Custom AI & Machine Learning Development Services

The cost of custom AI development depends on the complexity of the problem, the volume and quality of your data, and the required level of accuracy and scalability. A focused project such as a document classification model or a chatbot integration typically starts in the range of fifteen to forty thousand pounds. More complex systems involving custom model training, multiple data sources, and enterprise-grade deployment can range from fifty to two hundred thousand pounds. We provide detailed estimates during the scoping phase so there are no surprises.

Timeline depends on the project scope. A straightforward LLM integration or API-based AI feature can be delivered in 4 to 8 weeks. A custom machine learning model requiring data preparation, model training, and iterative refinement typically takes 8 to 16 weeks from kick-off to production deployment. Complex systems with multiple models or real-time inference requirements may take 4 to 6 months. We always provide a detailed timeline during the proposal phase.

The data requirements depend entirely on the type of AI solution being built. For predictive analytics, you need historical data relevant to the outcomes you want to predict. For NLP systems, you need text data representative of your domain. For LLM integrations, you may need relatively little proprietary data since the models are pre-trained. Our first step is always a data assessment — we evaluate what you have, identify gaps, and recommend the most practical path forward given your current data landscape.

We do both, and the right approach depends on your use case. For many business applications — particularly those involving text generation, summarisation, classification, or conversational AI — integrating with a pre-trained large language model like GPT-4o, Claude, or an open-source alternative is the most cost-effective and fastest path to production. For use cases requiring domain-specific accuracy, proprietary data, or specialised predictions, we train custom models from scratch or fine-tune existing ones to achieve the performance your business demands.

Model performance monitoring is a core part of every deployment we deliver. We implement automated monitoring for key metrics such as accuracy, latency, and data drift. When model performance degrades — which is natural as real-world data evolves — our support plans include scheduled retraining cycles and model updates. We also set up alerting systems so your team and ours are notified immediately if performance drops below defined thresholds, ensuring issues are addressed before they impact your business.