AI Engineering Intelligence for Smarter Product Development
Better decisions require trusted information
Engineering Knowledge Is Everywhere—But Rarely Where You Need It
Engineering teams already have the information needed to make better decisions, but more often than not they spend too much time searching for it. Product knowledge is scattered across requirements, documents, CAD models, BOMs, and multiple business systems. As data volumes grow, finding the right information becomes increasingly difficult—and traditional AI tools often lack the engineering context to deliver reliable answers.
The challenge isn't collecting more data. It's making information available in the right context, exactly when your teams need it.
Engineering Knowledge Is Everywhere—But Rarely Where You Need It
Engineering teams already have the information needed to make better decisions, but more often than not they spend too much time searching for it. Product knowledge is scattered across requirements, documents, CAD models, BOMs, and multiple business systems. As data volumes grow, finding the right information becomes increasingly difficult—and traditional AI tools often lack the engineering context to deliver reliable answers.
The challenge isn't collecting more data. It's making information available in the right context, exactly when your teams need it.
Engineering teams already have the information needed to make better decisions, but more often than not they spend too much time searching for it. Product knowledge is scattered across requirements, documents, CAD models, BOMs, and multiple business systems. As data volumes grow, finding the right information becomes increasingly difficult—and traditional AI tools often lack the engineering context to deliver reliable answers.
The challenge isn't collecting more data. It's making information available in the right context, exactly when your teams need it.
Imagine Every Engineer Having Instant Access to the Right Knowledge
Imagine an AI assistant that understands product structures, technical documents, CAD models, requirements, and engineering relationships—not as isolated data, but as part of one connected Digital Thread. Instead of searching multiple systems, comparing documents, or relying on tribal knowledge, your teams can instantly find what they need, make better decisions, and focus on developing innovative products.
In other words, AI only delivers value when it understands your business.
Imagine Every Engineer Having Instant Access to the Right Knowledge
Imagine an AI assistant that understands product structures, technical documents, CAD models, requirements, and engineering relationships—not as isolated data, but as part of one connected Digital Thread. Instead of searching multiple systems, comparing documents, or relying on tribal knowledge, your teams can instantly find what they need, make better decisions, and focus on developing innovative products.
In other words, AI only delivers value when it understands your business.
Imagine an AI assistant that understands product structures, technical documents, CAD models, requirements, and engineering relationships—not as isolated data, but as part of one connected Digital Thread. Instead of searching multiple systems, comparing documents, or relying on tribal knowledge, your teams can instantly find what they need, make better decisions, and focus on developing innovative products.
In other words, AI only delivers value when it understands your business.
The CONTACT Approach: Engineering Intelligence Built into Your Enterprise Architecture
Whilst most AI solutions connect to individual applications, solve isolated use cases, and often lack the engineering context needed to deliver reliable results, Fourier AI is different. Built natively into the CONTACT Elements platform, it gives you AI that not only understands your products, your processes, and the relationships across your engineering data, but scales across your entire product lifecycle.
As such, Fourier AI helps your teams find answers faster, solve problems more efficiently, make more informed decisions, and put existing knowledge to work.
The CONTACT Approach: Engineering Intelligence Built into Your Enterprise Architecture
Whilst most AI solutions connect to individual applications, solve isolated use cases, and often lack the engineering context needed to deliver reliable results, Fourier AI is different. Built natively into the CONTACT Elements platform, it gives you AI that not only understands your products, your processes, and the relationships across your engineering data, but scales across your entire product lifecycle.
As such, Fourier AI helps your teams find answers faster, solve problems more efficiently, make more informed decisions, and put existing knowledge to work.
Whilst most AI solutions connect to individual applications, solve isolated use cases, and often lack the engineering context needed to deliver reliable results, Fourier AI is different. Built natively into the CONTACT Elements platform, it gives you AI that not only understands your products, your processes, and the relationships across your engineering data, but scales across your entire product lifecycle.
As such, Fourier AI helps your teams find answers faster, solve problems more efficiently, make more informed decisions, and put existing knowledge to work.
This Allows Your Teams to:
Give engineers instant access to product history, drawings, specifications, and technical documentation, all grounded in your engineering information.
Search existing components using text, images, or 3D CAD geometry to increase reuse and reduce engineering effort.
Analyze production data to uncover bottlenecks, detect anomalies, and improve operational performance before small issues become costly problems.
Translate technical documentation while preserving your organization's engineering terminology and product language.
Give field service teams instant access to asset history, technical documentation, and real-time asset context to improve first-time fix rates.
"Based on its ability to embed AI into governed lifecycle workflows, support federated architectures, and protect sensitive product data, CIMdata recommends that manufacturers seeking to strengthen their digital threads evaluate CONTACT Software’s Fourier AI."
CIMdata, Inc. about CONTACT Fourier AI, March 2026
AI That Fits the Way You Work
Manufacturers shouldn't have to spend months building AI infrastructure before seeing results. Fourier AI works with your existing engineering environment, so you can focus on applying AI where it creates the greatest business value.
Build on your existing investment
Apply AI to the engineering and operational systems you already use
Protect your engineering data
Your proprietary data remains isolated and is never used to train public AI models
Scale as your business evolves
Start with one use case and expand as new opportunities emerge
AI in Test and Requirements Management: Proven in Real Engineering Work
12 minutes saved per test case, delivering high five-figure annual savings
Our customer, an automotive supplier with more than 4,000 employees worldwide, wants to make repetitive engineering processes more efficient and reliable. These include the analysis of stakeholder requirements and the verification of developed systems using test cases (around 5,000 per year). For both processes, engineers needed about 10 to 15 minutes in each instance. Despite global standards, the quality of the results depended on the expertise of individual experts.
Our AI team worked closely with the customer's subject matter experts to define what AI could achieve in both cases. An analysis of the existing data in CONTACT Elements led to the identification of two use cases that quickly deliver noticable improvements. Corresponding prototypes, based on an LLM, were refined through prompt engineering in multiple iteration cycles. This resulted in two operational AI assistants in a short period of time.
With the AI assistants, our customer can reduce manual efforts and ensure consistently high quality. The AI systems reliably evaluate stakeholder requirements and, based on these, generate test cases, including acceptance criteria and semantic links. Employees only need to review and approve the results. They save up to twelve minutes per test case. Due to the large number of requirements, this results in annual savings in the high five-figure range. The quality of the results is consistently high, regardless of the experience of the personnel involved.
AI Knowledge That Moves You Forward
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On-Demand Industry Talk: The AI Reality Check for Complex Machinery Development
If valuable AI projects are still struggling with messy data, delivering unreliable insights, or just adding another layer of complexity to your complex machinery operations, it's time for a reality check. This Industry Talk cuts through the hype, showing you what it actually takes to make AI deliver real results, not just headaches, in your engineering environment.
On-Demand Industry Talk: The AI Reality Check for Complex Machinery Development
If valuable AI projects are still struggling with messy data, delivering unreliable insights, or just adding another layer of complexity to your complex machinery operations, it's time for a reality check. This Industry Talk cuts through the hype, showing you what it actually takes to make AI deliver real results, not just headaches, in your engineering environment.
If valuable AI projects are still struggling with messy data, delivering unreliable insights, or just adding another layer of complexity to your complex machinery operations, it's time for a reality check. This Industry Talk cuts through the hype, showing you what it actually takes to make AI deliver real results, not just headaches, in your engineering environment.
