AI or Knockout: The modular AI tech stack as the key to successful AI in manufacturing

Artificial Intelligence (AI) is no longer just a "nice-to-have"; it is becoming a decisive competitive factor in almost all industries. Whether in large corporations or SMEs, the use of AI methods, particularly with the help of generative techniques (e.g., Large Language Models - LLMs), promises enormous productivity gains, resilience, cost efficiency, and innovative capacity. At the same time, companies face the challenge of finding the right strategies and technical concepts to integrate AI profitably and securely.

In this article, we present the AI tech stack and address the following questions:

• What manufacturing companies can learn from software companies to lay the foundations for AI in manufacturing correctly from the start.

• What a modular tech stack for AI is – and how it helps manufacturers implement AI more agilely, efficiently, and innovatively.

• How the ENLYZE Manufacturing Data Platform supports companies in this transformation.

• What seamless integration of machine data and AI models can look like in practice.

  
  

What exactly is behind the term tech stack for AI?

It refers to the overall technical solution – the combination of infrastructures, data processing, AI models, and user interfaces. This is necessary to successfully implement digitalization and AI solutions in a company. The better these components are coordinated, the more efficiently and effectively machine data can be used to increase productivity in manufacturing and make AI-supported decisions.

Modular Tech Stack: What Producers Can Learn from Software Companies

The primary value creation in manufacturing traditionally lies in the production of physical goods. The focus has long been on stable supply chains, optimally utilized production, high quality, and delivery reliability.

Digital tools like Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), or Quality Assurance (QA) systems already support these goals, but they are often monolithic and complicated to connect with other corporate programs. The problem here is high complexity, long implementation times, and limited flexibility.

To keep pace with the innovation speed of AI, it is worthwhile to look at young software companies where value creation takes place entirely digitally and the use of new software for product optimization, such as through a modular tech stack for AI, is already commonplace.

Technology Stack Significance: What is a Modular Tech Stack?

In the software industry, one often speaks of a company's tech stack. This refers to the entirety of all technologies, tools, and infrastructures that a company uses to operate digital products. In practice, a technology stack can usually be imagined as several layered components:

• Infrastructure and Hosting: e.g., AWS, Microsoft Azure, Google Cloud

• Data and Processing Layer: e.g., MySQL, PostgreSQL, MongoDB

• Middleware

  • Security (Authentication, Access Control, Security Solutions)

  • Monitoring (e.g., Production Monitoring with Grafana)

  • Logging (e.g., Error logs for analysis)

• Frontend and Interaction: Dashboards, website interfaces, chat interfaces.

The layers communicate with each other through standardized interfaces. This makes it possible to flexibly exchange or supplement technologies as needed, such as when switching from AWS to Azure or adding a new monitoring tool like the integration with Power BI. This allows for relatively low effort in responding to technological changes.

AI Tech Stack in Manufacturing: Levels & Structure Explained

For companies with a physical value chain, the tech stack can be divided into four levels:

1. Data Layer
Here, machine, production, and quality data are collected in normalized and retrievable form - the foundation of a functioning tech stack for AI.

2. API Layer (Data Access)
Access to this data occurs through clearly documented interfaces – without directly accessing databases. This keeps systems secure and flexibly integrable.

3. AI Layer (Models and Orchestration)
Central AI models use techniques such as Retrieval-Augmented Methods or Prompt Techniques to query and process relevant data. These are also referred to as agents or agentic AI models.

4. Application and User Interface Level
Various frontends, integrations (e.g., Excel, Power BI, production control systems), or chat interfaces through which employees can interact with the systems in natural language or graphically.

Access Management is essential in any case: Who is allowed to access which data? Each layer should clearly define which parts of the system are reachable and what rights are needed for that.

AI Tech Stack and Machine Data: How ENLYZE Supports

The ENLYZE Manufacturing Data Platform forms the backbone of a modern technology stack in the manufacturing industry. Before an AI model can make informed analyses, the generated machine data must be reliably collected, enriched with contextual information, and structured for retrieval.

Exactly here our manufacturing data platform comes into play: It continuously combines recorded time series data - such as temperatures, pressures, speeds, or energy consumption - with important production context data like order information, production times, downtimes, and parameter trends.

Reliable Collection and Linking of Machine Data

As a first step, the relevant machine parameters must be continuously recorded. Machine controls, sensor data, or energy meters provide different process values.

Different process values from machine controls (in kg/h), sensor data (in bar), and energy meters (in kW).

In the second step, contextual information is added to describe what happened on the machine at what time. What product was manufactured? Was there any downtime – and if so, why? Which shift was active? Only when all data sources are consolidated in a consistent AI tech stack do reliable key figures for AI applications emerge.

This linking in the ENLYZE Manufacturing Data Platform enables the calculation of precise key figures, such as:

• Produced quantity per order: From the throughput over the duration of an order, the amount produced can be calculated.

• Energy consumption per order: The energy consumption for an order can be calculated from the power consumption.

• Specific energy consumption / e.g., in kWh/kg): From energy consumption and quantity, the specific energy consumption can be determined.

Overview per facility (colorfully displayed above): Kilograms per hour and the corresponding energy consumption.

Structured and Accessible Data as the Key to AI

A central component of the ENLYZE Manufacturing Data Platform is the provision of processed machine data via a standardized API. This allows internal web applications, visualization tools (e.g., Grafana or Power BI), and especially AI models to always access consistent, high-quality, and up-to-date data – such as production quantities, energy consumption, or machine downtimes – without direct access to the database. Read more about the API documentation of the manufacturing data platform.

An example of data exchange:

Through the endpoint get_production_runs, specific production orders for a facility can be retrieved. In addition to raw data, already aggregated key figures (e.g., produced quantities and energy consumption) are provided, reducing integration effort and minimizing sources of error.

By storing, processing, and providing data via API, two essential parts of the AI tech stack are covered: a solid data layer and a clearly defined data access.

Why LLMs Need Structured Data

Large language models like GPT or Claude are specialized in understanding and formulating text – but not in evaluating raw data. Therefore, they benefit enormously from clearly structured, pre-processed data:

• Limited context windows: LLMs (Large Language Models) can only process a limited amount of text or tokens at the same time. When extensive raw data is fed as text, the context window can fill up quickly. Aggregated values save storage space and keep AI responses precise.

• Efficient data processing: No language understanding is required for mathematical operations. Database systems like TimescaleDB are optimized for processing large amounts of data and provide fast, reliable results. This allows the LLM to focus on interpretation and formulation.

• Reliability and reduced risk: When the LLM receives pre-calculated key figures, the risk of misinterpretation and “hallucinations” decreases. Certain values like energy consumption or the number of downtimes should be deterministic and not vary based on queries.

Practical Example: Analyzing Energy Consumption per Order

Imagine an employee asks the following question: “What was the specific energy consumption for product X in the last month on machine Y?”

Here's how the response occurs automatically:

• The LLM interprets the question and identifies which key figure is needed.

• Through the ENLYZE API, the specific energy consumption for machine Y and product X is queried.

• The ENLYZE Manufacturing Data Platform calculates the key figure value – for example: 1.8 kWh/kg.

• The LLM then formulates a comprehensible answer, such as: “The machine consumed an average of 1.8 kWh per kilogram produced.” Or it can create a report directly for these data.

Example of a worker interacting with an LLM and the ENLYZE Manufacturing Data Platform to answer a question about the energy consumption of a specific product produced on a specific machine.

The advantage is that the LLM does not need to calculate or search data independently. It focuses entirely on communication, as the data logic of the AI tech stack takes over.

Why a Tech Stack for AI in Manufacturing is Crucial Now

Artificial intelligence is no longer a future topic, but a strategic competitive factor. Companies that adopt data-driven processes early on increase their efficiency, compensate for the shortage of skilled labor, and assert themselves in the market in the long term. However, merely deploying an AI model is often not sufficient.

The Technical Foundation: A Modular AI Tech Stack

Crucial for a successful AI Industry 4.0 is the technological foundation. A flexible, scalable tech stack ensures that data is reliably collected, processed, and made usable for AI.

The ENLYZE Manufacturing Data Platform forms the core of a modern AI tech stack. It enables a continuous flow of data through:

• structured collection of machine data.

• integration of contextual information.

• provision of data through standardized APIs.

This creates a continuous flow of data that enables not only classic AI analyses but also the seamless integration of LLMs like GPT.

Get consulting now and find out how your company can become AI-future-proof with the right manufacturing data platform.

FAQ about the AI Tech Stack in Manufacturing

What prerequisites are needed to implement an AI tech stack in a company?

fundamentally, you need production data that is captured digitally. With a complete solution like ENLYZE, which offers connectivity and a data platform in one, you can easily connect existing systems and gradually build a data foundation suitable for AI.

How does a modular AI tech stack differ from traditional IT systems?

A modular tech stack is built flexibly and allows individual components such as databases, AI models, or user interfaces to be quickly exchanged or expanded as needed – in contrast to rigid monolithic systems that often involve hosting the entire solution or parts of it on the company’s own servers.

What role do APIs play in the AI tech stack?

APIs (Application Programming Interfaces) are the central link between data sources, AI models, and user interfaces. They enable secure, structured, and flexible access to data without requiring direct database queries, which can pose security vulnerabilities.