Why does Feedier use artificial intelligence?
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What is Eureka AI?
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Eureka AI transforms data management by combining real-time feedback and business records from all sources while providing excellent security with user access control, name anonymization, and ISO 27001 certification. It uses Feedier's query language with 20+ filters for fast and accurate data searches. Built on cutting-edge LLM technology, Eureka AI delivers precise text insights and information retrieval. |
How it works?
Eureka AI serves as your personal AI analyst within the platform. In this initial iteration, it will be integrated into two key modules:
Assignment Module: Previously known as the Copilot module, the Assignment module now leverages Eureka AI to generate insightful results for your assignments.
Feedier Report: Eureka AI also powers the annotations with AI-generated text in Feedier Reports, providing in-depth analysis to enhance your understanding and efficiency in interpreting your data.
Text Analysis : In the Feedier platform, some of the data processed is unstructured data (textual feedback). Analysing this data can be tedious.
What AI techniques are used on the feedier platform?
Today, Feedier uses artificial intelligence (AI) to analyse 4 types of tasks:
Sentiment analysis. In other words, the ability to determine a sentiment in a given verbatim.
Entity detection. The ability to identify "entities" such as personal data, product names, brand names, etc.
Theme detection. The willingness to mark the verbatim with themes related to the context of the project (bug, technical problem, idea for improvement, etc.).
Content generation. In practical terms, this means being able to automate tasks such as summarising verbatim, analysing feedback and generating reports.
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Feedier uses LLM (Large Language Model) technology for all AI-related tasks. We work mainly with Mistral (based in France) and the models are hosted on Microsoft Azure in the European Union (Sweden). |
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There are 3 types of text analysis used on the platform: |
General configuration
Feedier Feedier’s Eureka AI uses the context of the business to enable our models to deliver results that make sense in the operational context.
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It is important to complete the Description field on the "Organization" page. This description must be short and general. It can come directly from the website or the Wikipedia page.
Type 1 : Sentiment analysis
Each verbatim analysed is assigned to a sentiment: Positive, Neutral ou Negative.
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😐 Number of neutrals | 0 | 10 | 10 | 10 |
😠 Number of negatives | 0 | 10 | 5 | 10 |
Sentiment rate | ~ 100% | ~ 50% | ~ 66% | ~ 40% |
Number of positivesType 2 : Entity detection
With Feedier, the "entities" extracted from the verbatims are added as attributes.
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The labels (eg. Nlp Brand) displayed on the dashboard can be changed from the Attributes page. |
Type 3 : Theme (topic) detection
Themes allow verbatims to be grouped according to common subjects. Once you have created a topic, you can:
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Choose topics that you’d like to track, if available, Feedier will group text responses that would fit under this topic type.
Our commitments
We do not use our customers' data to train the AI used (except in the case of a specific request in the "Feedier NLP" contract). The models are pre-trained.
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