Wiener offers an industrial‑grade Agentic AI platform that generates high‑quality, domain‑specific interaction data and integrates it into a closed‑loop system for enterprises. Its three‑layer nested feedback cycle and multi‑model collaborative architecture enable automatic task decomposition, stepwise processing, and continuous error correction, delivering high‑precision AI outputs at scale.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often struggle to deploy generative AI solutions that deliver reliable, high‑precision results at scale because existing models lack closed‑loop feedback, domain‑specific interaction data, and continuous error correction.
Solution
Wiener provides an industrial‑grade Agentic AI platform that generates high‑quality industry interaction data and integrates it into a closed‑loop system. The platform uses a three‑layer nested feedback cycle to coordinate data generation, model training, and human‑machine interaction, enabling models to self‑evolve their prompts and correct errors based on user feedback. Its multi‑model collaborative architecture automatically decomposes tasks, processes steps sequentially, and validates outputs, ensuring consistent accuracy for complex enterprise workflows. Continuous learning algorithms keep the system up‑to‑date with evolving business contexts, reducing operational costs and driving innovation.
Target Audience
Primary customers are large enterprises and industry verticals seeking to integrate reliable, self‑optimizing generative AI into their operational workflows, such as manufacturing, finance, and technology firms.
Features
- Three‑layer Synergistic Nested Feedback Cycle linking data generation, model training, and interaction for co‑evolution of AI behavior
- Master‑slave multi‑model collaboration that auto‑splits tasks, handles stepwise processing, and performs result verification
- Continuous Learning Algorithm for Prompt Evolution that dynamically refines prompts to handle high‑complexity commands
- Error‑Feedback driven Continuous Learning system that automatically detects and corrects inaccurate outputs using user feedback
- Domain‑specific high‑precision interaction data generation pipeline to train models on industry‑relevant scenarios
- Scalable architecture designed for enterprise deployment with industrial‑grade reliability and security