BoardBrief Pipeline

• 

  Workflow

Inspiration

The energy sector moves at a breakneck pace, yet the communication between Trading Analysts and Traders often relies on manual, repetitive reporting. Observing this bottleneck in a professional power trading environment, we saw an opportunity to automate the "analyst-to-trader" pipeline. Our goal was to streamline these interactions and create a generalized framework applicable to any high-stakes, data-heavy domain.

What it does

Our project is a sophisticated AI-driven data science workflow that transforms raw market data into professional voice presentations.

Analysis: It ingests PJM market data and uses SphinxAI to extract critical insights and report on trends.

Synthesis: These insights are passed to Gemini, which transcribes the technical data into a humanistic script tailored for an executive audience.

Delivery: Finally, ElevenLabs converts the script into a professional-grade audio presentation suitable for conference calls, Zoom meetings, or shareholder updates.

How we built it

The backbone of the project consists of three primary technologies: SphinxAI for deep data analysis, Gemini for contextual transcription, and ElevenLabs for high-fidelity text-to-speech.

Challenges we ran into

Prompt Engineering: Perfecting the transition from technical analysis to a natural, "human" speaking tone for ElevenLabs required rigorous formatting and iteration.

Contextual Nuance: We had to account for varying user interests—some traders prioritize renewables, while others focus on FTRs or Day-Ahead versus Real-Time spreads.

Confidentiality: Balancing general market data with user-specific, confidential context was a major hurdle in ensuring the tool's practical utility.

Accomplishments that we're proud of

We successfully engineered a functional, end-to-end product lifecycle. Beyond just a hackathon proof-of-concept, we are incredibly proud that this workflow will be discussed for implementation in a real-world trading environment.

What we learned

Building this project deepened our understanding of automated pipelines and the immense potential for AI to solve niche, professional pain points. We saw firsthand how emerging AI technologies can be woven together to create a product that feels intuitive and human.

What's next

Dynamic Interaction: We plan to shift from a linear presentation format to a conversational UI, allowing users to ask follow-up questions in real-time.

Hyper-Personalization: We aim to integrate deeper company-specific context so the AI is aware of a trader's specific positions and priorities.

Built With

• elevenlabs
• python
• sphinxai