peek is for everyone who wants to create better personas but doesn’t know where to start.
Let AI peek at your personas to uncover biases before they get adopted by your team.

This is how it works


peek was made for everyone who works with ad hoc personas (the ones you made up in a meeting)

Step 1: Provide some context

Working with peek is super easy. All you need to do is to share some basic information about your project.

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Provide your project's goal. To make the evaluation more relevant to you, share the objective of the project or feature you developed the personas for

2

Upload your persona document. Just share what you have: whether it's a screenshot from Miro or a whole chapter from a keynote. peek can work with it.

3

Run the analysis. Yes, we really don't need any more information.

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Step 2: Get your analysis in 90 seconds

While you briefly wait, peek ensures that the AI-generated evaluation will be reliable and insightful.

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Management Summary: A brief summarization of the evaluation results

2

Project fit: An assessment if the personas, the way they are described, are informative for the project's goal.

3

Stereotypes: Reflection on the implicit ideas and stereotypes that characterize each persona in your document.

4

Social environment analysis: Evaluation of the personas in terms of their affiliation to social segments of different segmentation models.

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Diversity criteria: Deduction of assumed traits of each persona that reflect social and economic diversity representation.

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Documentation & Insights





Credits


Thank You

Interviews, Sparring, Conversations on UX

  • Aline Henkys
  • Andreas Loos
  • Andreas Spiegler
  • Angelika Hinterbrandner
  • Arne Kittler
  • Bertram Gugel
  • Chris Fotheringham
  • Christoph Rieth
  • Dorothée Schwartzmann
  • Hannah Bergmann
  • Harry Keller
  • Heike Lorenz
  • Indi Young
  • Johannes Klingebiel
  • Johannes Schiller
  • Jonas Föhr
  • Julia Kremer
  • Julia Lüke
  • Kai Ebert
  • Karin Bjerregaard Schlüter
  • Kristina Bonitz
  • Lukas Bezler
  • Martin Jordan
  • Maximilian Kraft
  • Michael Schieben
  • Paul Marsden
  • Sandra Schuster
  • Sebastian Waters
  • Silvia Fritzsche
  • Simon Wörpel
  • Stefanie Kegel
  • Thorsten Jonas
  • Tim Becker
  • as well as: LDI students, JVM-Academy students, participants of my UX workshops and seminars and everyone else I was lucky to talk about persona things before and during the fellowship
  • Kirby CMS-Community

Media Lab Bayern

  • Fellowship-Daddy Christian Simon
  • My co-fellows: Bianca Kriel, Daniela Späth, Kemi Fatoba, Leonid Klimov, Patricia Sack, Simon Hurtz, Uwe Martin
  • Our coaches: Angelika Goll, Jens Springmann, Masiar Nashat
  • Jessica Weber
  • Pia Lexa

Key Visual

Tool Stack

App

  • github
  • Google Cloud Run
  • Google Storage
  • Jetbrains PyCharm IDE
  • Jetbrains AI & ChatGPT
  • OpenAI GPT-4o-mini
  • python
  • Streamlit

Website

  • Jetbrains phpStorm IDE
  • Jetbrains AI & Claude
  • kirby cms
  • kirby community plugins
  • reddit
  • Stackoverflow

Productivity

  • DeepL
  • Dovetail
  • Fibery
  • Miro
  • Keynote
  • Kumu
  • Omnigraffle
  • Figma

Playlist

Resources

Segmentation Models

  • Sinus Institute: Sinus Milieus
  • GIM: Digial Media Types
  • More in Common: 6 Social Types
  • Uranos: Clåss Micromilieus
  • D21-Digital-Index: Digital Types

Diversity traits

Materials

  • You'll find an incomplete but growing material list on this page
Media Lab Bayern

peek was funded by the R&D Fellowship of Media Lab Bavaria