Perceptual Insights Inc. (piinc) is a science-driven firm that applies perceptual and cognitive research to evaluate and improve AI systemsasi. The company designs rigorous human evaluation strategies and annotation pipelines that help AI developers find model differentiators, detect failures, and optimize human-AI collaboration by leveraging insights from human perception and cognition.
Funding
Funding not disclosed
Founders
Product
Problem
Evaluating multimodal AI models is nuanced and challenging, with current approaches often failing to capture how humans actually perceive and interact with AI outputs. Human annotation pipelines are also expensive and inefficient, leading to poor data quality and high annotator fatigue. This makes it difficult for AI developers and enterprises to identify genuine model differentiators, detect failures, and build products that truly align with human capabilities.
Solution
Perceptual Insights Inc. (piinc) provides a science-driven evaluation and annotation service grounded in decades of expertise in human perception, cognition, and neuroscience. The company designs perceptually-grounded evaluation strategies that rigorously test AI models through human-in-the-loop assessments, enabling clients to find unique model differentiators and detect failures that automated metrics miss. For data providers and annotation marketplaces, piinc designs pipelines that optimally leverage human perceptual and cognitive strengths, improving labeling throughput and data quality while reducing annotator fatigue and churn. The firm's approach is informed by research insights such as how human attention varies across contexts)Skip awaiting subitizing limits, and the way the visual system averages input over time, which are applied to create more effective and human-centered AI evaluation.
Target Audience
Primary customers are frontier AI labs, AI model developers, product teams at enterprises, and data providers or annotation marketplaces that need rigorous, science-backed human evaluation to improve AI model quality and alignment.
Features
- Perceptually-grounded evaluation strategies that use rigorous human testing to uncover model differentiators and failures
- Annotation pipeline design that leverages human perceptual and cognitive strengths to improve data quality and labeling throughput
- Human-in-the-loop evaluation methodologies applied to generative imaging, video, and editing models
- Research-backed insights on human attention, subitizing limits, theory of mind, and event perception applied directly to product design and evaluation
- Assessment of models as "creative partners" using production-ready asset criteria and real creative workflows