AI Infrastructure Solutions for Industry Digital Transformation

01 · Internship Overview

I worked as a Product Marketing Intern within the AI Computing Product Line of a leading ICT technology company.

The internship focused on AI infrastructure, industry digital-transformation solutions, and product marketing. My work covered four primary areas:

1. An enterprise AI Agent platform solution for a telecommunications marketing scenario;

2. An AI healthcare solution presentation for industry customers;

3. A simulated computing-product bidding project for the financial industry;

4. An industry research report on AI-powered vocational education.

Rather than implementing the underlying code, I focused on translating customer requirements and business scenarios into solution architectures, product configurations, computing-resource requirements, customer value propositions, and presentation materials.

These projects were solution exercises and proof-of-concept demonstrations conducted during the internship. They do not represent production deployments or commercially launched customer projects.

02 · Understanding Product Marketing

Product marketing connects customer requirements, industry scenarios, technical capabilities, product portfolios, and commercial solutions.

The role requires more than an understanding of servers, AI accelerators, inference platforms, and software ecosystems. It must also answer several important questions:

• What business problem does the customer need to solve?

• Which products and technical capabilities should be combined into a complete solution?

• How much computing, storage, and supporting infrastructure does the customer require?

• What differentiates the proposed solution from alternative options?

• How should technical capabilities be communicated in language the customer can understand?

• How should product capabilities, delivery costs, and project returns be balanced?

Through industry research, scenario analysis, product learning, resource planning, solution presentations, and simulated bidding, I developed a product-marketing mindset that connects technical specifications with customer value.

03 · Enterprise AI Agent Platform Solution

Project Background

The solution was designed for the marketing team of a provincial telecommunications operator.

When marketing employees visited customers, they often needed to connect to the corporate intranet and sign in to multiple internal systems before they could retrieve customer information, review service status, or complete business operations.

The existing workflow presented several challenges:

• Internal systems were fragmented across different entry points;

• Each system had a different operating process;

• Intranet access and repeated sign-ins were inconvenient during customer visits;

• Marketing employees needed to remember the functions and locations of multiple systems;

• Even simple inquiries could require several cross-system operations;

• Data and operational capabilities lacked a unified user interface.

The project proposed an enterprise AI Agent platform to reduce the operational complexity of accessing internal business systems.

Product Objective

The solution aimed to establish a unified enterprise Agent interface.

Instead of searching for and signing in to multiple systems, employees could describe their needs in natural language. Within the user’s authorized scope, the Agent would identify the task, invoke the appropriate system APIs, and return the requested information or execute the relevant action.

For example, an employee could ask:

Show me the customers assigned to my account.

The Agent would interpret the request, invoke the relevant customer-management API, and organize the results into a consistent and readable format.

The core objective was not to build another question-answering chatbot. It was to:

Connect the APIs of fragmented business systems so that an Agent could independently select and invoke the appropriate capabilities based on a user’s natural-language request.

04 · Users and Business Scenarios

The solution primarily served marketing and service employees across the operator’s consumer, household, and enterprise business areas.

Potential application scenarios included:

• Retrieving consumer or enterprise customer information;

• Reviewing current services and account status;

• Finding product and service-package information;

• Preparing for customer visits and sales conversations;

• Searching internal policies and business knowledge;

• Invoking authorized business-system operations;

• Consolidating information from multiple internal systems.

A unified Agent foundation allowed different business scenarios to reuse common capabilities such as intent recognition, knowledge retrieval, tool invocation, permission management, and workflow orchestration.

This reduced the need for individual departments to build separate Agent systems from the beginning.

05 · Solution Architecture

The enterprise Agent platform was designed around three principles:

Unified access, unified capabilities, and unified computing infrastructure.

The complete solution was divided into five layers.

1. User Interaction Layer

Users interacted with the Agent through text or voice to submit inquiry, analysis, or operational requests.

This layer was responsible for:

• Receiving natural-language input;

• Presenting task-execution progress;

• Returning query results;

• Requesting user confirmation before high-risk operations.

2. Agent Capability Layer

The Agent capability layer interpreted user requirements and planned task execution.

Its principal capabilities included:

• Intent recognition;

• Parameter extraction;

• Knowledge retrieval;

• Tool selection;

• Workflow orchestration;

• Context management;

• Response generation.

3. Tool and API Layer

The platform encapsulated the capabilities of different internal systems as standardized tools, allowing the Agent to invoke the appropriate API for each task.

Representative tools included:

• Customer-information retrieval;

• Product and service lookup;

• Business-status inquiry;

• Internal knowledge retrieval;

• Work-order and business-process invocation;

• Permission validation and operational auditing.

4. Data and Application Layer

This layer connected the operator’s existing customer-management, marketing, knowledge-base, and business-support systems.

The platform did not replace existing applications. Instead, it integrated their capabilities through standardized interfaces and provided employees with a consistent interaction layer.

5. AI Computing Infrastructure Layer

The underlying infrastructure used a domestic AI computing platform to support model inference and Agent execution.

The solution translated model usage, concurrency requirements, response-time targets, database requirements, APIs, and microservices into corresponding computing and infrastructure resources.

This provided the basis for product configuration and computing-resource planning.

06 · AI Computing and Product Mapping

An enterprise AI solution consists of more than hardware. It includes AI processors, heterogeneous computing software, development tools, inference platforms, and industry applications.

During the internship, I studied and organized the following areas:

• AI processors and inference accelerator cards;

• AI inference servers and computing-resource configurations;

• Heterogeneous computing software architecture;

• Industry-enablement software;

• Model inference and application deployment;

• Database, API, and microservice requirements;

• The impact of concurrency, response time, and model size on computing demand.

Within a solution, business requirements must be progressively translated into infrastructure requirements:

Business scenario → User scale → Request frequency → Concurrency → Model size → Inference resources → Server and supporting-product configuration

Compared with presenting hardware specifications alone, this method provides a clearer explanation of how product configurations support customer business requirements.

07 · My Responsibilities

For the enterprise Agent platform project, I focused on overall solution design rather than low-level software implementation.

My responsibilities included:

• Researching the field-work scenarios of telecommunications marketing employees;

• Identifying the problems caused by multiple system logins and cross-system operations;

• Defining the positioning of the unified enterprise Agent platform;

• Designing the natural-language entry, intent-understanding, and API-invocation flow;

• Organizing the core capability modules of the Agent platform;

• Identifying opportunities across different telecommunications business scenarios;

• Mapping business requirements to AI inference resources;

• Planning supporting database, API, and microservice resources;

• Contributing to the platform solution and demonstration framework;

• Translating technical capabilities into customer-oriented value propositions.

The project produced a solution prototype and demonstration framework to validate the feasibility of using a unified Agent foundation across multiple business scenarios.

08 · Solution Value

The enterprise Agent platform created value in three primary areas.

Reduced System Complexity

Employees no longer needed to remember the entry points and operating procedures of multiple business systems. They could describe their requirements directly in natural language.

Improved Operational Efficiency

The Agent could invoke different APIs based on user intent, reducing repeated operations and cross-system information searches.

Reusable Agent Capabilities

Intent recognition, knowledge retrieval, tool invocation, permission management, and workflow orchestration could be reused across multiple business scenarios.

This reduced the cost of building separate Agent applications for individual departments.

09 · AI Healthcare Solution Presentation

During the internship, I independently completed a presentation exercise on AI-powered healthcare solutions.

The purpose was not to design a single healthcare product. Instead, the exercise focused on organizing computing products, AI capabilities, and industry requirements into a solution that customers could understand.

The presentation followed the structure:

Industry challenges → AI capabilities → Solution architecture → Application scenarios → Customer value

The scenarios discussed included:

• Medical-image-assisted analysis;

• Medical knowledge assistants;

• Medical-record processing;

• Intelligent hospital customer service;

• Healthcare data analytics;

• AI model training and inference.

Through this exercise, I strengthened my ability to:

• Translate technical language into business language;

• Adjust solution narratives for different customer roles;

• Identify the customer value most relevant to an industry;

• Deliver a structured solution presentation;

• Respond to questions about computing resources, cost, and deployment.

10 · Financial-Industry Bidding Simulation

I participated in a simulated bidding exercise for a computing-product solution designed for a financial institution.

The exercise required the team to evaluate multiple domestic computing-product options while considering:

• Business scenarios;

• Product performance;

• System configuration;

• Project budget;

• Procurement cost;

• Future scalability;

• Competitive differentiation;

• Return on investment.

My responsibilities included:

• Analyzing customer requirements and evaluation priorities;

• Comparing the strengths and limitations of different computing solutions;

• Contributing to differentiated product configurations;

• Discussing pricing and commercial strategies;

• Evaluating procurement costs and long-term benefits from the customer’s perspective;

• Using ROI analysis to communicate solution value;

• Participating in the simulated proposal presentation and defense.

The exercise demonstrated that product marketing is not about proposing the highest possible configuration. It requires identifying the most appropriate balance among performance, budget, delivery, and long-term value.

11 · AI-Powered Vocational Education Research

I independently prepared an industry insight report on the application of artificial intelligence in vocational education.

The report examined industry challenges, customer roles, application scenarios, product capabilities, and commercial opportunities.

It focused on five directions:

1. AI teaching assistants;

2. Intelligent course and content generation;

3. Personalized learning and competency assessment;

4. Virtual training and practical skill development;

5. Education management and employment services.

The report analyzed the needs of schools, teachers, students, education authorities, and employers. It also examined how AI computing infrastructure could support these applications.

This work helped me establish a research framework that connects industry trends with application scenarios, product opportunities, and solution-entry strategies.

12 · Competitive and Industry Research

During solution development, I participated in research on enterprise Agent platforms and AI computing products.

The research focused on several questions:

• How do competing products build unified Agent platforms?

• How do Agents connect to internal enterprise systems?

• How are knowledge retrieval, tool invocation, and workflows combined?

• How does the platform manage user permissions and operational auditing?

• How are model requirements mapped to computing resources?

• What differentiated value can a domestic AI computing ecosystem provide?

This research reinforced my understanding that an enterprise Agent platform is not simply a model-access interface. It must connect enterprise data, business systems, permission structures, and computing infrastructure.

13 · Primary Deliverables

The main deliverables produced during the internship included:

• An enterprise Agent platform solution for a telecommunications marketing scenario;

• An analysis of consumer, household, and enterprise telecommunications scenarios;

• An Agent platform capability and solution architecture;

• Learning materials covering AI inference accelerators and computing products;

• A mapping between business requirements and computing resources;

• An AI healthcare solution presentation;

• A simulated financial-industry bidding strategy and proposal;

• An industry insight report on AI-powered vocational education;

• An Agent platform prototype and demonstration framework.

These deliverables were developed for internship exercises, internal simulations, and capability validation. They do not represent formal production deliveries or commercial deployments.

14 · Key Learnings

This experience helped me develop a structured understanding of how industry requirements are translated into computing-product solutions.

My most important insight was that product marketing is not simply the presentation of product specifications. It requires completing the following transformation:

Industry problem → Business scenario → Technical capability → Product portfolio → Customer value → Commercial solution

I also learned that an enterprise AI solution cannot focus only on model capabilities. A deliverable solution must also consider:

• Computing resources;

• Data and knowledge bases;

• APIs and business systems;

• Permissions and security;

• Deployment methods;

• Cost and return on investment;

• Operations and future scalability.

The internship strengthened my capabilities in industry research, solution design, product configuration, customer communication, and solution presentation.