in knowledge retrieval accuracy through RAG and vector search implementation.
Full control over proprietary data with no external AI provider exposure, ensuring full ownership.
faster research analysis with AI-driven summarization, reducing manual review time.
stronger data security by keeping all research and AI processing within AWS infrastructure.
cost savings achieved by eliminating third-party AI model licensing and subscription fees.
