Pain Points

1. Development Dilemma

Technical gap between business and developers causes requirement distortion and application-scenario mismatch.

2. Data Silos

Enterprise documents, equipment data, business systems scattered, knowledge extraction efficiency low.

3. Deployment Barriers

Difficulty integrating agents with industrial systems like MES and ERP, hard to form business closed-loop.

4. Management Out of Control

Lack of standardized process for agent development, chaotic version iteration and permission management.

User Value

Experience Agent Cockpit

Build AI Desk unified workbench with 70+ AI assistants, business experts develop, adjust, collect self-built efficient agent apps, cover internal knowledge Q&A, external customer service.

Directed Agent Quick Development

Provide manual and intelligent agent development modes. Business experts achieve visual orchestration, support debugging and history viewing. Development supports external system integration, including page integration and API methods, meet enterprise scalability needs.

Flexible Access to Mainstream Large Models

Provide powerful model management, support private and public cloud large models. Platform integrated 25+ mainstream models. Business experts use model evaluation to build best-performance agents.

Easily Integrate RAG Knowledge Base

Support building RAG knowledge base and visualization, business experts easily manage content. Upload internal multimodal documents, FAQs, specs to knowledge base, integrate into agents for precise answers and complex scenarios.

Application Scenarios

Intelligent Operations Manager

Access device real-time data, link with fault knowledge base and prediction models, auto-generate maintenance plans and dispatch orders. Fault response time reduced 50%, reduce million-level production losses.

Process Optimization Engine

Combine historical production data and industry knowledge base, intelligently recommend process parameters. Improve key process yield, annual cost savings over ten million yuan.

Cross-System Connector

Pre-set 200+ industrial system interface templates, break MES, ERP, WMS data silos. Achieve intelligent linkage of production, logistics, quality data, improve decision timeliness.

Cases

Case: A Semiconductor Customer Intelligent Centralized Control System

Customer Pain Points: Hard to detect issues, lack data monitoring: Production lacks real-time core indicator tracking. Hard to find weaknesses, business data scattered: Production, equipment, process, quality data scattered, no unified monitoring. Alarm lag, slow handling: Alarms scattered across systems, no centralized control. Hard to check violations, outdated inspection: Over-reliance on manual inspection, hard to find violators. Hard to find documents, hard to accumulate knowledge: Event handling has not formed data and knowledge accumulation, which cannot guide problem optimization

Solution: Based on Octopus Brain Agentic AI Platform, build intelligent centralized control system with smart knowledge recommendation, knowledge base, report generation, alarm analysis, intelligent dispatch, enhance factory ops intelligence and efficiency, create value and competitive advantage. Customer Benefits: Help achieve: Cost reduction: Operational costs down 30% via optimized scheduling. Quality control: Fault warning more accurate, handling time shortened 45%. Benefit improvement: Overall benefit up 5%, employee efficiency increased 60%.

Case: An LED Chip Maker Uses AI Middle Platform to Build Custom AOI-AI All-in-One Machine

Customer Challenge: Diverse high-end products, many custom, specs vary, traditional AOI cannot cover complex scenes. Only manual microscope inspection, low efficiency, high labor cost, quality hard to control.

Solution: Via AI middle platform, provide multiple algorithm libraries for model iteration, compatible with various products, customize algorithms as needed. Business Results: Solve quality inspection issues, yield increased from 95% to 98%, reduce labor costs.

Case: An Air Conditioning Leader Uses AI Middle Platform to Build AI Apps Improve SMT First-Pass Yield

Customer Challenge: SMT inspection uses AOI devices, but high overkill, hard to improve first-pass yield, need manual review; multiple AOI devices form data silos, hard for overall quality control and visualization.

Solution: Via AI middle platform training, help quickly build models, improve anomaly detection accuracy, reduce overkill, boost first-pass yield. Via AI middle platform BI, build visualization dashboards for clear quality control. Business Results: Model deployed in 1 month, overkill rate down from 40% to 0.8%, business users built dashboards in half a day for clear data view.