Generative AI & AI Agents
CineAssist
AI-Powered Customer Support
In brief
CineAssist is a Directrix Technologies project for CineGear Pro, London, UK: AI-Powered Customer Support. Summary: A conversational AI assistant that answers routine customer questions in natural language. Outcome: Reduced repetitive manual query handling by automating answers to routine customer questions, and provided a foundation for expanding customer service automation.
Overview
CineAssist is an AI-powered customer support solution designed to help customers obtain answers to common questions through natural-language conversations.
The project focused on integrating a large language model into customer service workflows to reduce repetitive manual query handling and improve the efficiency of customer support operations.
What we built
- A conversational AI assistant for customer enquiries.
- Language-model response generation.
- AI integration with customer-facing support workflows.
- Automated handling of routine questions.
- A foundation for expanding customer service automation.
How we built it
We integrated a large language model into a conversational support workflow, enabling incoming questions to be processed through a language model and responses to be returned through the application.
The implementation combined AI integration with application-level processing to support customer interactions. The solution was developed to complement existing customer support operations rather than replace the entire customer service function.
Services this project shows
Project outcome
Reduced repetitive manual query handling by automating answers to routine customer questions, and provided a foundation for expanding customer service automation.
Interactive sample · runs in your browser
Ask a sample support assistant
Explore one idea from this project with fictional sample content. It is not connected to the project's systems or data.
Chat with a sample support assistant
- Assistant: Hi. I answer routine questions about this sample camera-rental shop: hours, deposits, delivery and extending a rental.
Try asking
- Answers with a source
- Each routine answer names the approved FAQ entry it came from.
- Hands off instead of guessing
- Refunds, damage, orders and unknown questions go to a person. Hand-offs in this chat: 0.
How this sample differs from the project: Answers come from a short fictional FAQ and no language model is called. The project used a large language model; this sample shows the answer, source and hand-off pattern around it. It shows an interaction pattern, not a measured project result.