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Capital-intensive delivery
Any delay costs millions in lost production, so the finish date carries the business case.
Use cases
By organization
Featured stories
Latest from the blog
AI with full project context: Connect Nodes & Links with AconexNovember 5, 2026All blog posts →Featured guide
GuideThe Executive Guide to AI for Project DeliveryTest date confidence, understand project risk and focus interventions, with auditable evidence.Read the guide →All guides →Watch, read and learn
WebinarsLive sessions and on-demand replays.WhitepapersLong reads on where project delivery is going.ResearchThe peer-reviewed work behind the models.Case studiesHow teams use Nodes & Links on live projects.The Physics of Project AccelerationOur book on pulling project dates forward.All resources →Every day saved in fab construction is worth millions. Hold every contractor to the standard and find the time you can still win back.
What makes it hard
Any delay costs millions in lost production, so the finish date carries the business case.
Finding integrity issues across thousands of tasks took hundreds of hours a month.
Every contractor plans differently, and the gaps between them are where time disappears.
Customer story
A leading semiconductor manufacturer
Challenge
Persistent schedule quality issues. Finding integrity problems across thousands of tasks took hundreds of hours a month.
What changed
Automated integrity analysis detected issues in seconds and held contractors to scheduling best practice.
Built for both sides of the project
The decisions fab teams bring to it
One-pager
Nodes & Links for chip fabs: the results, the decisions it answers and how teams get started, on one page.
Yes. It connects to Oracle Primavera Cloud, Primavera P6 and Microsoft Project, reads documents from Aconex, Procore, Autodesk and Trimble, and takes anything else by API or upload.
Yes. Set your integrity standard once, and every contractor schedule is checked against it as it lands, with issues flagged in seconds.
Yes. Customer data is never used to train AI or LLM models, and each customer’s data stays in its own environment.