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ADGTech

Data and Knowledge · Artificial Intelligence · Sovereign AI

Enterprise RAG Platform

Turn private knowledge into grounded intelligence.

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Available by demo

Overview

Private knowledge pipelines with grounded, cited answers.

The ADGTech RAG platform ingests enterprise content, indexes it with embeddings and keyword search, and retrieves only what a user is permitted to see — so models answer from your knowledge, with citations, without retraining.

Interactive preview

Explore Enterprise RAG Platform

An interactive demonstration using sample data. It illustrates the experience and does not connect to live systems.

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

Key features

Capabilities

Ingestion

  • Document parsing and chunking
  • Metadata extraction
  • Ingestion progress and error reporting
  • Scheduled knowledge refresh

Retrieval

  • Embeddings and vector index
  • Hybrid (semantic + keyword) search
  • Reranking
  • Metadata filtering

Governance

  • Document-level permission boundaries
  • Citations on every answer
  • Retrieval quality evaluation
  • Audit of queries and sources

Workflow

How it flows

  1. 01Enterprise files
  2. 02Processing
  3. 03Embeddings
  4. 04Knowledge index
  5. 05Retrieval
  6. 06LLM
  7. 07Grounded response

Use cases

Where it delivers value

  • Knowledge assistants

    Power ADG Chat and internal assistants with current, permissioned content.

  • Agent knowledge

    Give agents a governed retrieval tool rather than raw database access.

  • Research synthesis

    Answer questions across thousands of reports with traceable sources.

Architecture overview

How it's built

  1. Connectors

    Pull content from approved sources on a schedule.

  2. Processing

    Parse, chunk, enrich with metadata and ACLs.

  3. Index

    Vector + keyword index with permission metadata.

  4. Serving

    Retrieval API used by chat, agents and applications.

Integrations

  • Local files and folders
  • Document repositories
  • Approved cloud storage
  • Git repositories
  • Structured business data
  • Internal APIs

Deployment options

  • Hosted
  • Private cloud
  • On-premise / AI-in-a-Box

Available deployment options are confirmed per engagement.

Related products

Works well with

See Enterprise RAG Platform in action

Book a guided demo with the ADGTech team, tailored to your workflows and environment.

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