What would help your customers understand, choose, buy, use, and keep using your LLM?
For model developers, agent platforms, API providers, and AI-native software companies. We research the full human adoption cycle — from product interaction and narrative to willingness to pay and retention.
Test whether strategically repeated or structurally surfaced constraints improve compliance in long, multi-turn and tool-using LLM tasks without materially increasing tokens.
View hypothesisMeasure goal drift across long-running LLM agent trajectories and test lightweight checkpoints that restore user intent without restarting the workflow.
View hypothesisTest whether outcome-oriented, workflow-specific framing helps customers understand why an LLM matters enough to consider buying it.
View hypothesisCompare capability-led demonstrations with workflow- and outcome-led demonstrations to learn what moves qualified buyers from “wow” to a concrete next step.
View hypothesisTest whether a richer LLM experience creates enough additional customer value to justify both a higher willingness to pay and its incremental serving cost.
View hypothesisFind the point at which evidence and uncertainty improve customer action rather than overwhelm or discourage it.
View hypothesisTest whether structured visual responses make advanced LLMs useful to non-expert customers without increasing inference complexity.
View hypothesisWe design research around what your customers need to understand, value, choose, buy, use, or keep paying for. Purchase consideration, willingness to pay, adoption, retention, and unit economics can be measured research targets — never promised results.
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