Cannabis AI
Our client, a prominent player in the cannabis e-commerce space in California, faced significant challenges in managing compliance-related queries. The cannabis industry faces complex regulations, necessitating frequent consultations with their regulatory lawyer and in-house compliance employee. To streamline this process and increase operational efficiency, the client sought an AI solution.
Technology:
Langchain, OpenAI GPT, Pinecone Database
Service:
Artificial Intelligence
Challenge
The primary goal was to develop an AI chatbot that could assist the compliance employee by quickly providing accurate information on cannabis regulations. This would reduce the employee’s workload and reliance on the regulatory lawyer, thereby increasing productivity and operational efficiency.
Productivity and Operational Efficiency
Having to read documents of 5000+ pages was hampering the productivity and operational efficiency of the compliance lawyer.
Data Accuracy
The reliance on the compliance lawyer not just hindered their productivity but also had higher chances of compromising data accuracy.
Solution
We developed a sophisticated compliance chatbot, trained on extensive cannabis compliance information specific to California. The training data included:
- Government guidelines
- Regulatory documents
- Public blogs
- Informative content
The chatbot was designed to give accurate responses to compliance-related queries, with an accuracy rate of 80-85%.
We built the chatbot using JavaScript for the frontend interface, ensuring a user-friendly experience for the compliance team. On the backend, Python scripts were implemented to integrate with GPT-4, which provided the natural language understanding necessary for accurately processing and responding to compliance queries. Additionally, we utilized the Pinecone vector database to manage the vast amounts of compliance data, enabling efficient and scalable storage and retrieval.
To ensure the chatbot remained up-to-date and accurate, we empowered the compliance employee to continually train the chatbot based on new information and real-world interactions. A feedback loop was implemented, allowing the employee to validate and correct the chatbot’s responses, which led to ongoing improvements in both accuracy and relevance. This approach ensured that the chatbot could adapt to the evolving regulatory landscape and provide reliable assistance over time.
