LLM & RAG
LLMs become useful products when they are grounded in the right data and measured against real tasks. We build RAG and generative AI systems that work beyond the demo.
Why we use it
- /Turns unstructured information into usable workflows
- /Makes existing knowledge searchable through natural language
- /Can automate extraction, summarisation, and classification
- /Works across multiple model providers and data sources
When we reach for it
- /Knowledge assistants over internal documents
- /Search across large unstructured datasets
- /Document extraction and summarisation workflows
- /AI features that need answers grounded in proprietary data
We start with the task and an evaluation set, not the model. Retrieval quality, prompt behaviour, latency, cost, and failure cases are measured before the feature ships.
For RAG systems, we design ingestion, chunking, metadata, retrieval, permissions, and citations as one system. The model is only one layer.
Common questions
What is RAG?+
Retrieval-augmented generation combines a language model with external information retrieved at query time, allowing responses to be grounded in your own documents or data.
When should we use RAG instead of fine-tuning?+
RAG is usually appropriate when the model needs access to frequently changing or proprietary knowledge. Fine-tuning is better suited to changing model behaviour rather than continuously updating factual knowledge.
Can you build an AI assistant using our internal documents?+
Yes. We can build ingestion, retrieval, permissions, citations, evaluation, and the application layer required for an internal or customer-facing knowledge assistant.
Which LLM providers do you work with?+
We work across providers such as OpenAI, Anthropic, and AWS Bedrock and select models based on quality, latency, privacy, and cost.
How do you reduce hallucinations?+
We ground responses in relevant source material, control retrieval, define fallbacks, and evaluate outputs against representative use cases before production.
How do you measure an LLM feature?+
We define task-specific evaluations covering dimensions such as accuracy, retrieval quality, relevance, latency, failure rate, and cost.