Industrial Intelligent Agent

BangQi Technology leverages its industry-plus-AI technology ecosystem to develop echOS, an enterprise-grade AI operating system, and Unitrix, the world’s first industry-focused Agentic AI multi-agent collaboration platform. Together, these solutions provide comprehensive coverage across the core business processes of manufacturing enterprises—from procurement and operations to sales. Beyond enabling internal process optimization, cost reduction, and efficiency gains, the platforms empower enterprises to transcend organizational boundaries by connecting upstream and downstream partners, seamlessly integrating with channel customers. This creates a robust, industry-scale network of intelligent agents working in tandem, fostering seamless, real-time interactions and driving smart collaboration between supply and demand sides.

In manufacturing scenarios, BangQi Technology’s intelligent agents are no longer limited to supporting roles in the background—they are now directly integrated into critical processes such as order processing, procurement sourcing, production and operations collaboration, and sales execution.

Procurement Process: Intelligent agents handle tasks such as supplier sourcing, automated price inquiries and comparisons, contract term reviews, and coordination between inventory management and planning—enabling end-to-end intelligent operations from detecting raw material price fluctuations to generating the optimal procurement strategy.

Operational Process: By leveraging the collaboration of an order intelligence agent, a production scheduling intelligence agent, and a data analytics intelligence agent, the system automatically optimizes production plans and inventory structures, ensuring efficient resource allocation while minimizing capital tied up in inventory.

Sales Process: Leveraging roles such as the Intelligent Order Clerk, Channel Data Analyst, and Smart Quoter, we enable customers to place orders independently, deliver personalized, dynamic pricing, and expand into new channels—resulting in a significant boost to order conversion rates and market coverage.

Decentralized Multi-Agent Collaboration

This refers to a scenario where, without a single central authority controlling the system, agents with distinct roles can autonomously perceive their environment, make independent decisions, and interact in real time with other agents—thanks to standardized protocols and a task分工 mechanism. Ultimately, these agents collaborate under a consensus-based approach to accomplish complex tasks. This decentralized model eliminates the single-point bottlenecks inherent in traditional centralized systems, making it particularly well-suited for intricate, multi-enterprise, multi-tier, and geographically dispersed networks like industrial chains. As a result, it enables highly resilient, reliable, and intelligent collaboration along with efficient resource allocation.

Industry-Grade AMA Smart Agent Consensus Protocol

A multi-agent collaboration standard tailored for industrial scenarios, enabling agents representing various roles—such as procurement, order management, sales, and logistics—to communicate using a unified semantic framework. This facilitates rapid consensus-building during task breakdown, priority allocation, and conflict resolution, while also ensuring a feedback loop is established after execution. As a result, these agents can not only collaborate seamlessly within a single enterprise but also extend their coordination across enterprises and entire industry chains, optimizing resource allocation and matching supply with demand. Ultimately, this approach paves the way for building a truly efficient, autonomous, and self-evolving industrial-grade agent network.

Distributed Collaborative Algorithm

Allow multiple business units—such as factories, warehouses, suppliers, and channel stores—to make local decisions in real time, based on their own business goals, without relying on a single "command center." Instead, they can use algorithms to exchange information and dynamically adjust their operations: for instance, different factories can automatically divide tasks according to their production capacity and order demands; suppliers can swiftly align with customer needs by assessing inventory levels and delivery capabilities; and channel stores can automatically initiate restocking based on sales data.

Unique three-level RAG model

At the individual level, the system primarily leverages personal memory and dedicated small corpora—such as customer orders, product specifications, and conversational context—to deliver quick, accurate, and highly relevant responses. This is ideal for frontline tasks like handling quotes, order tracking, and answering queries. At the enterprise level, the system seamlessly integrates with internal systems like ERP, MES, and CRM, as well as document repositories, enabling efficient retrieval and management of both structured and unstructured data—including BOMs, inventory records, price lists, and contracts. This ensures consistent data definitions across departments and maintains full traceability, ultimately supporting cross-departmental operations and strategic decision-making. At the industry level, the system connects upstream and downstream stakeholders within the industry, along with external knowledge resources such as raw material market trends, industry standards, and channel intelligence. Thanks to robust permissioning and compliance controls, it facilitates secure, cross-enterprise sharing and access to this external expertise. This empowers complex business processes like supply chain collaboration, joint production planning, and cross-company quoting by providing critical external insights.