At this historical juncture of AI transitioning from generalization to verticalization, DecideAI uses blockchain technology as a foundation to build an open ecosystem integrating data governance, identity verification, and model sharing, establishing a new paradigm for the development of large language models in specialized fields. This AI infrastructure, created by a global top technology team, is redefining the value creation system of the next generation of artificial intelligence.

I. Golden opportunity and infrastructure dilemma for vertical domain LLMs.

The global large language model market is expected to exceed $260 billion by 2030, but the current market faces structural contradictions: the demand for specialized models in high-value fields such as healthcare and finance is increasing, while existing infrastructure struggles to meet the development needs of professional-grade AI. Traditional paths face three major constraints:
1) Data quality dilemma: the 'data refining' mechanism of general models cannot meet the precision requirements of specialized scenarios.
2) Talent collaboration barriers: domain experts find it difficult to effectively integrate into the model training closed loop.
3) Technical black box risks: centralized architecture leads to privacy leaks and uncontrollable algorithms.

II. Trinity of technical solutions.

DecideAI addresses industry pain points through a modular architecture, constructing a comprehensive infrastructure for professional LLM development.

Decide Protocol: RLHF-enhanced precision training engine.

As the core driving system of the ecosystem, this protocol innovatively deeply integrates reinforcement learning with human feedback (RLHF). Through a five-stage training system – domain adaptation (Seeding), expert annotation (Annotation), incentive consensus (Incentivization), model iteration (Training), real-time evolution (Evolution) – it forms a continuously optimizing flywheel effect.

Technical highlights include:

  • Use DeBERTa v3 architecture to achieve heteroscedastic uncertainty assessment.

  • Integrate TRLX framework and PPO algorithm to ensure training stability.

  • Introduce Shapley value to quantify data contribution.

  • Develop an annotation allocation mechanism to achieve precise matching of tasks and experts.

In medical diagnosis model training, the system can automatically generate professional prompts such as 'chest pain emergency differential diagnosis', which are labeled and graded by certified physicians, ultimately forming a professional model with clinical decision support capabilities.

Decide ID: digital identity layer built on zero-knowledge proof.

This certification system establishes a trusted digital identity system through an original 'Proof of Personhood' protocol.

  • Dual verification of biometrics + educational/professional qualifications

  • Zero-knowledge proof technology enables privacy protection.

  • Cross-platform universal Principal ID architecture.

In model training scenarios, the system ensures that each training data point can be traced back to a certified expert in the specific domain, fundamentally eliminating data contamination. This identity layer can also extend to over 20 application scenarios such as DeFi compliance and digital content rights confirmation.

Decide Cortex: open-source collaborative knowledge hub.

This model sharing platform breaks technological monopolies and provides two core values:

  • Flagship model library: continuously updated foundational LLM (text generation/summary/question answering)

  • Vertical model marketplace: pre-trained specialized models (e.g., anti-abuse detection model Redactor)
    Supports API calls and customized training, equipped with a complete model monitoring and management system.

III. Incentive revolution empowered by blockchain.

The ecosystem constructs a value circulation system through DCD tokens:

  • Data annotators receive rewards based on their contributions.

  • Developers share ecosystem benefits through tool development.

  • Quality-first Leaderboard mechanism.

  • Incentive distribution system resistant to witch-hunt attacks.

This Proof of Contribution-based economic model transforms the one-way consumption of traditional AI R&D into sustainable value co-creation.

(Team showcase)
Founder Raheel has assembled top talents from the University of Waterloo and the University of Toronto: Dr. Jesse Glass, an expert in machine learning with over ten AI patents, Chief Architect Tareq Khandaker, who led core system development for companies like Qualcomm, and COO Pema, who brings global capital market operational experience. This team, combining academic depth with industry experience, is redefining the technological landscape of AI infrastructure.

(Future outlook) While most enterprises are still chasing parameter competitions, DecideAI has built a complete ecological closed loop for professional LLMs. This open platform, which integrates blockchain transparency, zero-knowledge security, and RLHF precision, is setting a new standard for infrastructure in the AI 2.0 era – where every contribution of professional wisdom will be precisely measured, and every vertical domain's know-how can be transformed into competitive advantages in the intelligent era.