Probabilistic NLP · Responsible AI

DPULSE.AI — Probabilistic NLP for Organizational Narratives

Developed probabilistic NLP pipelines to identify emerging themes in employee narratives, with an emphasis on uncertainty calibration and responsible interpretation.

Conceptual diagram
Role
Co-founder & Founding Engineer
Institution
Founded with two EPFL professors
Date
Sep 2025 – Jan 2026

Technologies

PythonProbabilistic topic modelingNLPUncertainty calibrationResponsible AI

Context

Context

DPULSE.AI explored how probabilistic natural language processing can surface emerging themes in employee narratives. The emphasis throughout was on uncertainty calibration and responsible interpretation: treating model outputs as probabilistic evidence rather than verdicts, and being explicit about what the models can and cannot support.

Problem

Problem

Emerging themes in employee narratives are diffuse, evolving, and context-dependent, which makes them hard to capture with rigid, deterministic classifiers.

Outputs in this domain are sensitive, so calibrated uncertainty is essential to avoid over-confident conclusions.

Responsible interpretation requires keeping a human in the loop and resisting the temptation to over-claim what the model has detected.

Scope

System scope

  • Probabilistic topic modeling of employee narratives

  • Uncertainty calibration of model outputs

  • Responsible, human-in-the-loop interpretation

  • Product-oriented engineering scaffolding

Methods

Approach & methods

  • Used probabilistic topic modeling to represent emerging themes as distributions rather than hard labels.

  • Focused on uncertainty calibration so that confidence scores reflect genuine reliability.

  • Framed outputs around responsible interpretation, designed to support human judgment rather than replace it.

  • Built the engineering scaffolding needed to turn research ideas into usable tooling.

Contributions

Contributions

  • Co-founded the project and acted as founding engineer, building the probabilistic NLP pipelines and the engineering scaffolding around them.

  • Collaborated directly with two EPFL professors on the modeling and interpretation methodology.

  • Prioritized uncertainty quantification and responsible interpretation as first-class design constraints.

Results

Results

Role

Co-founder & Founding Engineer

Collaboration

2 EPFL professors

Focus

Uncertainty calibration

Process

Technical process

  1. 01Narrative corpus
  2. 02Probabilistic topic model
  3. 03Calibrated signals
  4. 04Human interpretation

Limitations

Limitations

Early-stage work: this case study deliberately makes no claims about traction, customers, accuracy, or deployment scale.

Theme detection in narratives is inherently subjective and benefits from careful human interpretation.

Labelled data in this domain is scarce, which constrains evaluation and motivates the focus on uncertainty.

Probabilistic NLP · Responsible AICo-founder & Founding Engineer