Enterprise RAG
Connect AI responses to approved organisational websites, documents and knowledge sources.
- Knowledge ingestion
- Retrieval & evidence
- Grounded response design
AI & Data Solutions
Use AI where there is a real question, real data and someone accountable for the answer.
ADR Group works on business knowledge assistants, RAG, AI-assisted workflows, data integration and analytics. We start by asking who will use it, what information is trustworthy, what action—if any—should follow, and how the result will be checked.
Applied intelligenceQuestion → trusted source → AI response → checked next stepLet the model interpret language. Keep permissions, approvals and sensitive actions in normal application controls.
AI & data capabilities
Not every problem needs a chatbot—or even an LLM.
Sometimes the better answer is search, a dashboard, a rule-based workflow or an ordinary application. We use AI when language understanding or generation genuinely improves the task.
Business knowledge assistants
Give employees or customers a conversational interface to approved business information.
- Business scope
- Conversation context
- Human escalation
AI-assisted automation
Use AI to interpret requests while deterministic workflows control sensitive actions and completion.
- Intent understanding
- Structured handoff
- Confirmation & execution controls
Analytics & decision support
Turn operational data and recurring business questions into clearer information.
- Dashboards
- Trend analysis
- Operational insight
Data integration & preparation
Improve the movement, quality and structure of data required by analytics or AI workflows.
- Source mapping
- Data preparation
- Integration pipelines
AI integration
Connect AI capabilities to applications, enterprise systems and controlled business workflows.
- APIs & tools
- Application integration
- Audit boundaries
Delivery approach
Prove one use case before expanding the scope.
Keeping the first use case narrow makes knowledge, data quality, model behaviour, controls, user experience and operating cost easier to validate.
Business problem
Define user, decision and workflow.
Knowledge & data
Identify approved sources and ownership.
Intelligence layer
Choose retrieval, model and workflow patterns.
Quality & controls
Test grounding, edge cases, cost and latency.
Measure
Use telemetry and feedback to improve.
AI with controls
LLMs can understand a request. Business controls should decide what is allowed to happen.
Information answers and business actions should be treated differently. AI can interpret language and compose responses; sensitive actions need workflow rules, confirmation and execution evidence.
GroundingUse sufficient approved evidence when the use case requires factual business information.
ActionsDo not say booked, sent or submitted until the corresponding workflow has succeeded.
CostModel, context and tool usage should be observable so quality and cost can be managed together.
AI use case
Bring one real workflow and the information it depends on.
We’ll start from the users, source data, current process, risk, expected action and how success should be measured.