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ADR Group/Solutions

Enterprise AI Solutions

Choose the customer problem first—not the AI feature.

ADR Group’s AI solution pages focus on five recurring support problems: answering repeatable questions, finding the right business knowledge, handing a case to a person, adding customer channels and learning from conversation patterns.

Approved knowledgeConversation contextHuman handoffChannelsAnalytics
From question to next stepIllustrative workflow
Support workflowOne problem at a time
QuestionWhat is the customer actually asking?

Use conversation context rather than relying on one exact keyword.

EvidenceWhat information may the assistant use?

Retrieve relevant content from sources the business has approved.

BoundaryShould it answer, ask, hand off or run a workflow?

Keep judgement and sensitive actions behind explicit controls.

Solution areas

Five customer-conversation problems with different controls.

These are related capabilities, but they are not interchangeable. The best pilot candidate is the problem your support or service team can describe with real examples.

Routine questions

AI Customer Support

Answer repeatable questions from approved information and preserve a clear route to a person.

Explore Customer Support →
Business knowledge

Enterprise RAG

Find the relevant website, document or FAQ content before the model writes a business-specific answer.

Explore Enterprise RAG →
Exceptions

Agent Inbox & Human Handoff

Move a case to a person with the conversation and supporting context intact.

Explore Human Handoff →
Channels

Omnichannel Engagement

Keep knowledge, conversation state and handoff rules shared as additional customer channels are connected.

Explore Omnichannel →
Patterns

AI Analytics & Insights

See repeated questions, missing knowledge and handoff patterns that point to something worth improving.

Explore Analytics →

Pilot inputs

Bring examples from last week, not a list of AI buzzwords.

For a practical pilot, bring real customer questions, the information agents use to answer them, and the situations where a person or system action must take over.

Questions

Collect real requests

Use actual recurring support or service questions as the test set.

Sources

Name the approved information

Identify the websites, documents, FAQs or system data that can support an answer.

Boundaries

Mark the human cases

List approvals, exceptions, sensitive topics and actions that need explicit ownership.

Measure

Agree what good means

Review answer quality, handoff quality and recurring failure patterns rather than only counting messages.

First conversation

Bring the customer questions your team already sees.

We can use them to decide what should be answered, what needs more information and what should never be automated without a person or an authorised workflow.

Discuss an AI use case