RAG solutions

Reliable AI assistants that ground large language models in your data through retrieval augmented generation.

We architect data pipelines, vector search and orchestration layers so answers stay accurate, auditable and secure.

RAG architectureVector databasesKnowledge basesPrompt orchestrationEvaluationMonitoring
Plan a RAG initiativeBack to Software Development

What is retrieval augmented generation

Retrieval augmented generation combines information retrieval with large language models to provide grounded, context aware responses.

Relevant documents are retrieved from knowledge bases, embedded and passed to the model to reduce hallucinations.

We deliver RAG systems with observability, feedback loops and governance so outputs remain trustworthy.

Data pipelines

Ingestion, cleansing and chunking of documents, transcripts and structured data.

Vector search

Embeddings, similarity search and hybrid retrieval for accurate context.

Prompt orchestration

Dynamic prompts, tool usage and response ranking for consistent quality.

Evaluation

Automated scoring, human review and analytics dashboards.

Why RAG is valuable

  • Grounds AI responses in approved knowledge
  • Improves compliance and reduces hallucinations
  • Enables rapid updates as content changes
  • Supports internal productivity and customer support
Successful RAG programmes rely on strong data governance, monitoring and user feedback.

RAG projects we deliver

How we apply retrieval augmented generation across industries.

Knowledge assistants

Internal experts that surface policies, procedures and best practice.

Customer support

Self service portals and chat experiences grounded in manuals and FAQs.

Research copilots

Assistants that summarise journals, case law or market intelligence.

Compliance automation

Audit ready summaries and evidence gathering across documentation.

Sales enablement

Proposal and pitch support using approved collateral and data.

Operations automation

Procedural guidance and checklist support in regulated environments.

When RAG is the right choice

  • You have curated knowledge bases or document repositories
  • Teams require accurate, explainable AI responses
  • Regulatory requirements demand audit trails
  • Content changes frequently and needs rapid updates

When to consider alternatives

  • Simple FAQs may work with traditional search or automation.
  • Transactional workflows might rely on rule based systems.
  • If data quality is low, invest in data governance first.
  • Prebuilt SaaS assistants could provide short term coverage.

RAG vs direct LLM calls

CriterionRAGGroundedDirect LLMGeneric
AccuracyHigh when knowledge base is curatedDepends on provider training data
ComplianceAllows audit trails and citationsLimited visibility on sources
MaintenanceRequires content updates and embedding refreshMinimal upkeep but less control
SpeedSlightly higher latency due to retrievalLower latency for straightforward prompts
Use casesInternal knowledge, regulated support, complex journeysGeneral purpose chat or creative ideation

We help you evaluate whether RAG or direct LLM calls best meet your accuracy, compliance and speed targets.

Build grounded AI assistants

We deliver discovery, architecture and implementation for retrieval augmented generation systems.

Talk to us about RAGDownload the RAG readiness checklist

No obligation. We protect your data and remove artefacts on request.