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Stories, papers and videos on engineering intelligence — from our team and the engineers we work with.

A single orange keycap standing out in a grid of identical white keycaps, representing AI governance bringing order to scattered AI experiments

Blog · Jul 02, 2026 · Alexander Matthey

There is no AI strategy without a data strategy: an AI governance framework for automotive OEMs

Walk into almost any automotive OEM today and you will find the same picture. Marketing has its AI vendors. Manufacturing has others. R&D, sales, and aftersales each have their own. Every team is experimenting, ...

Blog · Jul 02, 2026 · SPREAD Team

The Digital Product Twin: A Practical Guide for Engineering Teams

The term "digital twin" gets used for everything from a 3D model to a physics simulation, which is why it so often disappoints. In engineering, the version that changes how teams work is more specific. A digital product ...

Blog · Jul 02, 2026 · SPREAD Team

AI in Systems Engineering: A Practical Guide for Complex Product Teams

Every engineering organization is being told to "use more AI." Few can say what that means for a discipline as unforgiving as systems engineering. AI in systems engineering is the use of artificial intelligence, ...

Blog · Jul 02, 2026 · SPREAD Team

Engineering Knowledge Graphs: How Connected Data Powers AI in Engineering

Every engineering organization has the data to answer its hardest questions. What it lacks is a way to connect that data. An engineering knowledge graph is a connected, semantic model of a product that links ...

Blog · Jul 01, 2026 · SPREAD Team

Engineering Intelligence: A Practical Guide to the New Engineering Stack

For decades, engineering ran on documents and disconnected tools, and it worked because products were simpler and slower. Neither is true anymore. Engineering intelligence is the capability to turn fragmented ...

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There is no AI strategy without a data strategy: an AI governance framework for automotive OEMs

The Digital Product Twin: A Practical Guide for Engineering Teams

AI in Systems Engineering: A Practical Guide for Complex Product Teams

Engineering Knowledge Graphs: How Connected Data Powers AI in Engineering

Engineering Intelligence: A Practical Guide to the New Engineering Stack

Software-Defined Vehicles: An Engineering Guide to Building SDVs Without the Chaos

AI can answer your engineering question. The interface shows you what happened.

Why SDVs Demand a Domain-Specific Knowledge Graph

Former Stellantis Chief Software Officer Yves Bonnefont Joins SPREAD’s Advisory Board

Beyond accuracy: Designing AI features engineers can trust

SPREAD AI Raises $30M Series B to Propel Software-Defined Defense, Vehicles and Machines from Concept to Mission

SPREAD joins the AWS Partner Network and launches on AWS Marketplace to scale Engineering Intelligence

The case for friction in AI UX: why "Accept all" is the wrong pattern

Scaling Industrial AI from Pilots to Value: Target Architecture

Requirements Management in the Age of AI: Turning Past Projects into Tender Intelligence

From AI Hype to Real Impact, How Enterprises Turn Artificial Intelligence into Measurable Business Value

Zero-Surprise Development in the Era of Software-Defined Products

Why Software-Defined Products Broke Engineering Intuition

Unifying icons at scale: lessons from a growing engineering tool

2025: The year SPREAD became a preferred software partner for Industrial AI in Engineering AND BEYOND.

From many shades to one family: the design story behind our colors update

Integrating AI into product workflows: Insights from Future Product Days 2025

Why Speed Will Define Europe's Defense Advantage

CUBE 2025: Engineering’s Next Era Begins with AI-Ready Teams

Inside the Industrial AI Summit 2025: What Europe’s Leaders Agreed Must Happen Next

SPREAD completes SOC 2 Type II Examination

A product that works isn’t enough: why thoughtful UX defines customer happiness

Engineering Intelligence, Unlocked. The SPREAD AI Platform Explained

The Next Generation of Engineering Intelligence

The Engineering Intelligence Index

The Ontology Shortcut That Costs You Tomorrow: Why Custom Schemas Create New Data Silos

Life at SPREAD: Becoming a Product Manager – How I moved from UX Research to Product, almost over night

Read faster, decide faster: why sentence case delivers clarity in complex engineering software

What is Engineering Intelligence?

One Platform, one experience: Introducing SPREAD’s new sidebar

Closing the Feedback Loop: What Europe Can Learn from China’s Software-Driven Auto Strategy

Digitalization of Complex Systems: Knowledge Graphs and LLMs in Systems Engineering

SPREAD completes SOC 2 Type I Examination

AI in PLM: How Intelligent Product Lifecycle Management (iPLM) Is Transforming Systems Engineering

Industrial AI in Discrete Manufacturing: Unlocking Higher FPY with Engineering Context

Why Software-Defined Vehicle Projects Stall and How Functional Control Restores Confidence

Product Twin Across the Product Lifecycle: Turning Product Data into Operational Decisions

AI in Systems Engineering: Why Most Pilots Fail and What Top OEMs Do Instead

Purpose-built AI for Engineering Excellence

From Reactive Service to Strategic Uptime: Refining Commercial Vehicles Aftermarket for Complexity

The Cost of Complexity: Why Commercial Vehicle R&D Needs Architecture Transparency Now

Managing Requirement Chaos & Variant Complexity With Digital Product Twins

Software-Defined Defense: Engineering Intelligence for Europe’s New Era of Defense

Agentic Engineering Intelligence: Transparency & Efficiency through AI

Agentic Engineering Intelligence: Transparency & Efficiency through AI

SPREAD joins the EU AI Champions Initiative: Shaping the future of Engineering Intelligence in Europe

SPREAD's Technology: How We Build Trusted, Scalable Engineering AI on Product Data and Semantics

Closed-Loop Manufacturing: Can Digital Twins enhance innovation and efficiency in Production?

French National Railways meets SPREAD: How Digital Twins and unified product data redefine rail operations

Joining SPREAD to bring actionable Product Knowledge at engineers’ fingertips

Solve E/E and software errors during R&D up to 9x times faster

Agentic Engineering Intelligence explained

Knowledge Graphs explained: How you turn data into valuable insights

Mastering SDVs: Why innovative CTOs and R&D Teams turn to Agentic Engineering Intelligence

Back to the future: CUBE 2024 Report

Back to the future - Inside CUBE 2024 and Agentic Engineering Intelligence

Practical demonstration: How Agentic Engineering Intelligence (AEI) empowers engineers

CUBE Expert Panel: How we drive AI in Engineering

Agentic Engineering Intelligence (AEI) - The future of engineering

Back to the future: 5 years of Engineering Intelligence - A journey into the future of engineering

Tech Deep Dive: Federated queries in Dataspaces

Speeding up chip-to-cloud-architecture: Have end-to-end product transparency and efficient change management to be rethought?

Enhancing Engineering Intelligence with AI Agents

How 3D diagnostics unveil efficiency in data-driven troubleshooting[DE]

AI-powered Systems Engineering: our solution explained

See SPREAD Studio in action: How to build an application in a few clicks

Mastering product complexity: how to meet industry standards and customer expectations

How we integrate AI and LLMs to make product data more accessible

John Deere, Rheinmetall, and TU Berlin share their experiences with Systems Engineering to manage product complexity

CARIAD and Renumics on the opportunities of AI applications and LLMs in Manufacturing

Introducing our platform: 4 steps to an end-to-end product journey

How can we demonstrate the added value of replacing blow fuses with semiconductors in a car?

How we use a GraphQL schema as a contract to efficiently build applications

How engineers can design more efficient products