AI and ML Development Services for Businesses That Run on Data

It depends on the problem, how much data preparation is needed, whether an existing model can be adapted or a new one must be trained, the number of integrations and where it is hosted. A proof of concept costs a fraction of a full build. After a requirements review you get a fixed quote for the agreed scope.

A proof of concept typically takes 3 to 6 weeks. A full custom solution typically takes 2 to 6 months, and large enterprise platforms take longer. Most of the variation comes from data preparation and the number of systems the model has to connect to.

We agree the accuracy target before any build and measure the proof of concept against it on data the model has never seen. If it falls short, we explain why, whether that is too little data, noisy labels or a problem framed too broadly, and what would close the gap: more or better data, a narrower scope, or a person reviewing the uncertain cases. If none of those is worth it, we recommend stopping, and you have spent only the proof-of-concept budget.

No. We bring the machine learning engineers and data scientists. From your side we need someone who knows the process and can make decisions, and someone in IT who can give access to systems and data. We document everything and train your team, so an in-house team can take over later if you build one.

Python with scikit-learn, PyTorch and TensorFlow for custom models; GPT, Claude, Gemini, Llama and Mistral for language tasks; MLflow or Kubeflow, Docker and Kubernetes for deployment; and AWS, Azure, Google Cloud or your own servers for hosting. We pick per project by testing candidates on your data, not by habit.

It depends on the use case. It can be database records, documents, images, video, sensor readings or public datasets. You do not need perfectly clean data on day one: preparing and cleaning it is part of our work, and the feasibility review tells you whether there is enough to learn from.

If a ready-made tool already handles your task well, use it. Custom work makes sense when your data, process or accuracy needs are specific to your business, or when the AI has to sit inside your own systems. We often combine both, for example a hosted LLM with your own retrieval and rules.

Yes. We connect models to ERP, CRM, accounting, e-commerce, mobile and custom systems through their APIs, webhooks or data exports, and return results into the screens your team already uses.

You do. On completion and final payment, the code, prompts, trained or fine-tuned models and documentation we build for you are handed over, as set out in the contract. Third-party and open-source models stay under their own licences.

Monitoring, performance tuning, model retraining, security updates, bug fixes, infrastructure management and new features, under a support plan agreed at handover.