Applied AI & Machine Learning for Enterprise Growth

A typical AI proof of concept takes 4–8 weeks. That covers 1–2 weeks of discovery and data checks, and 3–6 weeks of building and testing a prototype on your real data. At the end you get measurable results against agreed success metrics, such as time saved, accuracy or cost per task, so you can decide whether to scale before making a larger investment.

No. We use business-grade AI services whose terms exclude customer data from model training, and we configure them that way by default. For sensitive projects, we can deploy models inside your own cloud account (for example AWS Bedrock or Google Cloud) or your private network. Your data, prompts and outputs stay under your control, and we follow GDPR requirements throughout.

RAG (Retrieval-Augmented Generation) gives an AI model access to your documents and data at the moment it answers. It finds the relevant information first, then uses it to respond. Fine-tuning retrains a model on your examples, so it permanently learns a style, format or specialised task. For most businesses RAG is the better starting point: it’s faster and cheaper to build, always uses your latest data, and can cite its sources. Fine-tuning makes sense for narrow, repetitive tasks where consistent output format or domain-specific behaviour matters most. We often combine the two.

An MCP server is a connector, built on the open Model Context Protocol, that lets AI assistants and agents securely read and act on data in your existing systems, such as databases, CRMs, ERPs or internal APIs. You need one if you want AI to work with your real business data instead of only general knowledge, especially in legacy systems that weren’t built for AI. A single MCP server makes that system usable by any compatible AI tool, including Claude, ChatGPT and custom agents, without rebuilding the system itself.

For most companies, we recommend starting with a leading commercial model through an enterprise plan, then moving specific workloads to open-source models if cost or data residency requires it. We stay model-agnostic, often combine several models behind one gateway, and benchmark them on your real data before deciding.