Context
Team Machine ran from late 2017 to early 2021. The temporal workgraph had no direct precedent - holding an organisation's structure as a live graph and running ML inference against how it changed over time was genuinely novel in enterprise analytics. The interest was real. The architecture was right. The product-market fit wasn't there - not because the product was wrong, but because the processing layer that would have made it legible to buyers didn't exist yet.
The question the product was designed to answer - how does an organisation actually work, and how is that structure changing over time - turned out to require a processing layer that didn't exist yet. The thing that would have made the work graph useful at scale arrived four years after the company closed. What would have taken consultants weeks to derive from interviews could now run against a work graph in hours.
We were building memory for a brain that hadn't been born yet.
The work
Team Machine ingested the live data traces of how an organisation worked - conversations, plans, ideas, code, releases - and held them as a real-time graph. Every event updated the graph as it happened. Who was working with whom, on what, in what sequence, across time - visible as it moved.
Both founders came to the same question by independent routes - looking at organisational graphs from different disciplines, arriving at the same gap. Why is it so difficult to see how an organisation actually works? The org chart shows the intended hierarchy. The architecture diagram shows the intended topology. Neither shows what's actually happening. The organisation is opaque to itself, and the tools designed to help were making it worse.
The answer was a work graph - assembled from the live data traces the organisation was already producing. Slack, Jira, GitHub, Dropbox, Confluence, email. The graph already existed in pieces. The infrastructure to hold it together, and to hold it across time, didn't.