Enterprise Chatbot for the Company Y
Portfolio Best Practices
This is a simplified example project. When creating your own portfolio:
- Include detailed technical challenges and how you solved them
- Add specific metrics and KPIs that demonstrate impact
- Show code snippets of interesting implementations
- Include architecture diagrams and system designs
- Document your decision-making process
- Highlight your specific contributions to the project
- Add visuals of the final product (if possible)
Case Study Summary
Client: Dev X
Website: devx.com
Industry: Software Development
Impact Metrics:
- 90% reduction in customer service overhead (projected)
- 100% accuracy on initial evaluation datasets
- < 3 second response time for customer inquiries
- Successfully transitioned 12 CSRs to account management roles
- $240,000 annual cost savings in customer support operations
Company Y an AI project featuring a private ChatGPT-like tool, streamlining mobility data analysis and advancing digital innovation in public sector policy evaluation.
Challenge
The regional data team at Company Y faced the challenge of analyzing complex mobility data, including cars, bridges, traffic, and cyclists. Tasked with assessing policy compliance and the impact of changes, they struggled with data scattered across multiple systems, such as the Dexter portal's structured SQL data and various policy documents. This dispersion made analysis laborious, prompting the Province to explore how digitization and AI could streamline the process and foster innovation.
Our Approach
To tackle this challenge, we developed a custom-built AI solution similar to a "private version of ChatGPT." This tool was designed to access and analyze large volumes of PDF documents and structured data exported from the Dexter database. By enabling a ChatGPT-like interaction, users could query this diverse data pool in a conversational manner, leveraging the AI to gain company-specific insights.
Results & Impact
- Successfully integrated structured SQL data and unstructured PDF documents
- Featured in major company meetings
- Enabled conversational querying of complex mobility data
- Streamlined policy compliance assessment
- Enhanced decision-making through comprehensive data analysis
Solution Overview
Baseline OpenAI end-to-end chat reference architecture
Tech Stack
- OpenAI
- Pinecone vector database
- Microsoft Azure cloud infrastructure
- Python backend services
- FastAPI for RESTful endpoints
- Docker containerization
- GitHub Actions for CI/CD pipeline
Additional Context
- Timeline: 3 months
- Team Size: 2 people
- Role: AI Engineer
- Expertise in custom chatbot development
- Specialization in retrieval-augmented generation
- Focus on OpenAI model integration
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