Retrieval-augmented generation

Give AI the right context.

We engineer retrieval, permissions, chunking, ranking, evaluation and citations so answers are grounded in the knowledge your organisation can trust.

What this means

Retrieval quality decides whether the answer is useful.

RAG is not a single database connection. It is an information architecture spanning ingestion, metadata, access, retrieval, generation, evidence and continuous evaluation.

01

Content pipelines

Ingest, clean, classify and refresh approved information.

We ingest, clean, classify and refresh approved information so retrieval is built on a governed, current knowledge base.

  • Ingestion & normalisation
  • Classification & tagging
  • Scheduled refresh
PIPELINE: SYNCING
02

Retrieval architecture

Combine semantic, lexical and metadata-aware search.

Semantic, lexical and metadata-aware search are combined so retrieval finds the right passage, not just a similar-sounding one.

  • Hybrid semantic + lexical search
  • Metadata-aware filtering
  • Chunking strategy
RETRIEVAL: OPTIMISED
03

Access control

Respect user, document and business-unit permissions.

Retrieval respects user, document and business-unit permissions, so answers never leak information beyond what a user can already see.

  • Permission-aware retrieval
  • Document-level access rules
  • Business-unit scoping
ACCESS: ENFORCED
04

Answer generation

Compose responses that remain bounded by retrieved context.

Responses are composed to stay bounded by retrieved context, reducing the risk of confident but unsupported answers.

  • Context-bounded prompting
  • Grounding checks
  • Fallback handling
GROUNDING: VERIFIED
05

Citations

Expose sources so users can verify important answers.

Every important answer exposes its sources, so users can verify claims rather than trust them blindly.

  • Source attribution
  • Passage-level linking
  • Confidence signalling
CITATIONS: ATTACHED
06

Evaluation

Test retrieval recall, grounding and task usefulness.

We test retrieval recall, grounding accuracy and task usefulness continuously, not just at launch.

  • Recall & precision testing
  • Grounding evaluation
  • Task usefulness scoring
EVAL SUITE: ACTIVE

Treat knowledge as a product

Ownership, freshness, structure and permissions determine whether retrieval can work. We map those concerns before choosing a vector store or embedding model.

Measure both halves

A fluent answer can hide weak retrieval. We evaluate the search result and generated response independently, then test the end-to-end task.

Plan for changing information

Source data changes, policies expire and teams gain or lose access. The system needs observable ingestion and refresh behaviour, not a one-time index.

When retrieval is invisible, mistakes are mysterious. When retrieval is measurable, quality can improve.

How we work

Clarity from first decision to production.

Discover

Define outcomes, users, constraints and the existing technical landscape.

Architect

Choose practical boundaries, integrations, delivery stages and quality gates.

Engineer

Build in transparent increments with testing and security embedded.

Operate

Release, observe, support and improve with clear ownership.

Build with Genesis

Turn the next important idea into a dependable system.

Bring us the outcome, the complexity and the constraints. We will help you shape the right path forward.