AI-Powered Compliance Chatbot
Development of a secure, internal chatbot prototype using generative AI to provide regulatory and compliance guidance based on internal documentation.
Pharmaceutical Client
2023
Client Description
A globally operating pharmaceutical company with strict regulatory requirements and complex internal processes. The client has a strong digital transformation agenda and aims to leverage AI to improve internal knowledge access and operational efficiency.
Problem
Inefficient Access to Critical Compliance Information
Employees working in compliance and regulatory affairs struggled to access relevant information quickly, often navigating hundreds of internal documents manually. The lack of a centralized, searchable knowledge interface led to inefficiencies, information silos, and an increased risk of non-compliance in highly regulated environments. The client wanted to explore the potential of a GenAI-powered assistant to solve this.
Solution
Design and Implementation of an Internal RAG-Based Chatbot
A Retrieval-Augmented Generation (RAG) chatbot prototype was designed to provide precise, document-grounded answers to regulatory questions. The architecture was defined based on Azure OpenAI services, embedding search, and secure access layers. The implementation followed a structured delivery model:
A solution architecture was developed to meet security, scalability, and usability requirements.
Terraform scripts were written to provision the required Azure infrastructure securely and reproducibly.
A GitLab CI/CD pipeline ensured smooth deployment.
The chatbot frontend was built with React, while the backend leveraged Python for orchestration and API handling.
The chatbot allowed internal users to ask natural language questions and receive cited answers with links to original documents — significantly reducing time spent searching for compliance information.
Technologies & Frameworks
Enterprise-Ready AI Chatbot with Azure OpenAI, React, and Infrastructure-as-Code
Azure OpenAI – provided the LLM backbone for generating context-aware answers using RAG
LangChain – orchestrated the retrieval and prompt flow within the RAG pipeline
FAISS / Azure Search – used for embedding-based document retrieval
React (Frontend) – built the user interface for natural language queries and document previews
Python (Backend) – handled query processing, retrieval logic, and API integration
Terraform – deployed cloud infrastructure including access control, storage, and compute resources
GitLab CI/CD – enabled automated infrastructure and application deployment pipelines
Azure Blob Storage – stored and served compliance documents securely for retrieval
More Works
AI-Powered Compliance Chatbot
Development of a secure, internal chatbot prototype using generative AI to provide regulatory and compliance guidance based on internal documentation.
Pharmaceutical Client
2023
Client Description
A globally operating pharmaceutical company with strict regulatory requirements and complex internal processes. The client has a strong digital transformation agenda and aims to leverage AI to improve internal knowledge access and operational efficiency.
Problem
Inefficient Access to Critical Compliance Information
Employees working in compliance and regulatory affairs struggled to access relevant information quickly, often navigating hundreds of internal documents manually. The lack of a centralized, searchable knowledge interface led to inefficiencies, information silos, and an increased risk of non-compliance in highly regulated environments. The client wanted to explore the potential of a GenAI-powered assistant to solve this.
Solution
Design and Implementation of an Internal RAG-Based Chatbot
A Retrieval-Augmented Generation (RAG) chatbot prototype was designed to provide precise, document-grounded answers to regulatory questions. The architecture was defined based on Azure OpenAI services, embedding search, and secure access layers. The implementation followed a structured delivery model:
A solution architecture was developed to meet security, scalability, and usability requirements.
Terraform scripts were written to provision the required Azure infrastructure securely and reproducibly.
A GitLab CI/CD pipeline ensured smooth deployment.
The chatbot frontend was built with React, while the backend leveraged Python for orchestration and API handling.
The chatbot allowed internal users to ask natural language questions and receive cited answers with links to original documents — significantly reducing time spent searching for compliance information.
Technologies & Frameworks
Enterprise-Ready AI Chatbot with Azure OpenAI, React, and Infrastructure-as-Code
Azure OpenAI – provided the LLM backbone for generating context-aware answers using RAG
LangChain – orchestrated the retrieval and prompt flow within the RAG pipeline
FAISS / Azure Search – used for embedding-based document retrieval
React (Frontend) – built the user interface for natural language queries and document previews
Python (Backend) – handled query processing, retrieval logic, and API integration
Terraform – deployed cloud infrastructure including access control, storage, and compute resources
GitLab CI/CD – enabled automated infrastructure and application deployment pipelines
Azure Blob Storage – stored and served compliance documents securely for retrieval
More Works
AI-Powered Compliance Chatbot
Development of a secure, internal chatbot prototype using generative AI to provide regulatory and compliance guidance based on internal documentation.
Pharmaceutical Client
2023
Client Description
A globally operating pharmaceutical company with strict regulatory requirements and complex internal processes. The client has a strong digital transformation agenda and aims to leverage AI to improve internal knowledge access and operational efficiency.
Problem
Inefficient Access to Critical Compliance Information
Employees working in compliance and regulatory affairs struggled to access relevant information quickly, often navigating hundreds of internal documents manually. The lack of a centralized, searchable knowledge interface led to inefficiencies, information silos, and an increased risk of non-compliance in highly regulated environments. The client wanted to explore the potential of a GenAI-powered assistant to solve this.
Solution
Design and Implementation of an Internal RAG-Based Chatbot
A Retrieval-Augmented Generation (RAG) chatbot prototype was designed to provide precise, document-grounded answers to regulatory questions. The architecture was defined based on Azure OpenAI services, embedding search, and secure access layers. The implementation followed a structured delivery model:
A solution architecture was developed to meet security, scalability, and usability requirements.
Terraform scripts were written to provision the required Azure infrastructure securely and reproducibly.
A GitLab CI/CD pipeline ensured smooth deployment.
The chatbot frontend was built with React, while the backend leveraged Python for orchestration and API handling.
The chatbot allowed internal users to ask natural language questions and receive cited answers with links to original documents — significantly reducing time spent searching for compliance information.
Technologies & Frameworks
Enterprise-Ready AI Chatbot with Azure OpenAI, React, and Infrastructure-as-Code
Azure OpenAI – provided the LLM backbone for generating context-aware answers using RAG
LangChain – orchestrated the retrieval and prompt flow within the RAG pipeline
FAISS / Azure Search – used for embedding-based document retrieval
React (Frontend) – built the user interface for natural language queries and document previews
Python (Backend) – handled query processing, retrieval logic, and API integration
Terraform – deployed cloud infrastructure including access control, storage, and compute resources
GitLab CI/CD – enabled automated infrastructure and application deployment pipelines
Azure Blob Storage – stored and served compliance documents securely for retrieval
More Works