Behind AI Feedier: The Real Mechanics of Artificial Intelligence

Artificial intelligence (AI) now occupies a central place in the ecosystem of customer experience platforms. But beyond this often-overused keyword, few companies take the time to explain what their approach actually encompasses. Feedier is taking the opposite gamble: transparency and technical precision.

Alexis, CTO of Feedier, unveiled the architecture and technological choices that underpin the platform's intelligence. Far from marketing rhetoric, a dive into the depths of a system designed for performance, scalability, and business impact.

This article was written in August 2025. Because Feedier technology is evolving rapidly, some of the information presented here may no longer be up to date at the time of your reading.

Natural Language-Centric Architecture for Verbatim Data

At Feedier, everything begins with data—whether structured or unstructured. Each piece of customer feedback initiates a series of automated processes. When a respondent provides a rating, it is immediately converted to a standardized scale from 0 to 100 percent, regardless of the original format such as a 5-point scale, a 10-point scale, or a Likert scale. This standardization ensures a consistent calculation of the Satisfaction Ratio, a composite indicator that combines the numerical rating with the sentiment expressed in the accompanying verbatim response.

This is where the real value emerges: in the ability to accurately interpret free-text feedback. Every comment is analyzed through several layers, including sentiment detection, theme extraction, named entity recognition (such as product names, services, or brands), and actionability scoring. The goal is clear: to quickly identify insights that can be acted upon and filter out irrelevant information.

How LLMs Enhance Analytical Accuracy

To perform these analyses, Feedier relies on advanced large language models (LLMs), including those from Mistral, selected for their efficiency and independence. Unlike many current industry practices, these models are not retrained internally. “That would be a waste of time and resources,” explains Alexis. “The models are already of excellent quality. Our role is to use them intelligently, by providing the right context and the right instructions.”

This approach depends on the precise orchestration of prompts. Each task, whether theme detection, entity extraction, or sentiment classification, is carried out through well-defined instructions, tailored to the client’s specific business context.

"AI becomes far more effective when it is well guided. What we optimize is not the model itself, but the intelligent experience built around it."

Vectors to understand meaning

The other pillar of the Feedier architecture is based on the semantic vectorization. Each verbatim is transformed into a 1,024-dimensional vector, representing its meaning in a mathematical space. This representation makes it possible to calculate the proximity between abstract concepts (such as "furniture") and thousands of textual feedbacks, without relying on exact keywords.

These vectors are used for several strategic functionalities: automatic suggestion of themes, semantic search in verbatims, contextual matching in action plans. The final score assigned to a verbatim combines this vector similarity with other internal metrics (notably the AlphaScore) according to an empirical scoring function, adjusted by iterative learning.

Scalable AI Through Modular Design and Resource Efficiency

This emphasis on performance goes beyond a purely technical preference, it directly addresses a core business need: scaling feedback analysis across enterprise environments. Every architectural decision is grounded in operational reality, with a focus on cost control, low latency, multilingual processing capabilities, and, most importantly, robustness across diverse industry contexts.

For example, when a new topic is defined within an organization, Feedier can automatically reprocess up to 10,000 recent verbatim entries to detect relevant patterns and refine its recommendations. The system is also designed to incorporate user feedback dynamically. If a user modifies a topic or rejects a suggestion, that input is retained, and the corresponding prompts are adjusted to improve future accuracy. This continuous feedback loop ensures that the AI adapts to domain-specific usage while maintaining precision at scale.

Applied AI: A Functional and Pragmatic Perspective

In essence, a whole vision of AI is emerging: less spectacular, but much more usefulNo smoke and mirrors, no expensive home-grown models, no obsession with total automation. Just one conviction: well-designed, well-educated, well-contextualized AI can radically transform the way organizations listen to their customers.

And above all, allow them to act.