Enterprise AI Use Case Research and Analysis System: Architecture and Implementation

1. Business Context and System Objectives

As artificial intelligence continues to advance rapidly, enterprises are paying increasing attention to how organizations in their industries are adopting AI in real-world scenarios. For business analysts, data scientists, and AI researchers, efficiently collecting, analyzing, and distilling industry use cases is challenging and time-consuming.

Traditional manual research is inefficient and often constrained by individual perspectives. As a result, an intelligent AI use case research and analysis system has become an important enterprise capability. This article describes the overall architecture, core modules, and data-processing workflow of such a system, providing a practical technical guide for developers.

Enterprise AI use-case research typically faces three major challenges:

1. Fragmented data sources: Case information is often distributed across company blogs, press releases, solution pages, and other sections of corporate websites.
2. Difficulties in processing unstructured data: Web pages contain large amounts of redundant HTML, navigation elements, and unrelated content.
3. Time-consuming in-depth analysis: Specialists must read lengthy documents and extract key elements such as use cases, technology stacks, business challenges, and outcomes.

To address these issues, an intelligent research and analysis system should provide the following capabilities:

1. Multimodal data ingestion: Support batch importing of specific case-study URLs through CSV files, as well as automated discovery based on a company website domain.
2. Automated content extraction: Accurately retrieve the main body of a web page and remove irrelevant content, producing clean text for model analysis.
3. Intelligent analysis and summarization: Use the natural-language understanding and reasoning capabilities of large language models (LLMs) to analyze case studies in depth.
4. Multidimensional report generation: Automatically produce detailed reports for individual cases, cross-case trend analyses, and visual executive dashboards.

2. Overall System Architecture

To achieve these objectives, the system can be divided into four core layers:

• Data input and link discovery
• Content crawling and parsing
• LLM-based intelligent analysis
• Reporting and visualization

These layers are decoupled through asynchronous task queues and messaging middleware, which improves system availability and scalability. In practice, developers can use infrastructure services from cloud providers, such as object storage, message queues, and LLM platforms provided by Baidu AI Cloud, to build the underlying environment more efficiently.

3. Core Modules and Technical Implementation

3.1 Data Input and Link Discovery

This layer provides the system’s entry points for external data and supports two primary workflows.

• CSV batch input: The system reads a CSV file containing a list of target URLs and places them directly into the processing queue.
• Automated website discovery: For a company website domain, the system uses the Map capability of a web content crawling service, or a site-map parsing module, to traverse the website structure and discover available URLs.

After collecting a large number of candidate URLs, the system should not immediately crawl every page. Instead, it first performs lightweight link analysis. URL paths, anchor text, and other metadata can be sent to an LLM so that it can determine whether a link is likely to point to an AI case study or customer success story. This significantly reduces the cost of crawling irrelevant pages.

3.2 Content Crawling and Parsing

After target links are identified, the system extracts their content. Modern corporate websites often rely on dynamic rendering, making it difficult for traditional static crawlers to retrieve complete pages. The system should therefore integrate a Scrape engine with dynamic rendering support.

The engine accesses each target URL and converts complex HTML documents into clean Markdown or plain text.

Text length must be controlled during parsing. LLMs generally have context-window limitations. When a page is too long, the system should split the document into multiple chunks. A suitable chunking strategy should retain a certain amount of overlap between adjacent chunks to preserve semantic continuity.

3.3 LLM-Based Intelligent Analysis

This layer serves as the system’s analytical core. The system calls advanced LLM services through APIs, such as high-performance models accessed through the Baidu AI Cloud Qianfan large model platform, to perform detailed analysis.

The analysis process can be divided into two main stages.

Stage 1: Case Identification and Information Extraction

After receiving cleaned web content, the model first determines whether the page contains a valid AI application case. If it does, the model extracts structured information according to a predefined schema.

Prompts should explicitly require JSON output so that the results can be processed programmatically.

Example prompt structure:

You are a senior enterprise AI analyst. Read the following web page and extract the key information.
Requirements:
1. Determine whether this page describes an AI application case study (yes/no).
2. If yes, extract the following fields: industry, application scenario, AI technologies used (such as NLP, computer vision, or predictive analytics), business challenges addressed, and business benefits.
3. Return the result strictly in JSON format without any additional explanation.
Web page content: {content}

Stage 2: Cross-Case Reasoning and Synthesis

After a sufficient number of cases have been collected, the system can combine their summaries and send them to the model for cross-case analysis.

The model can identify industry trends, common patterns in technology adoption, and frequently occurring application scenarios. These findings can then serve as source material for executive-level reports.

3.4 Reporting and Visualization

Once the model has structured the case data, the final step is to convert the results into readable reports and dashboards.

• Individual case-study reports: Use a template engine to populate standardized documents with the extracted JSON data. Typical sections include background, challenges, solution, and results.
• Cross-case analysis reports: Use the model’s text-generation capabilities to summarize multiple cases and produce reports on industry trends and strategic insights.
• Executive dashboards: Import the structured data into a database and use general-purpose visualization tools or cloud-based business intelligence services to build interactive dashboards. These dashboards can show the distribution of AI applications across industries, the proportion of commonly used technologies, and other key indicators that support management decision-making.

4. Data Processing Workflow

A robust workflow orchestration engine is required to coordinate the modules described above. A complete processing pipeline can include the following steps:

1. Input trigger: The user uploads a CSV file or enters a target website domain.
2. Link mapping: For website input, the system calls a crawling service to retrieve site-wide URLs. For CSV input, it reads the URL list directly.
3. Intelligent link filtering: The system calls an LLM to classify candidate URLs and remove pages that are unrelated to AI case studies.
4. Content crawling and cleaning: The system runs the Scrape operation on valid URLs to obtain clean text.
5. In-depth model analysis: The system calls the LLM API to extract and structure relevant information.
6. Report synthesis and rendering: The system aggregates the structured data, generates different types of reports, and updates the dashboards.

The workflow can use an event-driven architecture. After each node completes its task, it publishes a completion event to a message queue, triggering execution of the downstream node.

This design improves throughput and fault tolerance. Individual tasks can be retried when they fail without interrupting the overall workflow.

5. Conclusion

An automated enterprise AI use-case research and analysis system can significantly reduce the workload of analysts and researchers. By automating data collection, it allows them to focus on higher-value business insights.

By combining automated web content extraction, the reasoning capabilities of LLMs, and mature cloud infrastructure, developers can build a scalable and accurate intelligent research platform. As large models continue to evolve, systems of this kind are likely to become increasingly important assistants for enterprise strategy and decision-making.