Operational AI Infrastructure Built for Scale, Security, and Reliability

RAG (Retrieval Augmented Generation) is an architecture that gives AI systems access to your current, specific knowledge — by retrieving relevant documents from a vector database before generating a response. Without RAG, AI can only draw on its training data (which is historical and generic). With RAG, AI can answer questions about your specific products, policies, customers, and processes using current documentation.

Three primary levers: model routing (using GPT-3.5 or Claude Haiku for simple tasks, reserving larger models for complex reasoning — typically reduces cost 40–60%), response caching (storing outputs for repeated identical queries — reduces API calls for common questions by 30–80%), and prompt optimisation (reducing token count without reducing output quality). We also set cost alerts and usage budgets to prevent uncontrolled spend.

We implement: API key management via a secure gateway (no raw keys in application code), role-based access controls for AI capabilities, data classification to prevent sensitive data reaching external AI APIs, audit logging of all AI requests and responses, and regular security reviews. For regulated industries, we offer on-premise or private cloud model deployment options.

Yes — we extend existing infrastructure rather than replace it. If you have an OpenAI account, existing vector stores, or data pipelines, we build on top of them. Our value is in the architecture, integration, and optimisation layer — not in replacing tools that are already working.

We build automated ingestion pipelines that keep the knowledge base synchronised with your source systems — re-processing documents when they're updated in Notion, Confluence, or Google Drive, pulling CRM updates on a schedule, and triggering re-indexing when key data changes. Stale knowledge is a reliability risk we engineer against from the start.

We deploy monitoring covering: API latency and error rates, token consumption and cost by model, retrieval quality scores for RAG systems, output quality sampling (automated and manual), and AI system uptime. Alerts fire when cost exceeds thresholds, error rates spike, or retrieval quality degrades. Monthly reports summarise AI system health and optimisation opportunities.