Group Uniform - Video Delphi

Why It Matters

By providing transparent, data‑driven consensus tools, the platform can improve decision‑making in healthcare and other sectors plagued by echo chambers. It creates a market for structured, expert‑led dialogue that leverages AI without sacrificing rigor.

Key Takeaways

• Delphi technique fosters expert consensus via iterative questioning
• Platform merges video, text, and LLM facilitation
• Real-time analytics enable adaptive discussion configurations
• Aims to counteract algorithmic echo chambers
• Substack-like UI simplifies expert panel management

Pulse Analysis

Algorithmic recommendation engines dominate social media, often reinforcing users' existing beliefs and fragmenting public discourse. In healthcare, where evidence‑based decisions are critical, such polarization can hinder policy formation and patient outcomes. The Delphi technique—an iterative, anonymous survey method—has long been a trusted approach for achieving expert consensus, yet its traditional implementation lacks the speed and scalability demanded by modern digital ecosystems. Integrating this methodology with AI offers a pathway to restore balanced, data‑rich conversations.

The proposed platform blends video streaming, text forums, and large‑language‑model assistance to orchestrate Delphi rounds at scale. Participants engage through a Substack‑style dashboard, submitting insights that the LLM synthesizes into concise summaries and follow‑up queries. Embedded analytics track sentiment, participation rates, and argument evolution in real time, empowering moderators to adjust question phrasing, panel composition, or discussion pacing on the fly. This feedback loop transforms each run into a learning experiment, continuously sharpening the quality of consensus outcomes.

For investors and industry leaders, the solution opens a lucrative niche at the intersection of AI, health tech, and collaborative media. Organizations can deploy the tool for clinical guideline development, regulatory reviews, or internal strategic planning, reducing reliance on fragmented social signals. By delivering transparent, auditable consensus processes, the platform not only mitigates echo‑chamber effects but also builds trust among stakeholders, positioning it as a cornerstone for future AI‑augmented decision frameworks.

Original Description

Client - Ric da Silva, Flok.health

Arissa-Elena Rotunjanu, Adam Whittome, Laksh Sharma, Boris Hall, Nick Ha, Tosin Bea Jokosenumi

Social media and video streaming platforms create algorithmic bubbles that polarise public debate in unhealthy ways. In contrast, the Delphi Technique is used in healthcare and other domains for reaching consensus, by iteratively presenting a panel of experts with a question. This project is to build a media platform for developing LLM-assisted Delphi consensus. The platform should enable video and text forums to be set up and configured, perhaps in the style of Substack. But also, importantly, it should include comprehensive introspection and analytics on the discussion process as it unfolds so that the configuration may be updated to improve the result in a subsequent run.