AI & ML Solutions

Who this is for

Most AI projects fail not because the model is wrong, but because it was never production-ready. A prototype that works in a Jupyter notebook is not a system. Turning a model into a reliable service requires real engineering: data pipelines, inference infrastructure, monitoring, fallback logic, and cost controls.

You have an AI prototype that works in demos but fails under real load or real data
You have no monitoring for model drift, degradation, or silent failures
Inference is too slow or too expensive for your actual usage volume
Your data pipeline is a manual process that breaks when anyone is on holiday
You're relying on a vendor LLM API and have no fallback if it changes pricing, behavior, or availability
You need to fine-tune or train a model on your own data but don't have the infrastructure to do it

What we deliver

AI features that run reliably in production, not demos that work once in a controlled environment.

ML model development

Custom model development for classification, regression, ranking, anomaly detection, NLP, and computer vision tasks, selected based on your data and constraints, not the current trend.

Model selection and baseline evaluation
Feature engineering and data preprocessing
Training, validation, and hyperparameter tuning
Model versioning and experiment tracking

LLM integration and RAG

Practical integration of large language models into your product, with retrieval-augmented generation (RAG), prompt engineering, guardrails, and cost management built in from the start.

RAG pipeline design and implementation
Prompt engineering and optimization
Output validation and guardrails
Vendor fallback and cost controls

Training and inference pipelines

Automated, reproducible pipelines for data preparation, model training, and deployment, so retraining is a scheduled operation, not a manual scramble.

Data ingestion and preprocessing pipelines
Distributed training (where required)
Model registry and artifact management
CI/CD for model retraining

Production inference and monitoring

Serving infrastructure that keeps inference fast, cost-effective, and observable. We track model behavior in production so issues are caught before users notice.

REST and batch inference APIs
Model performance monitoring
Drift detection and alerting
Latency and cost optimization

How it works

AI projects have a high failure rate because teams skip the data and infrastructure assessment and go straight to building. We don't.

Data and feasibility assessment (1–3 weeks). We review your data: volume, quality, labeling, and freshness. We give you an honest assessment of what's achievable and what's not before committing to a build. Some projects are not ready for ML yet, and we'll say so.
Model selection and baseline (1–2 weeks). We establish a baseline with simple approaches first. If a heuristic or rule-based system is good enough, we'll tell you: it's cheaper and more maintainable than a trained model.
Pipeline and infrastructure design (1–2 weeks). We design the training pipeline, inference architecture, and monitoring strategy. This is documented and reviewed before build starts.
Build and training (4–16 weeks). Pipeline implementation, model training, evaluation, and iteration. Delivered in milestones with evaluation reports at each stage.
Production deployment and monitoring setup. Inference API deployed to your infrastructure. Monitoring dashboards in place. Alerting configured for drift and performance degradation.
Handoff and documentation. Full system documentation: data schema, model card, pipeline diagram, deployment runbook. Your team can retrain and redeploy without us.

Indicative pricing

AI and ML costs vary more than other software; they depend heavily on data readiness, model complexity, and infrastructure requirements.

Feasibility study and proof of concept

€20,000–€60,000+

What it covers: Data assessment, baseline model, initial evaluation, and a written recommendation for whether to proceed to production and what that would cost.

Best for: Teams who need to validate whether ML is the right approach before committing a larger budget.

LLM integration or RAG system

€40,000–€200,000+

What it covers: Production-ready integration of a large language model into your product or workflow: RAG pipeline, prompt engineering, guardrails, fallback logic, and monitoring. Includes proper cost management so you're not surprised by API bills.

Best for: Products that need document understanding, semantic search, classification, or generation features.

Full production ML system

€80,000–€400,000+

What it covers: End-to-end: data pipeline, custom model training, inference API, monitoring, and drift detection. Built for reliability, not a demo environment.

Best for: Teams with validated use cases who need a system that works at scale and can be maintained and retrained over time.

Ranges are indicative. Final fees depend on data readiness, model complexity, and infrastructure scope, all assessed during discovery.