GTM Strategy

Phase 1: Foundation – Building Viral Mini-apps

Phase 1- Build Foundation of Domain Specific LLM's with Quality data

• Target Audience: Retail/end-users, including individuals and small businesses looking for AI-driven solutions integrated into their daily lives.

• Description:

  • Develop core AI Models optimized for local devices using SoraChain AI Network and utility-based mini-apps.

  • Focus on integrating these small AI model into Telegram as a platform, offering users a seamless experience with lightweight, feature-rich mini-apps for tasks like productivity, scheduling, content creation, and personalized recommendations.

  • Build user trust and familiarity with AI services through easily accessible and privacy-respecting applications.

• Example

  • A Telegram mini-app that acts as a virtual assistant, helping users draft emails, plan daily tasks, or summarize lengthy messages.

  • Retail users can interact with AI-powered tools directly in their messaging app without needing additional software or technical expertise.

2. Developer Onboarding and Enablement

Phase 2: Developer Onboarding and Enablement by provding easy to use Pipeline of toolset

• Target Audience: Developers, startups, and AI enthusiasts looking to integrate advanced AI capabilities into their applications or services.

• Description

  • Focus on onboarding developers to the platform by providing easy-to-use tools, SDKs, and APIs for building and deploying custom mini-apps and AI-powered solutions.

  • Empower developers to build and scale their own applications leveraging the core LLMs and federated learning capabilities, enabling a wide range of AI use cases across different sectors.

• Example:

  • Offering an SDK that allows developers to quickly create Telegram bots with advanced language understanding and natural language processing features.

  • Providing an app marketplace for developers to showcase and monetize their mini-apps, creating a diverse ecosystem of AI-powered services.

3: Multi-Agent System on Device

Phase3-Introduce multi-agent systems (MAS) enabling intelligent, decentralized decision-making on device

• Target Audience: Consumers, businesses, and developers seeking to integrate intelligent, multi-functional AI agents directly on devices (smartphones, IoT devices, edge computing).

• Description:

  • Introduce multi-agent systems (MAS) that operate locally on edge devices, enabling intelligent, decentralized decision-making without reliance on cloud processing.

  • Each device hosts multiple autonomous agents that can collaborate, share insights, and execute tasks independently or in coordination with other agents, ensuring efficient, real-time performance.

  • Focus on privacy, where sensitive data is processed locally, maintaining security and minimizing data transfer to external servers.

  • Develop frameworks for integrating various types of agents (e.g., personal assistants, task managers, environmental sensors) into everyday consumer and business products.

• Example:

  • A smart home system where different agents control lighting, security, and climate systems independently but collaborate to optimize energy usage or enhance security.

  • A smartphone with several personal assistants that handle different tasks (e.g., scheduling, reminders, shopping), working in concert to improve user experience.

4: One-Click Integration of SoraChain AI Framework with Compute Engines

Phase 4-One Click Integration of SoraChain framework deployment with major Compute Providers

• Target Audience: Enterprises, AI developers, and organizations looking to easily integrate the SoraChain AI framework with their existing compute infrastructure.

• Description:

  • Simplify the process of deploying the SoraChain AI framework by providing one-click integration with popular compute engines and cloud platforms.

  • Focus on automating the connection between SoraChain’s privacy-preserving federated learning system and users’ compute resources (e.g., GPUs/TPUs, edge devices), enabling fast and efficient AI model training and deployment.

  • Ensure scalability and ease of use, allowing organizations to seamlessly leverage SoraChain AI’s capabilities without complex setups or manual configurations.

• Example:

  • A company can quickly deploy the SoraChain AI framework on their cloud infrastructure with a single click, instantly enabling federated learning and privacy-preserving AI on their compute engines.

  • Developers can integrate the framework into their AI pipelines, optimizing performance without needing deep technical knowledge of federated learning.

5: Enterprise and Institutional Solutions

Phase 5-Deploy Enterprise ready framework and domain specific subnet offerings

• Target Audience: Large enterprises, government institutions, and research organizations seeking tailored, scalable AI solutions for complex, privacy-sensitive use cases.

• Description:

  • Develop customized solutions leveraging the SoraChain AI framework to meet the specific needs of large-scale enterprises and institutional clients.

  • Focus on delivering secure, decentralized AI models that ensure data privacy and compliance with industry regulations (e.g., GDPR, HIPAA) for sectors like finance, healthcare, and public sector.

  • Provide end-to-end services, including consulting, deployment, integration, and ongoing support, to enable institutions to harness the full potential of federated learning and AI.

  • Offer robust tools for managing large, distributed datasets and improving AI model performance without compromising data confidentiality.

• Example:

  • A financial institution deploying SoraChain AI to collaboratively train fraud detection models across multiple branches without sharing sensitive customer data.

  • A healthcare provider using federated learning to enhance diagnostic tools with data from hospitals worldwide, ensuring compliance with patient privacy laws.