Skip to content
ADGTech

Enterprise RAG

Private knowledge, grounded intelligence.

Retrieval-augmented generation lets models answer from your documents and systems without retraining — with citations and your access rules intact.

RAG Platform productADG Chat

RAG pipeline demonstration

Enterprise RAG pipeline

Ingest · index · retrieve · ground

Demonstration · sample data
  1. Enterprise filesApproved sourceswaiting
  2. ProcessingParse · clean · chunkwaiting
  3. EmbeddingsVectorise chunkswaiting
  4. Knowledge indexVector + keywordwaiting
  5. RetrievalPermission-awarewaiting
  6. LLMPrivate or approvedwaiting
  7. Grounded responseWith citationswaiting

Ingestion · sample run

Files
0
Chunks
0
Vectors
0

Sample question

Ask as

Metadata filters applied

  • department: Finance
  • doc_type: procedure
  • status: current

Retrieved chunks

3 permitted · 1 blocked

  • 1

    Month-end close procedure (sample)

    §2 Timeline

    Close begins on working day 1 with accrual review…

    0.91
  • 2

    Finance team handbook (sample)

    §5 Reconciliations

    Bank and ledger reconciliations are completed by day 3…

    0.87
  • 3

    Company calendar (sample)

    Key dates

    Finance close windows are published each quarter…

    0.72

  • Board pack — Q3 (sample)

    Appendix B

    Blocked by permission boundary — content not sent to the model

    0.69

Grounded answer · sample

The sample procedure runs over five working days: accrual review on day 1, reconciliations by day 3, management review on day 4 and sign-off on day 5. Close windows for each quarter are published in the company calendar. 123

Principles

What makes RAG enterprise-grade

Grounded, not guessed

Answers are generated from retrieved passages and cite them, so people can check the source.

Permissions travel with the data

Document-level access rules are indexed alongside content; retrieval only returns what the user may see.

Fresh by design

Connectors refresh on a schedule, so assistants reflect current policy and documentation.

Measured quality

Retrieval and answer quality are evaluated against question sets, not assumed.

Connectors

Knowledge sources

The sources a deployment can connect to are agreed during scoping.

ConnectorTypical contentStatus
Local files & foldersFile shares, exported foldersConfirm with ADGTech
Document repositoriesDocument management systems, intranetsConfirm with ADGTech
Approved cloud storageOrganisation-approved storage buckets and drivesConfirm with ADGTech
Git repositoriesSource code, READMEs, runbooksConfirm with ADGTech
Structured business dataDatabases, data warehouses, spreadsheetsConfirm with ADGTech
Internal APIsIn-house services exposing content or recordsConfirm with ADGTech
Connector availability varies by deployment and is confirmed during scoping.

Connect your knowledge to AI — safely

Talk to ADGTech engineers about models, knowledge, agents and where your AI should run.

Book an AI Strategy DemoRequest Technical Architecture