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NLU-Powered AI for Faster Clinical Recruitment
Natural Language Understanding (NLU), intent recognition models, and entity extraction in NLP enable intelligent, context-aware chatbots that streamline recruitment for clinical trials. This automation removes bottlenecks, improves communication, and ensures smoother experiences for both staff and patients.
Context-Aware AI Chatbot with Multi-Turn Conversation Handling
With dialogue management systems and vector embeddings for AI chatbot memory, chatbots can maintain context over multi-turn conversations. Whether answering patient questions, scheduling appointments, or sending study updates, chatbot API integration ensures consistent engagement across web, mobile, and SMS channels.
AI Automation for Efficiency and Better Care
Automated patient communication allows healthcare teams to focus on critical tasks. AI assistant integration handles appointment scheduling and follow-ups, ensuring rapid response times and higher engagement. The result is reduced recruitment timelines, improved patient satisfaction, and more efficient resource allocation.
Typography
Onest
Aa Bb Cc Dd Ee Ff Gg Hh Ii Jj Kk Ll Mm Nn Oo Pp Qq Rr Ss Tt Uu Vv Ww Xx Yy Zz
Color Palette
#5405BD
#0062FF
#1C1C1C
#FFFFFF
A Typographic System Built for Clarity and Authority
Using the Onest typeface, we established a clear hierarchy that guides users through complex content with authority and excellent readability.
AWS Lex Conversational AI at the Core
Powered by AWS Lex and Amazon Bedrock, the AI chatbot engine runs on a .NET Core Web API with a SQL Server database for reliability. A React JS dashboard delivers real-time performance insights, ensuring scalable, secure, and intelligent recruitment automation for clinical trials.
NET FRAMEWORK
REACT NATIVE
MYSQL DATABASE
AMAZON BEDROCK
AMAZON LEX
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