AI solution
RAG
Retrieval-augmented generation grounded in your authoritative content.
Chunking and embedding strategy per content typeHybrid retrieval with metadata filtersAnswer citation and refusal behaviour
Capabilities
What this solution does.
Chunking and embedding strategy per content type
Hybrid retrieval with metadata filters
Answer citation and refusal behaviour
Outcomes
What changes for the business
- Hallucination rates fall sharply
- Answers traceable to source documents
- Content owners retain control
Architecture
How it is built
- 01Ingestion, normalisation and enrichment pipeline
- 02Access-controlled vector index
- 03Reranking and citation assembly
Related services
AI StrategyA board-ready plan linking AI investment to specific processes, numbers and timelines.AI Readiness AssessmentA structured audit of data, systems, skills and risk posture before you commit budget.Private AIModels hosted inside your cloud or data centre so sensitive data never leaves your boundary.Generative AIApplied generation for drafting, summarising, extraction and content operations.LLM IntegrationEmbedding language models into existing applications, APIs and business workflows.RAG SystemsRetrieval-augmented generation grounded in your authoritative documents with citations.
Industries applying this
HealthcareClinical throughput, administrative relief and defensible data handling.ManufacturingConnected plants, predictable output, lower unplanned downtime.EducationAcademic operations, student experience and institutional reporting.RetailDemand accuracy, margin protection and connected commerce.GovernmentCitizen services, digitised process and auditable delivery.MiningSafety, asset uptime and site-to-corporate visibility.
Downloadable guide
RAG implementation guide
Reference architecture, governance controls, evaluation approach and a phased rollout plan.
Move from AI pilot to
production system.
Bring a use case and we will map data readiness, guardrails, evaluation and the route to a governed production release.