Leveraging your medicast.ai expertise to transform arboriculture knowledge with Llama 4 and Google Cloud
Explore PartnershipBuilding on your successful medicast.ai architecture while embracing cutting-edge advancements
Leveraging Meta's latest Llama 4 model with its impressive 10 million token context window, enabling processing of entire research papers and their citations in a single pass.
This represents a significant upgrade from the LLAMA-3.3-70B used in medicast.ai, particularly for handling complex arboriculture terminology and concepts.
Utilizing Google Cloud's robust infrastructure for deployment, including Google Kubernetes Engine, Cloud Run, Vertex AI, and Cloud Storage.
This provides enhanced scalability, reliability, and cost-efficiency compared to the AWS infrastructure used for medicast.ai.
Continuing with ElevenLabs for high-quality text-to-speech, but with custom voice profiles for each botanical personality, creating a more engaging and distinctive listening experience.
Developing with SwiftUI for iOS and Kotlin for Android, with shared logic through Kotlin Multiplatform Mobile, aligning with your transition away from React Native/Expo.
Implementing a modular, scalable architecture with specialized services for content acquisition, transformation, audio generation, delivery, and user management.
Integrating BigQuery for powerful data analysis and insights, enabling continuous improvement of content quality and user experience.
Your expertise in AI-powered knowledge transformation is the perfect foundation for arborcast.ai
Zanir's interest in knowledge absorption through audio content was the original inspiration for medicast.ai. This same vision applies perfectly to the arboriculture industry.
Kesav's technical leadership on the medicast.ai project, which won the Llama track prize at the AITX Hackathon, provides the perfect expertise for implementing arborcast.ai.
Your firm's experience in developing specialized AI solutions across multiple industries creates the perfect foundation for extending the medicast.ai concept to arboriculture.
Building on your proven medicast.ai architecture with enhanced capabilities
Enhanced version of your successful medicast.ai architecture
Extending your successful model to a new professional vertical
Apply your proven medicast.ai approach to the arboriculture industry, with over 90,000 professionals in North America alone facing similar challenges in staying current with research.
Leverage your existing expertise and components to significantly reduce development time and risk, potentially moving from concept to MVP in a similar timeframe to your medicast.ai hackathon success.
Projected revenue of $500K-$750K in Year 1, growing to $5M-$7M by Year 5, with subscription model proven in healthcare adapted to arboriculture.
Demonstrate cross-industry applicability of your solutions, positioning Ferociter as innovators in knowledge transformation across multiple professional domains.
Establish a pattern that could be applied to multiple specialized professional fields, creating a scalable platform for knowledge transformation.
Partnership model that maximizes the value of your technical expertise while providing equitable distribution based on contribution and ongoing involvement.
A phased approach leveraging your established workflow
Technical discovery sessions, market validation interviews, and detailed project planning. Establish technical architecture and development approach.
Hackathon-style development sprint to create initial prototype. Implement core content transformation pipeline and basic mobile interface.
Limited release to select arboriculture professionals. Gather feedback, refine content quality, and enhance user experience.
Public release with complete feature set. Implement marketing strategy and begin subscription onboarding.
Add advanced features, expand content library, and refine botanical personalities based on user feedback and usage patterns.
Let's leverage your medicast.ai expertise to create another groundbreaking platform that bridges the gap between research and practice.
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