More customers
Reach people beyond expert and early-adopter audiences.
We research how humanities, design, and behavioral science can help your customers and your AI understand each other better—driving adoption and revenue.
Powerful models still lose customers through cognitive overload, unclear recommendations, misplaced confidence, and interfaces designed for experts rather than real people.
Reach people beyond expert and early-adopter audiences.
Help customers understand, trust, and act on AI responses.
Test ideas before committing product, compute, or marketing budgets.
How do you turn model intelligence into mass-market adoption?
Measure how explanation, uncertainty, tone, and interaction structure affect activation, paid conversion, and sustained use.
Which additional compute produces value customers can perceive?
Compare longer reasoning, multimodal output, and better response design against cost per successful customer task.
How should AI communicate through a screen, voice, image, or device?
Test which modality and presentation sequence helps customers understand faster and adopt features more deeply.
When will employees understand, trust, and safely delegate work to AI?
Research delegation thresholds, explanation of agent actions, and oversight designs that preserve productivity.
Model engineering improves what AI can do. We test how language, narrative, visual design, culture, and behavior improve what customers can understand and use.
Every project has a predefined population, intervention, comparator, primary metric, meaningful threshold, business target, and decision rule.
Find the point at which evidence and uncertainty improve customer action rather than overwhelm or discourage it.
View hypothesisStudy whether concise, emotionally appropriate structure improves action in high-stakes or stressful contexts.
View hypothesisTest whether structured visual responses make advanced AI useful to non-expert customers without increasing inference complexity.
View hypothesiscumulative reference score
working on the same commercial question
A negative result can be valuable: it may prevent a costly feature, campaign, or compute investment from scaling before it works.
Discuss a Research Project