A technical AI partner, not just a vendor
Some AI challenges can’t be solved by plugging in an existing tool. As an Amsterdam-based AI agency with over a decade of engineering experience, we design and build custom AI solutions from the ground up — taking ownership of architecture, development, and delivery so you get something that actually fits your business.
Our AI
development services
From computer vision systems to custom ML models and full AI-powered products, we build solutions tailored to your data, your workflows, and your goals.
Custom AI product development
We build AI-powered products from concept to deployment. That means owning the architecture, developing the models, and delivering something that’s production-ready and built to scale.
Machine learning model development
We develop, train, and deploy custom ML models for classification, prediction, recommendation, and anomaly detection, using your data to solve your specific business problems.
Computer vision solutions
Object detection, image classification, quality control, visual inspection: we build computer vision systems that turn visual data into actionable intelligence for your operations.
Natural language processing (NLP)
From document understanding and sentiment analysis to custom chatbots and intelligent search, we build NLP solutions that make sense of unstructured text at scale.
AI model fine-tuning and optimisation
We take foundational models and adapt them to your domain, data, and tone. The result is better accuracy, lower costs, and model behaviour that fits your use case precisely.
MLOps and AI infrastructure
We design and implement the pipelines, monitoring systems, and infrastructure needed to keep your AI models reliable, scalable, and performing in production over time.
Technologies & platforms
We work across the full AI development stack, from ML frameworks and LLM platforms to vector databases and deployment infrastructure.
AI & ML cases
Our engineers have built custom AI solutions for clients across industries — from predictive systems and computer vision tools to intelligent automation and ML-powered products.
Our AI development process
Problem definition and scoping
We define the problem precisely, assess data availability and quality, and confirm whether a custom AI solution is the right approach before any development begins.
Data preparation and modelling strategy
We audit, clean, and structure your data, then design the modelling approach, including algorithm selection, training pipelines, and measurable performance targets.
Model development and iteration
We build and train your models iteratively, testing against real-world scenarios and refining until we hit the agreed targets.
Integration and deployment
We integrate the solution into your existing systems, deploy to your chosen infrastructure (cloud or on-premise), and ensure everything performs reliably at scale.
Monitoring, maintenance, and improvement
We monitor model performance post-launch, detect drift, and continuously improve accuracy as your data and requirements evolve.
Enabling users to be part of the design and delivery process in such an artful way makes the leaders of this business quite unique.
The WeAreBrain team stood out because they understood the problem we were trying to solve, and how we aimed to solve it.
Their speed and their attitude were impressive — the speed of their work is limited by your speed only!
FAQs
Questions about how we build custom AI solutions? Find the answers here.
When does a business actually need a custom AI solution?
When your problem is specific enough that general-purpose tools can’t solve it reliably — or when existing solutions don’t integrate well with your data, systems, or workflows. If you’re not sure, that’s exactly the kind of thing we help you figure out first.
What types of AI models do you build?
We build supervised and unsupervised ML models, deep learning models, computer vision systems, and NLP solutions. The right type depends entirely on your data and the problem you’re solving.
Do we need a large dataset to get started?
Not necessarily. It depends on the complexity of the problem. We assess your data during scoping and advise on what’s needed, whether that’s augmenting existing data, generating synthetic data, or adapting a pre-trained model to your context.
Which cloud platforms and ML frameworks do you work with?
We work across AWS SageMaker, Azure ML, and Google Cloud, and use frameworks including PyTorch, TensorFlow, and scikit-learn, depending on what best fits the solution.
How do you ensure model accuracy and reliability in production?
We define performance benchmarks upfront, test against real-world data before deployment, and implement monitoring pipelines that flag degradation or drift so issues are caught early.
Can you work alongside our internal data science or engineering team?
Yes. We regularly collaborate with in-house teams — whether that’s leading the build, contributing specific expertise, or providing additional capacity on an ongoing basis.
