Tristan Ratz

Engineering Team Lead (AI, Search, Knowledge)

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Portrait of Tristan Ratz

Tristan Ratz

Engineering Team Lead (AI, Search, Knowledge)

At Serviceware, I manage the teams around AI, search and knowledge of the AI-native Serviceware Platform. We are working on the future of AI-native Enterprise Service Management (ESM) and, with Agent Hub, redefining the way people work in the service industry.

Before that I worked as a Generative AI consultant at Genow AI and studied Information Systems at TU Darmstadt, where I spent most of my time on natural language processing, from question answering to retrieval augmented generation.

What I keep coming back to: in a product, AI is a means to an end, never an end in itself. A model is only as good as the experience it enables. If people notice the AI more than the improvement, something went wrong.

Based in

Darmstadt near Frankfurt,
Hesse, Germany

Working at

Serviceware as
Engineering Team Lead (AI, Search, Knowledge)

Languages

German (native)
English (C1)

French (B1–B2)

Qualities

team player
high energy

analytical

curious

fast learner

Technologies

LLMs, RAG, Search, Python, PyTorch, TypeScript, React, AWS, GCP

Interests

Large Language Models
Retrieval Augmented Generation

Search & Knowledge

Human-centered AI

Selected work

Projects

2026

This Website: Redesign 2026

React · TypeScript · Vite · Embeddings · Model2Vec · Hybrid Search · BM25 · Physics Simulation · View Transitions

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2022

diverSATy

Satellite Data · Remote Sensing · Geospatial Analysis · Climate Adaptation · Green Roofs · Business Development

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2021

Interactive Image Segmentation

Python · Tensorflow · Label Studio · Computer Vision · Image Segmentation · Machine Learning · Bachelor Thesis

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2020

Podcast

Object Detection · NLP · Deep Learning · Banking

2020

Homepage

React · JavaScript · HTML · SCSS · AWS

2019

ChatApp

Swift · iOS · SwiftUI · Project Catalyst · Python · Twisted

2016

Website

PHP · HTML · CSS · MySQL · Python · Twisted

2014

Website

PHP · JavaScript · MySQL · HTML · Wordpress

02 / 13

2024

Paper: On the Role of Summary Content Units in Text Summarization Evaluation

NAACL Main Conference 2024 · NLP

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We conducted research on the role of summary content units in text summarization evaluation. The paper was accepted at the NAACL Main Conference 2024. The code is available on Github.

At the heart of the Pyramid evaluation method for text summarization lie human written summary content units (SCUs). These SCUs are concise sentences that decompose a summary into small facts. Such SCUs can be used to judge the quality of a candidate summary, possibly partially automated via natural language inference (NLI) systems.

Interestingly, with the aim to fully automate the Pyramid evaluation, Zhang and Bansal (2021) show that SCUs can be approximated by automatically generated semantic role triplets (STUs). However, several questions currently lack answers, in particular: i) Are there other ways of approximating SCUs that can offer advantages? ii) Under which conditions are SCUs (or their approximations) offering the most value?

In this work, we examine two novel strategies to approximate SCUs: generating SCU approximations from AMR meaning representations (SMUs) and from large language models (SGUs), respectively. We find that while STUs and SMUs are competitive, the best approximation quality is achieved by SGUs. We also show through a simple sentence-decomposition baseline (SSUs) that SCUs (and their approximations) offer the most value when ranking short summaries, but may not help as much when ranking systems or longer summaries.