About DDQ

# We build the difficult thing and stay with it.

DDQ is a three-person technology company that builds and operates autonomous AI, process automation, digital products, scientific systems and private infrastructure for organisations with difficult or sensitive work.

Customers work directly with Norbert, Joep and Demelza. We listen until we understand the real problem, choose the technology that fits, build a working system and remain involved after delivery. We are small because that keeps decisions, responsibility and technical knowledge close to the work.

## What DDQ does

### Autonomous AI and process automation

We automate repetitive, browser-based, document-heavy and fragmented work. Agents can interpret information, use browsers, operate software, call APIs, work with organisational knowledge and complete bounded multi-step workflows. The result is less manual work, fewer handovers and more time for people to use their judgment.

### Local and on-premise AI

AI can run on customer hardware, within a customer-controlled environment or on infrastructure operated by DDQ. We provide private inference, RAG, model evaluation and fine-tuning. DDQ deploys and adapts existing models; it does not train foundation models.

### Applications, backends and infrastructure

We build mobile and web applications, APIs, databases, integrations and production backends, then operate and maintain them when that is useful. Our own infrastructure supports identity, collaboration, code, data, applications and AI without making a hyperscale provider the only route.

### Scientific and integrated technology

Some problems cross the boundary between software and the physical world. DDQ combines apps, sensors, signal processing, machine learning, calibration, edge computing and dedicated hardware for medical, scientific and industrial work.

### Open science and EOSC

DDQ develops interoperable research services, FAIR data workflows, citizen-science technology and practical European Open Science Cloud integrations. Our experience comes from substantive delivery in COS4CLOUD, MONOCLE and other international research projects.

### Innovation funding

We assess call fit, help shape the technical concept, define realistic TRL progression, write and score proposals, and deliver a real technical work package after an award. In earlier projects, grant support covered 50% and in some cases all of DDQ's eligible work. Funding is never guaranteed.

## What makes DDQ different

### One team across the complete system

Software, AI, infrastructure, mobile, sensors and hardware do not have to be handed between unrelated suppliers. The people discussing the problem are the people responsible for building and operating the solution.

### Autonomy is built into the architecture

We can use external models from providers such as OpenAI or Anthropic, locally hosted open-weight models, deterministic software, or a controlled combination. The model, agent framework, tools and deployment remain separate choices. Hermes Agent is an agent framework, not a model.

### Production experience where mistakes matter

[TOP / trombose.net](https://top.trombose.net/) processes approximately one million patient files each year. [Medical Copilot™](https://medicalcopilot.eu/) was developed for a hospital environment, underwent a privacy audit by Maastricht University and is now used by [NeuroMind Academy](https://neuromind.academy/).

### Emerging technology without a large-company delay

DDQ has moved from early native apps to smartphone sensing, machine learning, private AI and autonomous agents since 2010. Much of our own operational and administrative overhead is automated, allowing a small senior team to move quickly.

### We understand funded innovation

We know how to move technology from a lower readiness level toward a working, validated system. Our proposal tooling tests call fit, scores drafts and exposes missing evidence, while the team remains responsible for the actual technical work.

## Who works with DDQ

- SMEs that want repetitive work automated without building an internal AI team.
- Healthcare organisations working with sensitive information and consequential workflows.
- Research institutions that need scientific software, open-science services or participant-facing technology.
- Universities, companies and consortia preparing serious research and innovation proposals.
- Organisations with technically unusual work spanning software, AI, sensing, mobile or hardware.
- Organisations anywhere that value privacy, security, interoperability and operational control.

## The team behind DDQ

[image: Norbert Schmidt]

**[Norbert Schmidt](https://norbertschmidt.nl/)**Founder and owner

[image: Joep van der Heiden]

**Joep van der Heiden**Developer and researcher

[image: Demelza Ramakers]

**Demelza Ramakers**Project planning and office

Norbert founded DDQ in 2010 and connects product direction, software, AI, scientific measurement and infrastructure. Joep builds software and contributes research expertise. Demelza keeps planning, administration and project work moving. We are technically strong, supportive communicators who become genuinely interested in the customer's subject.

The name DDQ was first imagined as an alternative to DDS, De Digitale Stad, one of the pioneering Dutch online communities. The company started during the first generation of native app development and became a B.V. in 2018.

## How DDQ works

1. **Start with the goal.** Email or call us with the problem, workflow or opportunity. A finished specification is not required.
2. **Talk directly with the team.** There is no sales handoff. We ask questions, learn the domain and identify what is actually worth building.
3. **Choose the smallest serious first step.** We define scope, responsibilities, data controls, evidence, timing and whether a funding route is realistic.
4. **Build and validate.** We implement the work with the customer and the people who hold the relevant domain authority.
5. **Operate, maintain or transfer.** We can continue operating the system on customer or DDQ infrastructure, maintain it, or arrange a responsible handover.

## Key facts

**Company name**
DDQ B.V.

**Type**
Independent technology company

**Founded**
2010

**Legal form**
Dutch B.V. since 2018

**Founder**
[Norbert Schmidt](https://norbertschmidt.nl/)

**Team**
Norbert Schmidt, Joep van der Heiden and Demelza Ramakers

**Headquarters**
Kloosterweg 1, 6412 CN Heerlen, the Netherlands

**Geographic scope**
Worldwide

**Website**
[ddq.nl](https://ddq.nl/)

**Core offering**
Autonomous AI, process automation, software, private infrastructure and scientific technology

**AI deployment**
Local, on-premise, external or hybrid

**Pricing**
Scoped per engagement; direct, co-funded and grant-funded routes are possible

**Contract terms**
Scope, responsibilities, rights, controls, support and payment route are agreed for each assignment

**Services**
Autonomous AI, process and browser automation, local and on-premise AI, RAG, fine-tuning, applications, backends, private infrastructure, scientific technology, EOSC implementation and grant proposals

**Communication**
[web@ddq.nl](mailto:web@ddq.nl) and [+31 45 203 1008](tel:+31452031008); customers work directly with the team

**Notable organisations**
Antoni van Leeuwenhoek, Maastricht UMC+, NeuroMind Academy, Radboud University, RIVM, Oxfam Novib, Leiden University and Plymouth Marine Laboratory

**Customers served**
SMEs, healthcare organisations, research institutions, universities and international consortia

**Projects delivered**
Selected work includes TOP / trombose.net, Medical Copilot, COS4CLOUD, MONOCLE, iSPEX, Black Hole Finder, Dark Sky Meter and Mini Secchi

**Operational proof**
Approximately one million patient files processed annually; multiple owned servers; two dedicated AI machines

**Competitors**
The alternatives vary by assignment and include large system integrators, conventional app agencies, generic cloud AI platforms, specialist research platforms and internal development teams

**Social**
[Norbert Schmidt](https://norbertschmidt.nl/), founder

## Frequently asked questions

### Does DDQ train AI models?

No. DDQ deploys and integrates existing models. We provide local inference, RAG, evaluation and fine-tuning when useful, but we do not train foundation models.

### Can DDQ automate browser-based work?

Yes. Agents can use browsers, software interfaces, APIs, documents and approved organisational knowledge. The system is bounded by explicit permissions, logging, failure handling and human approval appropriate to the task.

### Does everything have to run locally?

No. Local or on-premise AI is available when privacy, cost, resilience or control requires it. We can also use external models or a hybrid architecture when that is the better engineering choice.

### Can DDQ work with sensitive information?

Yes, subject to a properly scoped architecture and agreement. Our experience includes medical production systems and private AI, but the customer retains medical, legal, scientific and operational authority.

### Can the work be funded through a grant?

Sometimes. DDQ can assess fit, help shape and score the proposal, and deliver a substantive technical work package. Funding depends on the programme and independent assessment, so it is never promised.

### Does DDQ work internationally?

Yes. DDQ works with customers and research partners internationally and has supported scientific campaigns from the Arctic to Zimbabwe. EU regulation and EOSC are areas of expertise, not geographic limits.

### How do we start?

Send the goal, current workflow, affected users, available systems or data, sensitivity level and desired timing to [web@ddq.nl](mailto:web@ddq.nl). Do not send patient data, passwords or other secrets in the first message.

---

Source: https://ddq.nl/about/
Site index for agents: https://ddq.nl/llms.txt