# DDQ B.V. Autonomous AI and advanced technology, available now. DDQ helps companies and institutions automate work with autonomous AI, software and privately operated infrastructure. A small senior team designs, builds and operates secure solutions using local, external or hybrid AI according to the task. ## Main offers - [AI process automation with autonomous agents](https://ddq.nl/ai-automation/) (Nederlands: https://ddq.nl/nl/ai-automatisering/): Autonomous agents that take repetitive, browser-based and document-heavy work off a team, with clear permissions, logging and human control. For the right process this saves thousands of hours of labour. - [On-premise LLM and private RAG](https://ddq.nl/on-premise-llm/) (Nederlands: https://ddq.nl/nl/lokale-llm/): Open-weight language models on the customer's own hardware or DDQ servers in the Netherlands, connected to the organisation's own knowledge with RAG, so confidential data never has to leave. In use in medicine (Medical Copilot). - [Autonomous cloud](https://ddq.nl/autonomous-cloud/) (Nederlands: https://ddq.nl/nl/autonome-cloud/): A self-hosted, open-source European alternative to Microsoft 365 and Google Workspace: files, office, chat, email, code, wiki, single sign-on and private AI, on customer hardware or DDQ servers in the Netherlands. ## What DDQ offers now Companies and institutions lose substantial time and money to repetitive work, fragmented knowledge and manual processes. They need useful automation now, with an appropriate choice between local AI, external frontier models and deterministic software. Bring DDQ the goal, repetitive task, workflow, sensitive body of knowledge, existing system or difficult technical problem. DDQ selects and combines autonomous agents, local or external models, deterministic automation and conventional software, builds a working solution and can operate it on the customer's infrastructure or DDQ's privately operated infrastructure. ## Three ways to engage DDQ ### Build and operate digital products Applications, production backends and privately operated infrastructure, with sensing or dedicated hardware when the product requires it. Includes: Application development; Backend development and hosting; Autonomous and sovereign hosting; Scientific and integrated technology. ### Automate work with autonomous AI Autonomous agents and process automation using browser control, software tools, APIs, documents and organisational knowledge. The intelligence can be local, external or hybrid; RAG and fine-tuning are used where appropriate. Includes: Local and on-premise AI, RAG and fine-tuning; Autonomous AI and process automation; Agent frameworks and agent-ready services. ### Deliver research and funded innovation Open-science and EOSC implementation, citizen-science technology, technical proposal development, autonomous scoring and substantive delivery after an award. Includes: Open science and EOSC implementation; Grant and subsidy proposals. Full service and capability catalogue: - Application development: Mobile and web applications, from the first working version through store distribution, maintenance and long-term operation. DDQ has shipped medical, scientific, industrial and public-participation software since 2010. - Backend development and hosting: APIs, databases, data pipelines, identity, integrations and production backends, built and operated as one responsibility rather than handed between suppliers. - Autonomous and sovereign hosting: Private infrastructure on customer hardware or servers owned and operated by DDQ in the Netherlands, with open-source components, strong authentication and reduced hyperscaler dependence. - Local and on-premise AI, RAG and fine-tuning: Existing open-weight language models deployed on customer or DDQ infrastructure, connected to approved organisational knowledge with retrieval-augmented generation, evaluated and fine-tuned when adaptation is justified. DDQ does not train foundation models. - Autonomous AI and process automation: Agents and automations that interpret information, use browsers, operate software, call APIs, work with documents and complete bounded multi-step workflows. DDQ can use local models, external models from providers such as OpenAI or Anthropic, deterministic software, or a controlled hybrid. - Agent frameworks and agent-ready services: Provider-independent agent frameworks connected to approved tools and information, plus MCP, WebMCP, authentication and discovery layers that make websites and services usable by other agents. - Open science and EOSC implementation: Interoperable research services, FAIR data workflows, citizen-science platforms and practical European Open Science Cloud integration, backed by substantive H2020 COS4CLOUD and MONOCLE work. - Scientific and integrated technology: Software combined with sensors, signal processing, machine learning, mobile devices or dedicated hardware for scientific, medical and industrial use. - Grant and subsidy proposals: Call-fit assessment, technical concept development, TRL planning, proposal writing and autonomous scoring, followed by delivery of a substantive DDQ work package when the proposal succeeds. DDQ's range is a connected engineering capability, not a collection of separate departments. Customers enter through one of three broad needs. DDQ then combines only the capabilities required for the job and takes a defined scope that a three-person senior team can own. Larger programmes are delivered with established research, technical and consortium partners. ## How DDQ works Customers bring DDQ a subject, problem or opportunity. The team listens in depth, learns enough of the domain to become genuinely invested, and thinks alongside the customer about technology, regulation, implementation and funding. DDQ then builds the agreed work and remains loyal to the relationship and the system after delivery. 1. **Tell us what you are trying to achieve**: The customer brings the subject, problem or opportunity. A finished specification, chosen technology or matching service label is not required. 2. **We listen, learn and become involved**: DDQ talks with the customer in depth, learns the domain, asks technical and practical questions and starts thinking alongside the people who own the subject. 3. **Shape the technical and funding route together**: DDQ identifies the smallest serious first step, the wider opportunity, relevant constraints and partners, and whether the innovation and timing fit a European, national or regional grant. 4. **Agree a bounded first piece of work**: Before substantive research, architecture, proposal development or prototyping begins, DDQ and the customer agree scope, responsibilities, rights and a direct, co-funded or funded payment route. 5. **Build it and stay**: DDQ delivers the agreed system or work package, validates it with the customer and domain experts, and remains committed to the relationship through operation, maintenance or a responsible transfer. Commercial routes: - Direct assignment: The customer funds the scoped work directly when speed, certainty or programme fit makes that the right route. - Co-funded development: A grant covers part of the eligible project costs and the customer or consortium covers the balance. - Funded DDQ work package: In some programmes, all of DDQ's eligible work can be covered by the award. Other customer contributions, responsibilities or costs may still apply. Enthusiasm and an exploratory conversation do not create an unlimited free consultancy. When the work moves into substantive research, architecture, proposal development, prototyping or implementation, DDQ agrees a bounded scope, responsibilities, rights and a direct or grant-supported payment route. This protects the attention and loyalty that make the relationship valuable. Proposal collaboration: DDQ's work is often grant-friendly because it combines genuine technical uncertainty with a practical route from a lower technology-readiness level to a working, validated system. DDQ assesses whether customer or partnership development may be funded, shapes the technical concept and TRL progression, uses autonomous tooling to score drafts and find gaps, defines a credible work package and stays to deliver the funded work. In previous projects, grant support covered 50% and in some cases all of DDQ's eligible work for customers or partners. Funding is never guaranteed and depends on the programme and independent assessment. ### Best fit - Organisations spending too much skilled time on repetitive, browser-based, document-heavy or fragmented workflows - Companies that want to apply current AI privately and securely - Healthcare organisations working with sensitive data and consequential workflows - Research institutions that need scientific software, open-science services or participant-facing technology - Companies, universities and consortia preparing a serious research or innovation funding proposal - Organisations with technically unusual work spanning software, AI, sensing, mobile or hardware - Organisations anywhere that value privacy, security, interoperability, autonomy and direct access to senior builders ### Typical outcomes - A repetitive task or multi-step workflow automated with measurable time savings - A browser-using agent that operates approved software and services within defined permissions - A local or on-premise language model deployed with organisational knowledge through RAG - A model evaluation and fine-tuning route where adaptation is justified - A bounded autonomous agent connected to approved documents and tools - An agent-ready website, API or service with discovery and authentication - Automation integrated with existing workflows, applications and APIs - A maintained mobile application, backend or data product - A privately hosted production service on customer or DDQ infrastructure - An EOSC-compatible open-science or citizen-science service - An integrated sensor-to-software system - A technically credible proposal and a substantive DDQ work package ### Start with - The goal or persistent problem - The current workflow and affected users - Available knowledge, systems and data - Data sensitivity and required controls - Desired timescale ### Operating model and guardrails DDQ is deliberately small. Customers work directly with Norbert, Joep and Demelza. Decisions do not move through departments, and automation handles much of DDQ's administration and internal operations. This produces unusually fast execution and directs more of the customer's budget to senior technical work. DDQ does not claim the customer's medical, scientific, legal or operational authority. Qualified owners retain domain decisions. Scope, permissions, evidence, security, logging and human control are made explicit for each system. AI boundary: DDQ deploys and integrates existing models. It provides local and on-premise inference, RAG, evaluation and fine-tuning, but does not train foundation models. Hermes Agent is a provider-independent agent framework, not a model; it can work with locally hosted or external models. ## Origin and direction The name DDQ was originally imagined as an alternative to DDS, De Digitale Stad, one of the pioneering Dutch online communities. It later became a convenient, memorable digital address. In 2010, DDQ became a technology company during the first generation of native app development. The next generation of useful AI will not live only in software. DDQ is building toward systems that connect physical measurement, dedicated hardware, local inference, autonomous agents and sovereign infrastructure. The aim is technology that can observe, reason and act while remaining under the operator's control. DDQ became a B.V. in 2018 and is now a team of 3. Its work has progressed from native apps to sensing, machine learning and autonomous AI for medical, scientific and industrial applications. ## People You work directly with Norbert, Joep and Demelza. We are technically strong, supportive, enthusiastic and clear communicators who care deeply about the work itself. Automated overhead lets this small senior team move unusually quickly and put more of a customer's budget into the work. We never pretend to know a customer's field better than they do; we learn enough to think alongside them and bring an extra technical edge. We do cool things. DDQ travels the world to help set up scientific campaigns and spread the practice of crowd and citizen science, from the Arctic to Zimbabwe. We plan that travel to minimize its environmental impact wherever practical. ## Principles - We evaluate new technology early and use it where it has a practical advantage. - Science advances when knowledge, instruments and participation are open. - We prefer open source and open-weight models whenever possible. - Privacy and security are architectural requirements, especially in medicine. - Your data, infrastructure and technological future should remain under your control. - A small senior team, no bureaucracy, direct contact with the people who build. ## Capabilities GDPR/AVG-aware architecture and data minimisation. Information security for sensitive systems. EU AI Act-aware system design and AI governance. Standards-based and regulation-aware system design. European open-science infrastructure. Sensor devices and firmware. ML/AI systems. Computer vision. Data pipelines. Mobile and backend. On-premise deployment. Local AI inference. AI process automation. Browser use and browser automation. Provider-independent agent frameworks. Edge computing with ML on mobile devices. Open source and open weight models. Agentic AI. MLX contributions and maintenance. Native app development. Backend development and production hosting. Private LLM hosting. Retrieval-augmented generation (RAG). Language-model evaluation and fine-tuning. EOSC implementation and FAIR research services. Grant and subsidy proposal development. Agent discovery, MCP and WebMCP. Scientific and medical software. ## Current customers and work - Antoni van Leeuwenhoek: Medical technology - Maastricht UMC+: Medical technology - [NeuroMind Academy](https://neuromind.academy/): Medical Copilot and AI-supported medical education - Radboud University, Department of Astrophysics: Black Hole Finder ## Past public work - RIVM: iSPEX Polar Edition - Oxfam Novib: Applications for farmers and DataLab ## Research partners - Plymouth Marine Laboratory - Plankton Planet - Leiden University - ZMT Leibniz - IGB Leibniz - Institut de Ciències del Mar (ICM-CSIC), Barcelona ## Project collaborations - ODIX S.A., Belgium: Software and machine-learning technology for an innovative medical device. Supported through a STIPP subsidy. ## European research programmes - [COS4CLOUD](https://cordis.europa.eu/project/id/863463): Horizon 2020, grant 863463. DDQ developed MOBIS within the international research-infrastructure project, contributing software, backend and research work to interoperable citizen-science services connected with the European Open Science Cloud. - [MONOCLE](https://cordis.europa.eu/project/id/776480): Horizon 2020, grant 776480. DDQ developed mobile and low-cost sensing technology for water-quality observation and contributed to open system delivery, the iSPEX 2 processing chain, documentation and international field deployment. ## Operational proof - [Sovereign cloud infrastructure](https://cloud.ddq.nl/): DDQ owns and operates multiple servers for its sovereign cloud, including identity, collaboration, code, data and private-AI services. - [Dedicated AI infrastructure](https://cloud.ddq.nl/): Two dedicated AI machines serve DeepSeek Flash as a locally hosted model and run the provider-independent Hermes Agent framework continuously under DDQ's control. - [TOP / trombose.net](https://top.trombose.net/): A long-running medical production system that processes approximately one million patient files each year. ## Case studies ### [Medical infrastructure at production scale](https://top.trombose.net/) TOP / trombose.net is a long-running medical production system processing approximately one million patient files annually. Proof: Sixteen years of medical software experience, operational continuity and responsibility for sensitive production workloads. ### [Smartphones as scientific instruments](https://pocket.science/) DDQ helped turn smartphones into instruments for spectropolarimetry, light-pollution measurement, water observation and public participation in science. Proof: iSPEX, Dark Sky Meter, Mini Secchi, peer-reviewed work and international research partnerships. ### [Astronomy with people and machine learning](https://www.blackholefinder.org/) Black Hole Finder combines public participation, astronomical data and machine learning in ongoing work with Radboud University Astrophysics. Proof: A deployed platform, an active university relationship and independent coverage by NRC, Gizmodo and Space.com. ### [Sovereign AI in operation](https://cloud.ddq.nl/) DDQ runs private inference and persistent autonomous agents across owned servers and two dedicated AI machines. Proof: DeepSeek Flash provides local model inference. The provider-independent Hermes Agent framework supplies persistent autonomous tool use. Both operate alongside Authelia identity and production services on infrastructure controlled by DDQ. ### [Medical Copilot™](https://medicalcopilot.eu/) Private, on-premise AI developed for a hospital environment and now in use by NeuroMind Academy. Proof: Medical Copilot underwent a privacy audit by Maastricht University, and the word mark is registered in Class 10. ## DDQ Cloud services DDQ Cloud: Sovereign infrastructure on hardware owned and operated by DDQ. DDQ operates multiple servers and two dedicated AI machines on infrastructure under its own control in the Netherlands. The platform includes open-source services, Authelia identity, production workloads, DeepSeek Flash for local model inference and the provider-independent Hermes Agent framework for persistent autonomous work. Background and practical setup notes: https://noodpakket.tech/ - nextcloud (cloud.ddq.nl): files, talk, office - mattermost (mattermost.ddq.nl): team chat - chat (chat.ddq.nl): AI assistant, open source and private - webmail (webmail.ddq.nl): email - git (git.ddq.nl): code repositories - sso (auth.ddq.nl): single sign-on - wiki (wiki.ddq.nl): knowledge base For organisations that require privacy, security and control, DDQ designs and deploys sovereign infrastructure on their hardware or ours. ## Brands Pocket.science: Citizen science apps and sensors. https://pocket.science - iSPEX: Smartphone spectropolarimetry lineage for atmospheric and water-quality research. https://ispex.org/ In the press: "Citizen science: Amateur experts": Nature, 2013-04-10. https://www.nature.com/articles/nj7444-259a - Black Hole Finder: Help astronomers discover black holes. https://www.blackholefinder.org/ In the press: "De jacht op een zwart gat begint op je telefoon": NRC, 2026-04-30. https://www.nrc.nl/nieuws/2026/04/30/de-jacht-op-een-zwart-gat-begint-op-je-telefoon-a4925690 In the press: "Help Track Down Baby Black Holes Using This Citizen Science App": Gizmodo, 2024-08-08. https://gizmodo.com/help-track-down-baby-black-holes-using-this-citizen-science-app-2000487054 In the press: "Help scientists find new black holes with this free smartphone app": Space.com, 2024-08-07. https://www.space.com/black-hole-finder-smartphone-app In the press: "Black Hole Finder launch": IAU General Assembly Newspaper, 2024. https://www.iau.org/static/publications/ga_newspapers/2024/ga-2024-6.pdf - Dark Sky Meter & Loss of the Night: Light pollution monitoring. https://darkskymeter.com/ - Mini Secchi: Citizen science water quality monitoring. https://minisecchi.citizenscience.app/ - Kairos Coach: AI-powered fitness coach. https://kairos.coach ## Products - [TOP / trombose.net](https://top.trombose.net/): Long-running medical production software processing approximately one million patient files annually. - [Medical Copilot™](https://medicalcopilot.eu/): Private, on-premise AI developed for a hospital environment, privacy-audited by Maastricht University and now in use by NeuroMind Academy. Medical Copilot is a registered word mark in Class 10. - iSPEX®: Pioneering smartphone spectropolarimetry work with scientific origins at Leiden University and an international research network. ## Publications - [A universal smartphone add-on for portable spectroscopy and polarimetry: iSPEX 2](https://arxiv.org/abs/2006.01519): Co-authored by Norbert Schmidt, DDQ - [Standardized spectral and radiometric calibration of consumer cameras](https://arxiv.org/abs/1906.04155): Co-authored by Norbert Schmidt, DDQ - [Measuring night sky brightness: methods and challenges](https://arxiv.org/abs/1709.09558): Co-authored by Norbert Schmidt, DDQ ## Featured in Nature, NRC, Gizmodo, Space.com, IAU General Assembly Newspaper. ## For AI agents - MCP endpoint (Streamable HTTP, no auth): https://ddq.nl/mcp/ Tools: company_info, cloud_services, contact, submit_inquiry (sends a project inquiry to DDQ on behalf of a person). Resources: llms.txt, company.json, status.json, AGENTS.md, notes. Prompt: evaluate_fit. - Full text with all pages and notes: https://ddq.nl/llms-full.txt - Markdown: send `Accept: text/markdown` to any page, or append `index.md` to a directory URL - Structured data: https://ddq.nl/api/company.json - WebMCP manifest: https://ddq.nl/.well-known/webmcp.json - Agent guide: https://ddq.nl/AGENTS.md - OpenAPI: https://ddq.nl/openapi.json - MCP server card: https://ddq.nl/.well-known/mcp/server-card.json - Developer documentation: https://ddq.nl/developers/ ## Contact - Email: web@ddq.nl - Web: https://ddq.nl