Python, LangChain, OpenAI API, Pinecone, Docker
Business Services
AI Chatbot / Knowledge Assistant
United States
About The Project
SupportGenie was created for software companies with large, growing knowledge bases. As more documentation, guides, and demo articles were added, it became difficult for support agents to find the right information. Even simple questions often required searching through multiple documents before an answer could be provided.
The aim was to build a system that could quickly search their documentation and provide accurate answers based on approved content. We wanted to help support teams work faster while maintaining answer quality and consistency.
We developed a knowledge assistant that combines Large Language Models and machine learning-powered semantic search to understand questions in plain language, retrieve relevant information from the knowledge base, and generate clear responses with links to the original documentation.
Key Project Deliverables
We delivered solutions that improved access to knowledge, answer accuracy, and support efficiency.
Natural Language Search
Users can ask questions in plain language and receive accurate answers instantly without delays.
Knowledge Base Retrieval
Using machine-learning vector embeddings, the system understands documentation and queries.
Source Citations
Every answer includes links to original documentation for verification, transparency, and further reading.
Automatic Content Updates
New and updated documentation is automatically added to the knowledge base without manual intervention.
Secure API Access
The assistant integrates with existing support tools and applications through secure API connections.
Confidence Controls
When a reliable answer cannot be found, the system asks users to contact a support agent for correct information.
Major Project Challenges
The biggest challenge was organizing information spread across multiple documents and systems. Support teams needed answers quickly, but finding the right content often took time.
Accuracy was another major concern. The assistant needed to provide answers based only on approved documentation and avoid returning incorrect information.
Documentation was updated regularly, so the system needed a way to stay current without requiring manual updates. Performance was also important. The assistant had to return answers quickly, even as the amount of content and number of users continued to grow.
Solutions & Impact
We built a retrieval-based knowledge assistant that combines machine learning-driven semantic retrieval with AI-generated responses. By searching approved documentation before generating answers, the system ensured responses remained accurate and aligned with company information.
To improve performance and usability, we implemented several key features.
- Built a searchable knowledge base.
- Added semantic document search.
- Integrated Pinecone for fast content retrieval.
- Implemented source-based answer generation.
- Added automatic content synchronization.
- Created secure FastAPI endpoints.
- Containerized the application using Docker.
- Added confidence checks and escalation workflows.
The assistant quickly became a daily tool for support teams. Agents spent less time searching for information and more time helping customers with complex issues. Responses became more consistent, and users could easily verify answers through source links.
Turn Your Knowledge Base Into an AI Support Assistant!
Help your team find answers faster, improve support efficiency, and deliver more consistent customer experiences.
Project in Figures
5
Months
1,800
Estimated Man-hours
5
Team Size
Applied Technologies
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