Building an AI IoT Demo for a Power Plant – A Practical Step By Step Creation and Implementation Guide

For CTOs and technology leaders, the challenge with AI isn’t just if it can deliver value, it’s how to move from an idea to a working solution quickly, without overcommitting resources.

Proof-of-concept projects are the bridge: they validate business cases, expose technical constraints, and build confidence before scaling across the enterprise. But many PoCs stall because they are either too theoretical or too dependent on heavy custom development from day one.

This article presents a different path. We’ll walk through a demo of an AI-powered system for a power plant, developed using an AI-driven workflow.

Instead of starting with prebuilt templates, the builder used AI as a collaborator: researching UI/UX design, generating code, structuring the architecture, and even scripting the deployment to Azure. The outcome is a live application that integrates operational and weather data, provides real-time dashboards, enables AI chat-based recommendations, and makes the AI’s reasoning transparent to end users.

Why should this matter to you? Because the approach demonstrates:

• Speed to validation - a PoC can be developed in weeks, not months, by using AI to handle research and code generation.
• Scalable architecture - modular components (Angular, .NET, Azure Fabric, AI Agents) that can grow from demo to production.
• Enterprise alignment - the design enforces governance, integration consistency, and explainability, these are essential components for regulated industries.
• Reusability - the same methodology can be adapted to predictive maintenance, quality inspection, scheduling, or other operational intelligence use cases.

In the sections ahead, we’ll break down how this demo was created step by step, what challenges were encountered, what capabilities are needed, and how enterprises can reuse this approach to accelerate their own AI journey.

From Idea to Demo: How the Project Started

The demo didn’t follow a template or pre-built framework. Instead, the process unfolded in a series of AI-guided steps:

Step 1 - Research modern UI/UX approaches

AI was first prompted to survey contemporary frontend practices. The insights served as the foundation for the application’s dashboard and user interaction design.

Step 2 - Define prompts as the design brief

The research results were shaped into a structured prompt that outlined the requirements for the application. This prompt became the central reference point for the next stages.

Step 3 - Generate architecture with AI

An AI agent received both the UI brief and the details of the required data (from Azure Fabric). Based on these inputs, it generated the system architecture, including frontend–backend interactions, data flows, and integration points.

Step 4 - Build the application components

The agent produced the Angular dashboard, the AI chat interface, and the .NET backend with business logic and APIs to connect with Azure services.

Step 5 - Automate deployment

To simplify the rollout, AI also generated a scripted deployment to Azure. This reduced manual configuration and allowed the demo to run quickly in a cloud environment.

AI was used both as the analytical engine behind the demo and as a contributor to the engineering process. Each step blended automated output with human oversight, resulting in a working proof-of-concept aligned to the data and infrastructure requirements.

AI Technologies, Technology Stack and Tools

The demo was built on a modular stack, with each component chosen to balance speed of development with scalability in an enterprise setting:

• Frontend - Angular - Angular provided a structured framework for building a responsive dashboard and chat interface. Its component-based architecture made it easier to integrate real-time data visualizations and extend the UI without rewriting large parts of the codebase.
• Backend - .NET (C#) - The backend was developed in .NET, which handled API orchestration, business logic, and secure integration with Azure services. C# offered strong typing and maturity for enterprise-grade applications, while the .NET ecosystem provided direct support for Azure SDKs and authentication flows.
• AI Services - Azure AI Agent with GPT-5 - GPT-5 was embedded as the reasoning engine. The AI agent was designed to work with structured prompts, output strictly in JSON, and expose its “thought process” so that each recommendation could be audited. This made it suitable for scenarios where explainability is as important as accuracy.
• Data Platform - Microsoft Fabric - Microsoft Fabric acted as the primary data layer, ingesting both operational and environmental datasets. Its integration with the .NET backend allowed seamless querying, while its scalability made it ready for real IoT streams in production.
• Deployment - AI-Generated Azure Scripts - Deployment was automated through scripts created by the AI itself. This reduced the dependency on manual configuration and provided consistency across environments. For enterprises, this approach also improves reproducibility and shortens time to deployment. Data centers play an important role in supporting the hosting and operational needs of AI systems, offering the necessary infrastructure for scalable AI and IoT deployments.
• Data Simulation Layer - Since the demo was not connected to live equipment, simulated datasets were used to validate functionality. These simulated inputs mirrored IoT sensor feeds and weather records, ensuring the architecture could handle real-world conditions once connected.

Together, these tools created a full-stack system where the frontend, backend, AI agent, and data platform were tightly integrated, yet modular enough to evolve independently.

Integration with IoT Technologies

The convergence of AI and IoT technologies are changing the energy sector for the better, unlocking new levels of efficiency and intelligence across the entire energy value chain. IoT sensors and devices are deployed throughout energy infrastructure, continuously collecting vast amounts of real-time data on everything from equipment performance to environmental conditions. AI algorithms analyze this data to provide actionable insights, enabling energy companies to optimize energy consumption, predict equipment failures, and enhance grid resilience.

This powerful combination is also fueling the development of smart cities, where AI-powered IoT devices help manage energy usage, optimize traffic flow, and improve overall urban efficiency. In industrial settings, IoT technologies and AI work together to automate processes, enhance efficiency, and reduce operational costs. As the energy industry continues to embrace these innovations, we can expect significant improvements in energy efficiency, reduced carbon emissions, and smarter decision making that benefits both providers and consumers.

Architecture of the Artificial Intelligence Demo

The demo was designed as a layered system where each component plays a distinct role but connects through well-defined contracts. This made it easier to build, test, and later adapt the solution for other operational scenarios.

1. Frontend (Angular Dashboard & Chat Interface)

• Provides real-time visualization of operational and weather data.
• Hosts the chat interface where users can query the AI agent.
• Communicates with the backend through REST APIs.

2. Backend (.NET Services)

• Acts as the control layer between the frontend, AI agent, and data platform.
• Handles authentication, business logic, and API orchestration.
• Normalizes incoming data and enforces JSON-based communication contracts to maintain consistency.

3. AI Agent (Azure AI, GPT-5 )

• Receives structured prompts containing context, queries, and datasets.
• Produces outputs in a strict JSON format that separates user-facing insights from reasoning steps.
• Ensures explainability by exposing its “thought process” alongside recommendations.

4. Data Platform (Azure Fabric)

• Central source for operational and environmental data.
• Supplies historical and real-time feeds for analysis.
• The architecture is designed to sense real world conditions through IoT devices, enabling the system to collect and analyze data from the physical environment.
• In the demo, simulated data streams were used to validate performance.

5. Deployment Layer (Azure Services)

• Infrastructure deployed through AI-generated scripts.
• Uses Azure App Services to host frontend and backend components.
• Scripts enforce environment reproducibility and reduce manual setup time.

This architecture separates concerns while keeping integration tight through JSON contracts and Azure-native services. The modular design means that datasets, prompts, or even the AI engine can be swapped with minimal changes, making the system adaptable for different industries or operational contexts.

Please find below a complete diagram of the AI solution’s architecture.

Architecture Diagram

Legend

• Frontend (Angular): Real-time charts, tables, and the chat UI.
• Backend (.NET): Auth, routing, data shaping, prompt assembly, response validation, and rate limiting.
• Data (Azure Fabric): Operational + weather datasets (historical now; IoT-ready later).
• AI Agent (GPT-5 on Azure): Correlation analysis, explanations, and JSON-only outputs.
• Deployment (Azure App Services + scripted): Reproducible environment setup, versioned rollouts.

Request Flow (User Query → Insight)

1. User asks a question in chat (e.g., “Explain the vibration spike on Generator_1 yesterday.”).
2. Frontend sends the query to .NET API (with user/session context).
3. Backend:

• Pulls the relevant slices from Azure Fabric (timestamps, equipment, weather window).
• Builds a strict JSON prompt per the contract (type/data/text/thoughts required).
• Sends the prompt to the AI Agent.

4. AI Agent returns JSON with:

• type = text|chart|table
• data = referenced points for visualization
• text = user-facing summary with exact timestamps/values
• thoughts = explicit reasoning steps

5. Backend validates schema (JSON only, required fields present) and strips/flags any violations.
6. Frontend renders the result (e.g., a table + narrative) and optionally a “Reasoning” disclosure panel. The system is designed to provide insights to users by delivering actionable information based on the analyzed data.

Data Flow (Ingestion → Analysis → Presentation)

1. Data ingestion (simulated now; IoT-ready): operational metrics + weather into Azure Fabric.
2. Backend queries Fabric for a time-bounded slice aligned to the user’s question.
3. AI Agent performs correlation/sequence analysis over the slice and returns JSON.
4. Frontend displays: live charts, anomaly callouts, and explanation with cited timestamps/values.

Contracts to Keep Components Loosely Coupled

• API: REST over HTTPS, JSON only.
• Prompt/Response (AI contract):

Data Schema:

• weather_data.json: timestamp, temperature_celsius, rainfall_mm, humidity_percentage, wind_speed_kmh
• powerplant_equipment_data.json: timestamp, equipment_id, parameter, value

Prompt Design and Reasoning Transparency

One of the main challenges with operational AI is trust. Users need to see not only what the system recommends, but also how it reached its conclusions. To address this, the demo enforced strict rules for how the AI agent communicates its output. Data analysis is a core function of the AI agent, enabling it to extract insights from large datasets and provide accurate recommendations.

1. JSON-Only Responses - The AI agent was instructed to respond exclusively in JSON. This eliminated formatting inconsistencies and made it easier for the backend to parse and validate responses.

2. Structured Output Contract - Every response had to follow a schema with four key fields

• type: Defines how the result should be presented (narrative, chart, or table).
• data: References to the underlying dataset (timestamps, equipment IDs, values).
• text: A clear, user-facing explanation citing precise numbers and sequences of events.
• thoughts: A transparent log of how the AI reached its conclusion such as calculations, correlations, assumptions.

3. Data Schema Enforcement - Two datasets were consistently used:

• Weather data: timestamp, temperature, rainfall, humidity, wind speed.
• Equipment data: timestamp, equipment ID, parameter, value.

This schema meant the AI agent had a predictable data model to work with, reducing integration errors and making correlations easier to validate.

Example in Practice

When asked “What caused the blackout on March 15, 2025”, the AI didn’t just respond with a summary. It cited exact equipment IDs, timestamps, and values (e.g., “Generator_1 overheating to 145.2°C at 14:30”). In its “thoughts” field, it detailed the records it analyzed, the correlation coefficient it calculated, and the reasoning sequence that led to the conclusion. Natural language processing allows the system to understand and respond to user queries in everyday language, making the AI chat interface intuitive and accessible.

5. Benefits of Transparency

• Builds user trust by showing step-by-step reasoning.
• Simplifies auditing for compliance-heavy industries.
• Helps technical teams debug or validate AI outputs.
• Creates a reusable contract that can be applied to other use cases beyond power plants.

Capabilities of the Demo for Operational Efficiency

The demo showcased how AI can sit at the center of an operational monitoring environment, linking data ingestion, analysis, and user interaction in one system. The system demonstrates advanced ai capabilities such as real-time monitoring, predictive analytics, and automated recommendations.

• Real-time monitoring dashboard - The Angular frontend displayed live operational metrics alongside environmental data such as temperature, rainfall, and wind speed. This provided operators with a consolidated view of conditions that affect equipment performance, improving efficiency in plant operations.
• AI-driven chat interface - Users could ask natural language questions like “Why did Generator_1’s efficiency drop yesterday?”. The AI agent responded with explanations tied directly to data points and correlations, rather than generic summaries.
• Energy optimization and efficiency - The system supports improving energy efficiency by analyzing data from IoT devices to optimize energy consumption, reduce costs, and decrease carbon emissions.
• Transparent analytics and recommendations - Each AI response was accompanied by reasoning details that showed the data examined, the calculations performed, and the assumptions made. This transparency made insights more trustworthy and easier to validate. The system can also enhance safety by identifying potential risks and providing timely alerts.
• Automated data flow - Data moved seamlessly from Azure Fabric (ingestion) → through the .NET backend (orchestration) → into the AI agent (analysis) → and back to the frontend (visualization and recommendations).
• Scalability for IoT integration - While the demo ran on simulated data, its architecture was designed to handle real IoT feeds with minimal changes. The architecture can also be extended to incorporate wearable devices as additional data sources. This makes the system ready to transition from proof-of-concept to enterprise deployment.

Challenges and How They Were Solved

Building the demo surfaced several technical challenges, especially when implementing AI in complex enterprise environments. Each was addressed with a mix of AI support and human expertise:

• Rapid tool evolution - SDKs and libraries for AI services change frequently. To keep the demo working with the latest versions, the builder used AI as a research assistant to identify current best practices before each implementation step.
• Integration complexity - The system connected multiple layers: frontend, backend, data platform, and AI agent. Consistent JSON structures and prompt contracts were enforced so that components could exchange data reliably.
• User transparency - For operators, trust in AI outputs required more than answers. The demo enforced a rule that every AI response included its “thought process,” showing exactly which records were used and what calculations were applied.
• Deployment automation - Setting up cloud environments manually can be slow and error-prone. AI was tasked with generating Azure deployment scripts, which reduced configuration overhead and made it easier to reproduce environments.
• Debugging and refinement - While AI produced much of the code, not all challenges could be solved by AI alone. Expertise in Angular and .NET was essential to resolve errors, refine logic, and ensure the system behaved as intended.

Prerequisites for Enterprises

Before attempting to build a similar system, certain foundations need to be in place. The most important is data. Enterprises need access to well-structured operational and environmental datasets, ideally collected through IoT sensors or integrated from existing plant systems. Without reliable data feeds, the AI cannot perform meaningful analysis.

A second requirement is a robust cloud ecosystem. In this case, Azure was used, with services such as App Services for hosting, Microsoft Fabric for data, and AI Agent capabilities for analysis. Equivalent platforms can be substituted, but the principle remains: the cloud provides the scalability, connectivity, and deployment flexibility that an on-premise setup would struggle to match.

Security and access control must also be considered early. User authentication through Entra ID or a similar identity provider ensures that only authorized users can interact with the system. This keeps the demo aligned with enterprise security standards.

On the human side, a skilled team is required. Even though AI can generate large parts of the code and deployment scripts, expertise in cloud engineering, AI/ML, and application development is necessary to guide, validate, and refine the output.

Finally, two less technical but equally critical elements need to be in place:

• Defined business use cases – a clear understanding of the problems the system should solve.
• Data governance framework – policies that ensure data quality, ownership, and retention.

With these prerequisites addressed, enterprises can approach similar projects with a realistic path from proof-of-concept to scalable solution.

Team Composition for Delivery

A project like this benefits from a multidisciplinary team, with roles that cover both technology and domain knowledge:

• Frontend developer with Angular expertise for dashboards and interaction design.
• Backend developer experienced in .NET and Azure SDKs for APIs and orchestration.
• AI/ML engineer with a strong background in computer science and machine learning to design prompts, manage AI integration, evaluate outputs, and ensure the effective application of machine learning algorithms in energy and IoT scenarios.
• Cloud platform engineer to configure and optimize Azure services.
• QA and test automation specialist to validate system stability and accuracy.
• Domain expert (e.g., energy or manufacturing) to ensure outputs match real operational needs.
• Project manager to coordinate delivery and maintain focus on business outcomes.

Not every role is required at the proof-of-concept or MVP stage. Smaller teams can move quickly, relying on cross-functional skills and AI-assisted development. However, when the goal is to build a full-scale AI solution, the size and composition of the team should reflect the scope, timeline, and available budget. A larger team can accelerate delivery and add robustness, while a smaller one may prioritize agility and cost efficiency.

Lessons Learned and Reusability

The experience of building the demo highlighted practices that can be applied well beyond a single proof-of-concept.

AI as a development partner

Using AI for research, code generation, and deployment scripting accelerated delivery. While human oversight was always needed, AI reduced the time spent on boilerplate work and enabled faster iteration.

Modular architecture

Separating frontend, backend, AI agent, and data platform through clear contracts meant that any one component could be swapped or extended without reworking the entire system. This made the solution easier to adapt to different datasets and business cases.

Transparent reasoning

Enforcing an “AI thought process” increased trust and usability. The same approach can be applied to other enterprise AI applications, where explainability is often as important as accuracy.

Reusable assets

The codebase, integration patterns, and deployment scripts created for this demo are not one-offs. They can be repurposed by changing datasets, prompts, or models, making them a foundation for future projects.

Taken together, these lessons show how a single PoC can evolve into a reusable framework for operational AI, cutting down on development effort in subsequent projects.

Potential Extensions Beyond Power Plants

While the demo focused on power plant operations, the same approach can be applied across manufacturing and industrial settings where data-driven decision-making is essential. By swapping datasets and adjusting prompts, the methodology supports a range of use cases:

• Predictive maintenance - Monitoring sensor data to detect early signs of equipment wear and prevent unplanned downtime.
• AI-based quality inspection - Using vision systems or sensor data to identify defects during production.
• Scheduling and resource optimization - Automating workforce and machine scheduling based on operational data and demand forecasts.
• Incident detection and reporting - Real-time identification of anomalies on the shop floor, with automated logging and escalation.
• Adaptive workforce coaching - Tracking operator skills and performance data to provide tailored guidance and training.

Each of these examples builds on the same core principles demonstrated in the power plant demo: structured data ingestion, AI-driven analysis, transparent reasoning, and modular system design.

Business Value for Enterprises

The demo reflects how AI can address several recurring challenges in industrial and manufacturing environments:

• Unplanned downtime and maintenance costs - Equipment failures are expensive and often preventable. By correlating environmental conditions with equipment performance, AI can flag risks early and reduce unplanned outages.
• Rising operational costs - Inefficient scheduling, resource use, or maintenance practices drive costs upward. AI-powered monitoring and recommendations help identify where resources can be allocated more effectively.
• Slow response to anomalies - Without real-time analysis, small issues can escalate before they are noticed. An AI-driven dashboard and chat interface surface anomalies quickly and provide explanations operators can act on.
• Lack of transparency in decision-making - Traditional analytics often feel like a “black box.” In this demo, every AI output includes the reasoning behind it, helping users trust and act on the insights.
• High entry cost for AI initiatives - Building AI solutions from scratch is resource-intensive. The approach shown here reduces setup effort through AI-assisted development and reusable integration patterns, making it easier to start with a smaller investment.

Conclusion

What this demo shows is that building an AI system for a complex environment like a power plant doesn’t have to start with a massive investment or a long implementation cycle. With the right data, cloud setup, and a focused team, AI can already take on tasks that are difficult to manage with traditional systems: spotting risks earlier, cutting downtime, and making recommendations that can be traced back to the underlying data.

The value isn’t only in the demo itself but in the method. Using AI as part of the development process speeds up delivery, while the modular design makes it easier to reuse the same building blocks in other areas, whether that’s predictive maintenance, quality inspection, or real-time incident reporting.

For organizations weighing where to start with AI, we can create a proof-of-concept like this that provides a clear way to test ideas against real business problems, show tangible outcomes, and build confidence before moving to full-scale deployment.

Schedule a call with our team to explore where AI can bring the highest ROI in your business. We’ll apply our AI Framework to build a tailored proof-of-concept and, when ready, scale it into a full solution.