AI assists in formulating the most effective production and material planning to respond to rapid market and production line changes
Through the intelligent dispatch scheduling system, the manufacturing industry can quickly respond to market changes, improve efficiency, deliver on time, and move towards the transformation of smart factories
Combining automation and feedback systems to master the real-time operation and intelligent management of factories
Integrate warehouse management, inbound and outbound operations, and barcode automation to optimize inventory shipment costs and efficiency
Accurately monitor process variation, protect yield stability, and manufacturing quality
Complies with international regulations to ensure product safety, quality stability, record integrity, and quality defect tracking report processing requirements
Deconstruct the data value chain and trace the manufacturing process and data source
Master the key data of manufacturing execution, quickly query and visualize analysis in one go
The Intelligent Data Platform empowers AI and GenAI to accelerate transformation and drive innovative decision-making applications
The data convergence middle platform integrates enterprise data assets to improve operational efficiency and cross-domain insight capabilities
Empowering Enterprises from AI Strategy to Full Deployment – Mastering the Rhythm of Digital Transformation
Five core works and steps of a generative AI service to help enterprises develop applications
From data integration and conversion to visual output, help enterprises get rid of complicated manual operations and realize digital reports and intelligent decision-making processes
Forecast future demand trends for products or services, helping businesses plan ahead and reduce risks
Help you improve business performance and solve the core propositions of "efficiency" and "return"
AAT launches popular training courses that are highly praised
AAT and the Republic of China Computer Education Development Association jointly promote the "Business Data Analyst" certification
Advant Analytics Tactics Ltd. (AAT) and the Republic of China Computer Education Development Association jointly promote the "Business Data Mining Science Analyst" certification
Integrate data, provide AI analysis and visualization, improve decision-making efficiency, and ensure security
Generative AI data platform supports multiple models, efficient data management, and governance
Visual data mining, no programming required, supports predictive modeling and text analysis, and simplifies complex data processing
Powerful statistical analysis tools support data visualization and modeling, simplify complex data processing and decision-making
No coding required, combining open source and enterprise tools to provide intuitive data analysis, AI modeling, and cross-team collaboration solutions
Free open-source visual workflow, no coding required, supports data integration, machine learning, and AI analysis
Enterprise-level platform, supports collaboration, automation, and governance, strengthens data science deployment, and team sharing
Integrate IBM SPSS Statistics, provide data analysis and visual reports, support automated BI and decision-making
Based on IBM SPSS Modeler, supports enterprise-level data mining, integrates AI and predictive analysis
Intelligent survey platform, supports multi-technology questionnaire design and data analysis, generates dynamic reports
Integrate AI and BI, no coding required, provide real-time insights, automated workflows, and cross-cloud deployment, help enterprise decision-making
Self-service BI supports big data analysis, visual operations, and rapid insights, suitable for all employees
Excel-like design, fast creation of complex reports, support for visualization, and mobile terminal viewing
ETL and API integration, fast access to heterogeneous data, and solving data island problems
Consulting Services
With the rapid development of generative AI technology, data analysis companies must establish a comprehensive technical service chain to meet the growing market demand and establish a differentiated competitive advantage. To this end, AAT offers five core steps for generative AI services, assisting companies in developing their own generative AI applications. Since the application field of generative AI is quite wide, the following is a description of its introduction methodology for general applications. The unique application scenarios for each industry will be provided by AAT consultants.
AAT establishes an evaluation framework based on the dual axes of "technical feasibility" and "commercial value." The technical level includes data status evaluation, natural language understanding accuracy test, generated content consistency verification, and API integration complexity analysis. These evaluations test model performance through enterprise interviews and prompt test sets that simulate real enterprise scenarios.
Business evaluation requires quantitative ROI indicators, including workforce hours saved, error rate reduction ratio, and potential revenue growth model construction. Analysis Industry Consultants will help summarize the "technology" and "business value" analysis and recommend suitable application entry points for enterprises.
Prioritize initiatives in the “Sweet Spot” quadrant to maximize ROI with minimal risk.
Advanced diagnosis will introduce "differentiation positioning analysis" to compare competitors' technology stacks and application scenarios, identifying market gaps. For example, for financial industry customers, we focus on the specialized needs of compliance document generation and risk assessment models. Finally, a risk assessment report must be produced, including a data privacy compliance gap analysis and model bias detection results.
Data quality directly affects model performance, so AAT has established a “multi-source heterogeneous data model and integration pipeline”. The core technology includes document parsing programs, web crawlers, customization of enterprise internal database ETL tools, and vectorization of unstructured data (such as PDF contracts and meeting recordings). The key is to design domain-specific data models and cleaning rules. For example, medical texts require a professional terminology regularization vocabulary, and financial data necessitate a numerical type consistency verification module.
If the project process requires document annotation, AAT adopts a "hybrid annotation management mechanism" that combines an automated rule engine (such as filtering through regular expressions) with a human-machine collaborative verification process. For sensitive data, differential privacy technology is introduced to ensure compliance through anonymization processing, such as data desensitization. Finally, data version tracking records will be generated simultaneously to document the source distribution and feature statistics of each batch of data.
Data query is an application of large language models that is widely used within enterprises. Similar scenarios include regulatory/internal regulation queries, document manual queries, knowledge base queries, production line information queries, etc. This system needs to integrate "semantic understanding and knowledge search technology". The basic architecture consists of three layers: the bottom layer utilizes distributed indexing, the middle layer performs natural language semantic classification, and the upper layer can be combined with RAG or a related data search architecture to connect to the enterprise knowledge base. The key to querying large language models is to allow the system to "understand" domain-specific vocabulary or synonym expansions, such as automatically associating "revenue" with business terms such as "sales" and "performance".
Additionally, the query system must consider controlling access to permissions and filtering confidential information in real-time according to the user's role. This part requires a company-specific control system, achieved through system integration, a a large language model, and the overall configuration of the vector database.
During the model selection process, AAT helps the industry define application scenarios and specific task requirements, such as vertical domain knowledge enhancement, specific task optimization, or language localization. Consider hardware limitations, evaluate available computing resources and memory capacity, and select a model size that suits the customer's hardware conditions. When comparing basic models, study and analyze different open-source large language models, considering their architecture, parameter scale, and pre-training data. Finally, evaluate the licensing terms to ensure that the terms of use of the selected model meet your needs, especially for commercial applications.
Fine-tuning preparation includes data collection, data preprocessing, and data enhancement. During the fine-tuning process, select the appropriate fine-tuning method, set hyperparameters, and perform fine-tuning training. Monitoring and evaluation are conducted continuously throughout the training process to prevent overfitting. Deployment and optimization involve deploying the fine-tuned model in the target environment, conducting comprehensive testing, and regularly updating the model based on actual usage feedback.
The service of AAT aims to provide industry solutions. The technology selection requires the construction of a multi-dimensional evaluation matrix, including cloud-ground hybrid deployment cost simulation, model service API throughput stress testing, and disaster recovery solution verification. The hardware configuration recommends the use of a layered architecture to physically isolate LLM reasoning, vector databases, and application servers. At the software integration level, AAT customizes the model monitoring suite, which includes output toxicity detection, fact consistency verification, and a version control management interface. The talent training program designs a step-by-step course, from basic prompt engineering to advanced RLHF implementation, with real-life enterprise case exercises. Finally, a continuous optimization mechanism is established to regularly conduct technology stack health assessments and new model migration tests.
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Help you improve business performance and solve the core propositions of “efficiency” and “return”
AAT and the Republic of China Computer Education Development Association jointly promote the “Business Data Analyst” certification
Advant Analytics Tactics Ltd. (AAT) and the Republic of China Computer Education Development Association jointly promote the “Business Data Mining Science Analyst” certification
