David Jeczmionek
David Jeczmionek

Automation · Computer vision · LLMs · Data systems

I teach computers to see what is physically happening, first in a machine, then in a room and a city, now across a continent, and I build these systems so that those responsible stay in control of their data.

Founder & Technical Lead, Techno-Merce · MSc ETH Zürich · Microsoft Mixed Reality & AI Lab


Offers

Three ways to work together

Data & AI Quick Check

Many AI projects fail because nobody checked early whether the data can answer the question. This check does that first.

For: Teams with data and a concrete idea for automation, computer vision or an AI assistant.

You get:

  • A review of your data, your process and what success would mean in numbers
  • A small working prototype on a sample of your own data
  • A clear recommendation: build, adapt or stop, with an estimate for the next step
Ask about this offer

Private AI on Your Data

Language models are most useful on internal knowledge, but many companies cannot send their data to public AI tools. I build assistants that run where you decide.

For: Companies with knowledge in documents, manuals, tickets or databases that has to stay private.

You get:

  • An assistant that searches and answers from your own data sources, and shows where each answer comes from
  • Hosting under your control, for example in a Swiss cloud region or on your own servers
  • Tests of answer quality, data access rights and safeguards before go-live
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Data & AI Systems, Built and Handed Over

Once an idea has proven itself, it has to run reliably every day, on data you can trust. I design and build the system and make sure your team can own it.

For: Organisations with a validated idea or prototype in data systems, computer vision or automation.

You get:

  • Data architecture, development and deployment on AWS, Google Cloud or your own infrastructure
  • Documentation written for the people who will run the system
  • Training and handover to your team, with support during the transition
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About

From a machine to a continent

One question at every scale, across twelve orders of magnitude.

  1. Micrometres

    In a machine

    ETH Zürich · Spherene

    Machine learning that compensates thermal errors in CNC machining, automated structural simulations that run in half the time, and an MSc in robotics, systems and control.

  2. Metres

    In a room

    Microsoft Mixed Reality & AI Lab

    Privacy-preserving deep learning for 3D stereo matching, so camera images can be processed in the cloud without being exposed.

  3. Hundreds of metres

    In a city

    ETH Zürich · Computer Vision Group

    Augmented-reality localisation data for the LaMAR benchmark, which covers 45,000 m² of university buildings, courtyards and old-town city blocks, indoors and outdoors. Plus deep learning for autonomous driving on camera and lidar data.

  4. Thousands of kilometres

    Across a continent

    Techno-Merce

    Satellite intelligence for government agencies and international organisations in six countries across West and Southern Africa. Sovereignty Eye, our satellite platform, monitors land, forests and water from Sentinel-1 radar and Sentinel-2 optical imagery.

What I do

David Jeczmionek

I am an engineer who turns AI into working systems. I know computer vision and machine learning from research at ETH Zürich and the Microsoft Mixed Reality & AI Lab, and I build with language models and agents, automation, and data platforms on AWS and Google Cloud.

I offer the whole path from idea to production. I check whether your data can answer your question, build the model or system, put it into operation, and hand it over to your team with documentation and training. In Switzerland I take on selected mandates in automation, computer vision, LLM and agentic systems, and data systems.

Alongside this, I am building Techno-Merce with a few partners. We turn satellite data into decisions about land, water and natural resources, and build digital platforms for public institutions, such as the WSP Digital Audit for water safety.

I am at home in international environments, from ministries in West Africa to research labs and companies in Switzerland. I work professionally in English, German, Italian and French, and Polish is my native language.


Expertise

Four areas I work in

Automation

Automating engineering and business processes

Software and machine learning that take repetitive work off your team, from simulation and testing workflows to content and document pipelines.

Computer vision

Systems that see what is happening

Inspection, localisation, 3D and satellite imagery. From a quick feasibility check to a model running in production, with accuracy measured on data it has never seen.

LLM and agentic systems

AI assistants that work on your data

Language models and agents that search, summarise and act on your own documents and systems, with your data kept under your control.

Data systems

Designing and building data platforms

Architecture, APIs, databases, dashboards and cloud infrastructure on AWS and Google Cloud, including field apps that work with poor connectivity.


Have a project in mind?


Projects

Selected projects and activities

Computer vision

  • Sovereignty EyeTechno-Merce

    Satellite platform for government agencies and international organisations, built on Sentinel-1 radar and Sentinel-2 optical imagery. techno-merce.com

  • Privacy-preserving computer visionMicrosoft Mixed Reality & AI Lab

    Deep-learning encoding for cloud stereo matching, with two new privacy methods based on adversarial training and channel permutation.

  • LaMAR benchmarkETH Zürich, Computer Vision Group

    Augmented-reality localisation data captured for the LaMAR benchmark (ECCV 2022), covering university buildings and old-town city blocks. lamar.ethz.ch

  • Deep learning for autonomous drivingETH Zürich

    Multi-task learning for semantic segmentation and depth estimation, and 3D object detection from lidar point clouds.

Automation

  • Thermal error compensationETH Zürich, IWF

    Machine-learning model that compensates thermal errors in CNC machining.

  • Simulation automationSpherene

    Python automation of structural validation in Ansys, cutting runtimes by 50%.

  • Content and cloud automationGlobal Swiss Learning

    AI and software automation for cloud storage, e-learning content creation and in-house productivity, plus computer vision for image and video enhancement.

Data systems

  • WSP Digital AuditUNICEF Ghana and KNUST

    Water Safety Plan system based on the WHO methodology: mobile field assessment, role-based access, automated scoring and a certification workflow.

  • National Sanitation GHG AssessmentGhana

    Greenhouse-gas inventory model for the Ministry of Local Government, Chieftaincy and Religious Affairs and WaterAid Ghana, delivered with KNUST.

  • Digital learning platformGlobal Swiss Learning

    Personalised recommender, real-time analytics, AWS architecture, DevOps and data-privacy compliance.

Teaching

  • ETH ZürichTeaching assistant

    Computer Vision, Programming for Robotics (ROS), Introduction to Robotics and Mechatronics, and the Robotics Summer School.

Own company

  • Techno-MerceFounder & Technical Lead, since 2025

    Environmental intelligence for resource protection and sustainable supply chains. techno-merce.com


Recommendations

What colleagues say

“He is intelligent, motivated, kind, takes initiative and is a great communicator, traits which make him easy to work with, and ideal for project lead and delivery.”
Cayla Casares, colleague at Global Swiss Learning
“He brings enthusiasm to discussions and is eager to explore innovative solutions as well as to work independently.”
Renlong Li, former manager

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