Inspiration
As AI models become more capable, turning their intelligence into useful, reliable applications remains a challenge. We wanted to explore how LLMs, retrieval-augmented generation (RAG), and AI agents can work together to help users solve complex tasks through a single platform.
What It Does
NexusAI is an AI agent platform designed to support intelligent task solving using large language models, contextual information retrieval, and tool-based workflows. Its goal is to make AI interactions more useful by combining reasoning, relevant knowledge, and task-oriented execution in one interface.
How We Built It
The platform is designed around a modular architecture that connects an LLM-powered interface with retrieval and agent components. The planned workflow includes processing user requests, identifying relevant context, generating responses, and using available tools when needed. The architecture can be extended with scalable model inference and cloud GPU infrastructure.
Challenges We Faced
One of the main challenges was designing a workflow that balances response quality, latency, and reliability. Other considerations included maintaining useful context, structuring agent tasks, handling incomplete information, and making the system easy to extend.
What We Learned
This project helped us explore the practical challenges of building AI applications beyond basic prompting. We focused on modular system design, LLM integration, retrieval-based context, agent workflows, and the importance of evaluating AI outputs for usefulness and reliability.
What's Next
Our next steps include improving agent orchestration, adding more tool integrations, evaluating response quality, and optimizing inference performance. We also aim to explore GPU-accelerated deployment using the Nebius and NVIDIA ecosystem to support more demanding AI workloads.
Built With
- ai-agents
- generative-ai
- gpu
- llms
- python
- rag
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