AI for Everyone: How Gooey.AI Empowers Global Frontline Workers with Low Code Workflows - Ep. 244
Episode
40 min
Read time
2 min
Topics
Productivity, Health & Wellness, Remote Work
AI-Generated Summary
Key Takeaways
- ✓Platform Architecture: Gooey.AI abstracts AI components into hot-swappable modules, allowing users to compare OpenAI, Google, and open-source models side-by-side for performance, cost, and carbon usage without managing DevOps infrastructure or individual API subscriptions.
- ✓Golden Q&A Evaluation: Organizations upload custom question-answer datasets representing their specific use cases—like farming in Uganda or HVAC repair—to benchmark which model combinations perform best, moving beyond generic benchmarks like MMLU that don't reflect real-world applications.
- ✓Hallucination Prevention: For high-stakes applications like healthcare or agriculture, the platform uses semantic search to match user queries against pre-approved expert answers rather than generating new responses, ensuring accuracy while tracking gaps to expand the knowledge base systematically.
- ✓Collaborative Workflows: The platform enables teams to fork existing AI recipes with visible prompts and models, work together with version control, and deploy directly to WhatsApp or Slack—mirroring how Google Docs democratized document collaboration beyond standalone desktop tools.
What It Covers
Gooey.AI founders explain their low-code platform that enables non-technical users to build AI workflows using multiple models, focusing on frontline worker applications like Ulanghizi, a multilingual agricultural chatbot serving African farmers via WhatsApp.
Key Questions Answered
- •Platform Architecture: Gooey.AI abstracts AI components into hot-swappable modules, allowing users to compare OpenAI, Google, and open-source models side-by-side for performance, cost, and carbon usage without managing DevOps infrastructure or individual API subscriptions.
- •Golden Q&A Evaluation: Organizations upload custom question-answer datasets representing their specific use cases—like farming in Uganda or HVAC repair—to benchmark which model combinations perform best, moving beyond generic benchmarks like MMLU that don't reflect real-world applications.
- •Hallucination Prevention: For high-stakes applications like healthcare or agriculture, the platform uses semantic search to match user queries against pre-approved expert answers rather than generating new responses, ensuring accuracy while tracking gaps to expand the knowledge base systematically.
- •Collaborative Workflows: The platform enables teams to fork existing AI recipes with visible prompts and models, work together with version control, and deploy directly to WhatsApp or Slack—mirroring how Google Docs democratized document collaboration beyond standalone desktop tools.
Notable Moment
The team built Radbots in 2021, AI personas created by playwrights and poets that passed Turing tests through video messaging, with one child interacting over 1,200 times—proving non-coders could craft compelling AI experiences when given accessible orchestration tools.
Episode Transcript
Hello, and welcome to the NVIDIA AI podcast. I'm your host, Noah Kravitz. Our guests today were recently featured on the NVIDIA blog for their work in creating Ulanghizi, an AI chatbot that delivers multilingual support to African farmers via WhatsApp. As vital a project as that is, however, GUI AI is much more than a single chatbot. GUI AI is a platform for developing low code workflows built on private and open source AI models. Combining ease of use with innovative features like golden q and a's, GUI enables developers to code fast and change the world. Here to tell us the GUI story are the company's founder and CEO, Sean Blagsvath, and founder and chief creative officer, Archana Prasad. Welcome to you both, and thanks so much for joining the NVIDIA AI podcast. Hello? Hi. Thanks, Noah. So there's a lot that I'm looking forward to you getting into about the GUI platform, how it started, all the things it can do, including how you're helping developers combat AI hallucinations, which is a big topic these days. But I'd love it if you can start at the beginning and tell us what GUI AI is and how you got started. Let me take a shot at that. We got started from actually a digital arts project funded by the British Council many moons ago. I'd like to say 2018, 2019, where we applied to create an AI persona that would match make creators, activists, designers from across borders of The UK and India. And we won that award. We built out a prototype, we tested it, it worked beautifully. And, you know, long story short, we managed to get funded, seed fund from, Techstars in a hurry. Yep. Yep. We got into Techstars, which was an excellent program. We took this idea of an AI persona and built an entire communications app around it called dara.network meant to service cultural organizations and social impact organizations, enable them to manage their alumni and keep in touch with each other easily. The first AI persona we built also confusingly called Dara, feeling lonely and wanted some friends. And we thought, wouldn't it be great to invite non tech folks, writers, playwrights, authors, poets, could we have them come in and craft their own AI personas from scratch? And this is right in the middle of COVID. So all those Okay. Are out of work. Right? They're out of work. They're isolated. Isolated. Yep. Yep. Yeah. Yeah. And so we invited 23 folks from across The UK, The US, India, Sri Lanka, even, and met constantly, literally every week and ended up developing pretty much, underlying architecture that enabled them to build out what one might call a Turing test testing video box. Right? So our cofounder, Dave, was hanging out in Discord forums with, what's his name, Brockman, with the president of OpenAI until I think Chris Brockman. Yeah. Yeah. Yeah. Oh, it's, like, five years ago. And then so …
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