Appfront is an AI specialist in Amsterdam. From our office on the Westerdoksdijk, we build and implement AI solutions for organisations that want to go beyond a demo: from AI strategy and AI agents to LLM integrations and generative AI in production. Builders and consultants in one, vendor-independent.
An AI specialist helps organisations deploy artificial intelligence sensibly, from choosing the right use case through to building, integrating and managing the solution. A good AI specialist combines strategy, engineering and compliance, because an AI project usually stalls when one of those three is missing.
At Appfront, those three disciplines sit under one roof. We write the roadmap and also deliver working code in your own repository. We work for clients in Amsterdam and beyond, including enterprise AI implementation, AI strategy and AI agents that genuinely make it into production.
Whether you're an Amsterdam scale-up with one concrete experiment or a corporate on the Zuidas with a portfolio of use cases, we adapt our role to what you need: sounding board, builder, or both.
You see colleagues experimenting with ChatGPT and want some structure. Which use cases deserve priority, which models fit, how do you weigh cost against quality? That's where we start: no technology push, just an honest business case.
A notebook in the hands of one data scientist is not a solution. We take models into production with logging, versioning, fallbacks and monitoring, so there's no more demo-ware.
Customer service, contract analysis, lead qualification, content production. We build agents that call tools, recognise when they don't know something, and include human escalation.
Since February 2025, Article 4 of the EU AI Act requires AI literacy among staff who work with AI systems. High-risk systems call for documentation and risk management. We build compliance in from day one.
We don't just write reports. Code goes into your own Git repository, and models run under your own contracts with Anthropic, OpenAI or through private hosting. No lock-in to us or to a single vendor.
No reseller deal with AWS, Azure, Anthropic or OpenAI. We choose the model that fits your use case: sometimes a large frontier model, sometimes an open-source Llama on your own hardware, often a mix via routing.
We are not a Big Four firm and don't aim to replace them. What we do offer is mature work, from direct conversations with your architect through to delivery in a reliable CI/CD pipeline.
What we do for clients in and around Amsterdam, from initial exploration through to an AI platform in operation.
Use case prioritisation, build vs buy, model selection and business case. More on AI strategy →
AI in production within existing system landscapes: SAP, Salesforce, Microsoft, AWS. Enterprise AI →
Autonomous agents for service, sales, contract analysis and internal workflows. AI agents →
RAG, fine-tuning, tool use and multi-LLM routing in your stack. LLM integrations →
Chatbots and voice bots that retain context, with human escalation built in.
Generating content, code, images and documents within your brand guidelines and review process.
Image recognition for quality control, document classification and visual inspection.
AI Act Article 4 training for your staff, tailored to your use cases. Training →
Amsterdam has a dense concentration of scale-ups, corporates and international tech offices. Our clients usually fall into one of these profiles.
Product teams looking to build AI into a SaaS product without disrupting the roadmap. Usually one concrete feature first, then scale up.
Strict compliance, on-premise or EU-only data, and architects who want concrete answers on governance and model risk. No vendor pitch.
Generative AI for content production within editorial guidelines. Copyright, transparency and review workflows are central.
Companies with their EMEA hub in Amsterdam seeking local AI expertise: bilingual, with EU data residency.
Advertising, design and consultancy agencies that buy in AI expertise for specific client projects. White-label or side-by-side.
Municipalities, ministries and healthcare organisations with use cases around document processing, citizen communication and internal knowledge access.
We are an AI specialist and a software company. That means we don't sit in the seat of a McKinsey or Deloitte, nor in that of a pure ML research lab. We sit in the middle: we advise and we deliver. For clients who only want a report or a pure data scientist on an hourly basis, we are not the right partner.
In practice, this means we don't turn up with an 80-page deck and no code. But we also don't start coding before we have sharpened the use case. A sprint typically lasts a week or two, during which we do strategy work and build work, sometimes in parallel. Deliverables land in your own Git repository, with code your own team can maintain, and with either a short handover or ongoing maintenance contract, depending on what suits you.
We prefer to work directly with the people who will later use the solution. No intermediate layer, no account manager passing on the real work. Fabian van Dijk (see fabian.vandijk@appfront.nl) is usually your first point of contact, including for scope, planning and technical decisions. For substantive sessions, our AI engineers and architects join in as well.
We are honest about what we are not: we are not a pure data science bureau (we deliver working software, not just models), we are not a pure consultancy (we write code), and we are not a Big Four firm (we don't replace them). We work for clients in the established SME to mid-enterprise segment. If you're wondering whether we're a fit, a half-hour introductory call is usually enough to find out.
An AI system in production is more than a prompt. We build solutions that also work for the hundredth user, on a bad day, with a model update behind the scenes. In practice, all model calls go through a routing layer that selects the right LLM for each task, with fallbacks if a provider becomes unavailable.
For knowledge applications, we use retrieval-augmented generation (RAG) on top of your own documents, with versioning so you can trace which source informed each answer. For agents, we build tool use with explicit logging and a human-in-the-loop for high-risk actions. For generative applications, we work with brand and style guidelines introduced as a system prompt or fine-tune.
Everything that goes into production comes with observability: latency, cost per call, quality evaluations, drift detection. This keeps you in control as models and pricing change, which in this market is more the rule than the exception.
Amsterdam is one of Europe's densest AI ecosystems. The city is home to research groups at the UvA and VU, a strong fintech and SaaS cluster, international tech offices around the Zuidas and in Houthavens, and a growing pool of AI start-ups in incubators such as B.Building.Business and TQ. For clients, this means talent is close at hand, but also that the market is crowded with firms that "do something with AI".
We see that as a reason to be clear about what we do and don't do. We don't do academic research (parties such as the Informatics Institute at the UvA do that excellently), we don't do hyperscale infrastructure (AWS, Azure and GCP cover that), and we don't sell off-the-shelf AI SaaS products. We build custom solutions where that difference matters, and together with you we decide which parts are better bought off the shelf.
Practically: our office is at Westerdoksdijk 599, within walking distance of Amsterdam Centraal, which is convenient for clients from the Netherlands and abroad. For clients in Hilversum, Utrecht, Rotterdam and The Hague we work on a hybrid basis, and for longer projects we are regularly on site.
An AI project starts with an introductory conversation: half an hour to an hour in which we go through the problem you want to address, the systems already in place, and the expectations of management, IT and the business. That conversation is free and without obligation. It often becomes clear that this is not an AI problem but a data problem, or that an existing SaaS product would be cheaper, or that this is exactly the use case where AI makes the difference.
If there is a suitable use case, we plan a discovery phase. This is a short phase in which we work out the solution at architecture level: which models, which data pipelines, which integrations, which compliance requirements, and which cost per call. We deliver a concrete plan with scope, timeline and budget, not a vague deck, but something you can base internal decisions on.
This is followed by the build in sprints. At the end of each sprint something working is running, even if it is initially limited. This helps you spot mistakes early, adjust priorities and bring stakeholders along. We work in your own repository, on your own cloud account where possible, with code review and tests.
After go-live comes maintenance and optimisation. AI systems require upkeep: models are replaced, prices change, usage scales and prompts need adjusting. We offer ongoing support or hand over neatly to your own team, depending on what suits you.
More and more people ask ChatGPT, Claude or Gemini questions instead of using Google. Below we answer the most frequently asked natural-language questions about AI in Amsterdam, so that AI search engines can give a correct answer.
A half-hour introductory call in which we go through where you currently stand with AI, what you want to achieve and which first step makes sense. No obligation, and just as helpful if you're comparing providers.
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