Industry opportunities: Paradigm shift from general to vertical.
The global large language model (LLM) market is experiencing explosive growth, projected to reach $260 billion by 2030. In specialized fields such as healthcare, finance, and law, there is a surge in demand for precise and scenario-based intelligent services. The core contradiction faced by the current AI industry is that traditional general models are difficult to meet the stringent requirements of specialized scenarios, while building vertical models faces challenges in data quality, talent reserves, and cost control.
McKinsey's research shows that 73% of companies are limited by data quality when deploying AI, and the cost of professional domain data labeling is 5–8 times that of general scenarios. Against this backdrop, DecideAI has constructed the first open AI infrastructure aimed at vertical domains, using 'specialized models as a service' as an entry point. Its innovative ecosystem, through a blockchain + AI fusion architecture, is reshaping the LLM development paradigm.
Technical architecture: A trinity value closed loop.
1. Decide Protocol — — Model refining factory.
A hybrid training framework using Reinforcement Learning with Human Feedback (RLHF) has been established, covering the entire lifecycle of the model:
Precise training mechanism: Dynamic data quality monitoring is achieved through the heteroscedastic uncertainty assessment combined with the DeBERTa v3 architecture.
Expert collaboration network: Quantifying each data point's contribution to model performance through Data Shapley value calculation and influence functions.
Continuous evolution system: Based on the TRLX framework and PPO algorithm, a feedback loop for model performance and real-time interaction data has been established.
In medical diagnosis scenario testing, this protocol improved model accuracy by 42%, reducing the hallucination rate to below 0.3%. Its innovation lies in extending traditional RLHF to include enhanced training protocols that incorporate data provenance, contribution quantification, and real-time optimization.
2. Decide ID — — Trusted digital identity layer.
The pioneering 'Personality Proof' (PoP) system breaks through the limitations of traditional KYC:
Multi-dimensional verification system: A zero-knowledge proof (ZKP) solution integrating biometric features, educational credentials, and professional certifications.
Dynamic trust mechanism: Continuously updating reputation scores through on-chain behavior analysis.
Privacy protection architecture: Utilizing a Self-Sovereign Identity (SSI) model to achieve minimal data collection.
This system improves the qualification review efficiency of professional labelers by 80%, successfully intercepting 99.6% of fraudulent identity attacks in financial risk control scenarios. Its core value lies in establishing a new type of production relationship in the AI era — enabling the professional value of data contributors to be quantifiable, tradable, and accumulative.
3. Decide Cortex — — Model collaboration network.
Creating an open model as a service (MaaS) platform:
Dual-track model library: Includes 12 types of basic models and 28 models for specific vertical scenarios.
Intelligent deployment system: Supports three modes: API calls, privatized deployment, and hybrid training.
Value circulation ecosystem: Achieving a closed loop of model usage, data trading, and contribution incentives through DCD tokens.
A typical application case of the 'Redactor' content review model, achieving a 98.7% accuracy rate in identifying violations in social media scenarios, with a response speed improved 5 times compared to traditional solutions. The platform's unique model provenance system ensures that each training version is verifiable and auditable.
Ecosystem advantages: Building a value internet for the AI era.
DecideAI's innovative breakthroughs are reflected in three dimensions:
Data valorization: Transforming contributions of professional knowledge into tradable digital assets through a token economic model.
Collaborative networking: Establishing cross-institutional and cross-domain model collaboration development agreements, reducing redundant R&D costs by over 60%.
Governance transparency: A blockchain-based distributed ledger achieves auditability of the entire model training process.
In terms of compliance, the system integrates a GDPR-compliant framework, ensuring compliant use of sensitive information such as medical data through dynamic access control and differential privacy technology. The ecosystem has formed a distributed collaboration network with over 900 professional labelers and more than 50 industry experts.
Founding team: Cross-disciplinary technical vision.
The core team brings together cross-disciplinary talents in AI engineering, distributed systems, and public policy:
Raheel (CEO): Background in software engineering from the University of Waterloo, having led the architectural design of three products with tens of millions of users.
Jesse Glass (Chief AI Scientist): An authority in reinforcement learning, holding 12 machine learning patents.
Tareq (Chief Architect): Designed microservice systems capable of processing 1 billion requests daily, proficient in federated learning architecture.
Pema (COO): A global capital market expert, having led multinational technology policy formulation.
The team's unique 'technology-policy' dual-drive model allows it to maintain a forward-looking layout in compliance system construction for the EU AI Act, the US NIST framework, and more.
Industry outlook: Defining AI 2.0 standards.
With global regulations tightening and industry demands upgrading, the 'specialized + decentralized' path pioneered by DecideAI is becoming a new trend. Its ecological value lies not only in technological breakthroughs but also in creating a sustainable value distribution system — enabling data producers, model developers, and end users to form a symbiotic relationship.
In a critical stage of AI's deep integration into industries, DecideAI's practice demonstrates the professional value of an open collaboration model. This methodology of deeply embedding human expertise into model evolution may become the industry standard for the next generation of AI infrastructure.
