About Continker
Continker is an AI engineering studio based in Amsterdam, the Netherlands. Our roots are in machine learning for finance, going back to 2014. We became a company in 2021 and have since expanded into pharma, logistics, and retail.
Amsterdam put us in the heart of mainland Europe, close to the industries we serve. The discipline we built working with financial institutions carries into everything we do, from pharma to logistics to retail.
How we work
We build our own products and design custom AI solutions for organizations that need them. Products and services we have helped build reach millions of users. We also place senior engineers at clients on-site, working on your codebase, in your standups, alongside your people. Our engineers ship.
Our work spans the full delivery chain. Data science, engineering, and pipelines. Custom model development, from fine-tuning open models to designing bespoke architectures. Integration with OpenAI, Anthropic, Mistral, and others. Web interfaces, APIs, and tooling. Deployment across European or global infrastructure. Monitoring in production, with retraining when the data shifts.
Our products
We develop our own AI products first. Assistive, protective, analytical tools for markets and solutions that did not exist before generative AI. The problems are harder, the approaches are novel, and our engineers learn from real users every day. They track the latest research, test new ideas in production, and build with the state of the art.
This is what sets our client work apart. Our engineers bring ideas clients have not considered and they execute on them. They work independently or embedded in teams, with a bias toward getting things done. Practitioners who deliver.
What we build
Most clients come to us for agentic AI and LLM-powered systems. Complex reasoning chains, tool orchestration, AI assistants that automate real workflows. We design these end to end, from prompt architecture and retrieval pipelines to the interface that makes them usable.
Some problems call for a different approach. Continker AV uses a custom transformer and tokenizer attached to a reasoning model to detect malware on edge devices. It runs with minimal resources and solves a problem no general-purpose model can. We match the architecture to the problem.
Secondment
We place senior engineers in client organizations. Some clients want a long-term expert to join their team, work across multiple projects, and bring AI capability into the organization from the inside. Others need someone for a few months to build a specific system, a chatbot, a document pipeline, an internal tool, and then hand it over. Placements are typically mid-to-senior level, ranging from a few months to long-term embedded roles depending on what the client needs.
R&D partnerships
Some clients come to us with research problems, not just engineering tasks. Exploring whether a model can solve a specific challenge, prototyping new approaches, testing what is possible before committing to production. We run these as structured R&D engagements with clear milestones and honest assessment of feasibility.
Infrastructure
The default is European cloud, for compliance and trust. Data residency is part of the architecture from day one. We deploy on providers like Scaleway, Hetzner, and Exoscale when European hosting is required, and work with AWS, Azure, and Google Cloud when the project calls for global reach or specific platform capabilities. The choice depends on the client, the data, and the regulations that apply.
Tools we use
Most of the work happens in Python, with TypeScript and JavaScript for web interfaces and tooling. Rust and Go come in where performance or concurrency matters. On the AI side we build with PyTorch, TensorFlow, and Hugging Face for model development, ONNX and NVIDIA Triton for optimized inference, and vLLM for serving large language models. LangChain and LangGraph handle orchestration in agentic systems. Experiment tracking runs through MLflow.
The front end is React. Data lives in PostgreSQL, Redis, and Elasticsearch depending on the access pattern. Everything ships in containers or serverless functions, orchestrated with Kubernetes where needed. The stack is not fixed. We pick what fits the problem and the client environment.