foglight.ai technology is fast and easy to implement

The foglight.ai is easy to evaluate and implement by inserting a plug-in into the survey screen. The foglight.ai ‘black box’ runs on the cloud, sees only survey response data, and pushes the results to an API and reporting engine.

  • The survey taker experience is identical

  • Customer gets the enhanced survey results through an API

  • foglight.ai provides performance reporting

developed in biometric labs, refined through consulting, productised and democratized through software

BIOMETRIC LABS

BIONAVI™

laboratory to measure, filter,

and understand brain activity

and biometrics on human

behavior:

- brain waves (EEG)

- galvanic skin response (GSR)

- eye-tracking (ET)

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Customers:

- scientific and academic research labs

- marketing agencies for ad and concept validation

- content creators, including hollywood, to measure audience response


CONSULTING

NEUROHM™

combination of neuroscience and psychology tools to understand explicit declarations with implicit emotions

- consulting to improve quality of survey results and remove bias

- ‘behind the scenes’ customer and employee experience improvement

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Customers:

- high-end survey and focus group providers

- most high end consultancies

- agencies looking for research

- brands that are looking to increase customer and employee outcomes

SOFTWARE

FOGLIGHT.AI

leveraging emotion analytics into responses for digital interactions using AI in scalable and embeddable software

- combine what customers say and feel for more accurate business decisions

- add emotional metrics that predict behavior

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Customers:

- application providers in the customer and employee experience space

- digital survey providers

- brands that are building their own customer and employee experiences

backed by research

  • Lee, A.Y., Wang,J., Böckenholt, U., Lee, L., Ohme, R., Reykowska, D. & Yeung, C. (2022). The Enthusiasts and the Reluctants of COVID-19 Vaccine Uptake: A Cluster Analysis. Journal of the Association for Consumer Research. Healthcare and Medical Decision Making, 7(2), pp 222-234


  • Ohme, R., Matukin, M., & Wicher, P. (2020). Merging Explicit Declarations With Implicit Response Time to Better Predict Behavior. In V. Chkoniya, A. O. Madsen, & P. Bukhrashvili (Eds.), Anthropological Approaches to Understanding Consumption Patterns and Consumer Behavior (pp. 427–448). IGI Global


  • Chkoniya, V., Reykowska, D., Ohme, R., Côrte-Real, A. (2022). What Changed in One Year of a Pandemic and What the Portuguese are not Willing to Admit: Consumer Neuroscience and Predictive Analytic Contributes to Communication Strategy. In: Reis, J.L., López, E.P., Moutinho, L., Santos, J.P.M.d. (eds), Marketing and Smart Technologies. Smart Innovation, Systems and Technologies (Vol 279, pp 419-429). Springer, Singapore

emotional insights

speed and simplicity of foglight.ai binary scale

User Experience improvement with binary scale enhanced with foglight.ai: reduce speed and complexity of surveys.