Why your company needs a context graph, and how to build it

Official Schedule Context

Everyone building AI products eventually draws the same diagram: boxes representing data sources,

arrows pointing at the model, and a label that says "context." What that diagram doesn't show is the

system that has to run underneath it deciding, for each request: which sources to consult, whether

to fetch live or use cached data, if the user is actually allowed to view that data, how to stitch

it all together before the latency budget runs out. And it hides the counterintuitive part: fetching

more context usually makes your answers worse, not better. At Merge, we reframed context graphs as

control planes, helping companies scale context graphs to hundreds of thousands of users with

sub-300 ms latency. This talk walks engineers through the system design at scale: how to tier data

freshness, why provenance isn't optional once third-party systems are in the loop, and how to decide

when fetching less context is the right call. Attendees will leave with a mental model for context

system design that separates the orchestration decisions from the retrieval layer.

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