Product Demonstration, Tutorial
Workshop: Agentic Pipelines orchestrating open-source LLMs ft. n8n & Nebius AI Studio
- Plans to enable users to construct complex AI workflows by integrating diverse tools, specifically demonstrating combinations of N8n and Nebius AI Studio for automated tasks.
- Strategy involves building an AI-native data center through stacked and racked chips to maintain full vertical control from hardware to API, leveraging engineering-led design and vertical integration for market advantage.
- Expectations include providing open source LLMs that allow organizations to switch between over 30 models in seconds via simple endpoint replacements, supporting base models for cost optimization and fast models for speed.
- Operational goals prioritize speed, costs, reliability, and privacy, featuring zero data retention, EU-hosted infrastructure in Paris, and optimized inference on NVIDIA chips to ensure data safety.
- Architectural plans focus on common frameworks compatible with NVIDIA, Agno, Langchain, and Crew AI, while avoiding vendor lock-in through an open ecosystem model.
- Agent capabilities are designed to utilize tool calling, memory, and structured output to plan ahead and execute tasks without complex prompting, aiming to function as "Zapier meets GPT plus logic."
- Specific use cases outlined include agents that analyze sales data from Google Sheets to generate six-month strategies, update Google Docs, utilize calculators, and manage background document writing.
- Scalability projections anticipate expanding agents to handle sales and marketing outreach, internal HR support queries via Slack, competitive intelligence scraping, and customer support log summarization.
- Support tools include a GitHub cookbook with copy-paste presets, Helicone for observability and evaluation, and guidance on implementing if-else branching logic for conditional agent actions.
- Pricing and accessibility strategies offer $1 initial credits for new users, batch inference for 5 gigabytes of data processed within 24 hours at 50% lower cost than real-time, and rate limits exceeding OpenAI via autoburst capabilities.
- Expectations indicate that owning the full stack enables higher rate limits and cost efficiency compared to closed proprietary models, while NVIDIA collaboration ensures optimized performance.
- Long-term planning is supported by NASDAQ listing status and backing from Deep Capital to sustain engineering-led development and competitive positioning in the AI space.