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SDV architecture: distributed ECUs consolidated into zonal hardware and centralized compute, connected as one model across requirements, functions, software, and signals

SDV Architecture: How Software-Defined Vehicles Are Actually Built

Closed-loop digital twin: field and operations data fed back to engineering on one connected model, so real-world failures reshape the next design

Closed-Loop Digital Twin: Turning Field Feedback Into Engineering Action

How to build a knowledge graph for engineering: PLM, ALM, signal, and ticket data mapped to a shared ontology and resolved into one connected, queryable model

How to Build a Knowledge Graph for Engineering, Step by Step

Engineering data management: scattered PLM, ALM, signal, and ticket systems resolved into one connected, navigable product model

Engineering Data Management: From Scattered Systems to One Navigable Model

Model-based systems engineering: a formal system model reconnected to live requirements, parts, software, and test data

Model-Based Systems Engineering: When the Model Loses Touch With Reality

Root cause analysis methods: classic RCA diagrams resolving into a connected engineering data graph

Root Cause Analysis Methods for E/E and Software Failures

Engineering intelligence for automotive: a grid of white cars with several orange cars standing out as one connected fleet

Engineering Intelligence for Automotive: One Connected Model, End to End

Warranty data analytics: orange spheres standing out as repeat-failure patterns across a grid of white data blocks

Warranty Data Analytics: Spotting Repeat Failures Before They Scale

Robert Goebel speaking at CUBE 2026, with the CUBE 2026 pixel branding

Context Engineering: The New Edge in Industrial AI

SPREAD x Salesforce at CUBE 2026, connected vehicle diagnostics

Connected Vehicle Diagnostics: From Fault to Fix

Philipp Noll speaking at CUBE 2026, with the CUBE 2026 pixel branding

The Agent-Native Product Lifecycle

AI engineering copilot: a black keyboard key with a smiling pixel face inside an orange rounded outline

AI Engineering Copilot: More Than a Chatbot for Your Product Data

GraphRAG: text chunks resolving into a connected knowledge graph feeding a language model

GraphRAG: How Knowledge Graphs Make RAG Trustworthy

Illustration of a digital product twin, used to explain digital twin vs digital thread

Digital Twin vs Digital Thread: Two Halves of One Lifecycle

Orange connected nodes and edges over a grid of white tiles, illustrating a knowledge graph vs a relational database

Knowledge Graph vs Relational Database: When Joins Break Down

Floating grey and orange robot agents with glowing pixel faces, representing agentic engineering: AI agents that reason and act on connected product data

Agentic Engineering: When AI Agents Act on Your Product Data

Orange marble-run tracks connecting spheres across a white surface, representing engineering intelligence connecting engineering data versus isolated BI metrics

Engineering Intelligence vs BI: Different Questions, Different Tools

One orange tile stands out in a grid of white tiles, representing a single connected variant management workspace across product data

Variant Management: When Product Data Outgrows the Spreadsheet

Grid of white tiles with concentric traceability rings, each holding an orange sphere, representing ISO 26262 compliance evidence connected across engineering systems

ISO 26262 and ASPICE Compliance: The Evidence Problem Nobody Budgets For

Isometric network of white pipes and valves with orange connectors linking separate lines into one system, representing PLM modernization through a connected overlay

PLM Modernization Without the Rip and Replace: Why the Overlay Beats the Migration

Orange marble-run tracks weaving through a white maze wall, each ball tracing its path to a single destination, representing warranty root cause analysis

Warranty Root Cause Analysis: Tracing Field Failures to the Engineering Cause

White toggle switches with orange check marks floating in the air, representing requirements being reviewed and confirmed one by one

AI Requirements Management: From Raw Specs to Traceable Requirements

A white clay model car mirrored by an orange digital twin reflection beneath it, representing a digital product twin

The Digital Product Twin: A Practical Guide for Engineering Teams

A black keyboard key with a pixelated crashed-face icon inside an orange rounded frame, representing failed AI pilots in systems engineering

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

A laptop screen showing an engineering knowledge graph: component and variant schema tables connected by curved relationship edges

Engineering Knowledge Graphs: How Connected Data Powers AI in Engineering

A white robotic hand pressing a single orange key on a white keyboard, representing AI-assisted engineering intelligence

Engineering Intelligence: A Practical Guide to the New Engineering Stack

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

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

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

Scaling Industrial AI from Pilots to Value: Target Architecture

Why Software-Defined Products Broke Engineering Intuition

The Engineering Intelligence Index

What is Engineering Intelligence?

Agentic Engineering Intelligence explained

Tech Deep Dive: Federated queries in Dataspaces

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?

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

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

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?

Infineon, Stadler, Mercedes-Benz and FAPS in discussion on data transparency

How engineers can design more sustainable and efficient products[DE]

Requirement-led wiring harness optimization

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