What is a large language model (LLM)?
A large language model is an AI model trained on vast amounts of text that can understand and generate language, write and review code, summarise documents, extract data and reason through multi-step tasks. Well-known families include OpenAI GPT, Anthropic Claude, Google Gemini, and open-source models such as Meta Llama, Mistral, DeepSeek and Qwen.
RAG, fine-tuning or prompting?
Most business use cases do not need a custom-trained model. Prompt engineering gets surprisingly far with a strong general model. Retrieval-augmented generation (RAG) connects the model to your own documents and data, so answers are current, grounded and cite their sources. Fine-tuning adapts a model to a specific style, format or narrow task, and can make a smaller, cheaper model perform like a larger one. We help you choose the simplest approach that meets your quality, cost and privacy goals.
Hosted API or open-source model?
Hosted models from providers such as OpenAI, Anthropic and Google offer top quality with no infrastructure to manage. Open-source models like Llama, Mistral or Qwen can run in your own cloud or data centre for full data control and predictable cost at scale. Many production systems combine both, routing each request to the most suitable model.
From prototype to production
A demo is easy; a reliable LLM product is not. We add evaluation datasets, guardrails against prompt injection and data leakage, monitoring of quality, latency and cost, and human review for sensitive actions, so your LLM features keep working as models and data change.
