ACCELERATOR
Agentic RAG Accelerator
Secure enterprise knowledge systems that connect LLMs to governed enterprise data, documents, systems, and workflows.
The problem it solves
Why this exists
Naive RAG leaks data, hallucinates, and degrades silently in production.
How it works
What it actually is
A reusable architecture plus delivery patterns covering the full knowledge pipeline — from ingestion to observed production behaviour.
Production RAG · ingestion → governance
A production RAG architecture.
How we design retrieval that stays governed, grounded, and observable in production — from ingestion through evaluation.
Ingest & govern
Parse and semantically chunk documents, then tag every chunk with permissions and lineage.
Retrieve with permissions
Hybrid dense + sparse retrieval that respects entitlements at the retrieval layer.
Rerank & ground
Cross-encoder reranking and context consolidation, with answers grounded in retrieved sources.
Generate with citations
Route to the right model, build the prompt, and return answers with source links.
Evaluate & observe
RAGAS-style evaluation, drift and cost monitoring, and feedback back into the pipeline.
Reference pipeline
Example output
Reference architecture.
Ingestion & indexing
Storage
Query & retrieval
Generation
Evaluation & governance
Illustrative reference architecture · representative stack, adapted per engagement · no client data shown.
Modules
What is inside.
- Knowledge ingestion
- Permission-aware retrieval
- Source grounding
- Agent orchestration
- Response evaluation
- Observability
- Feedback loop
What you receive
- Reference architecture and design records
- Working governed retrieval pipeline
- Evaluation baseline
- Operating runbooks
Request an Agentic RAG Walkthrough.
We will walk through the architecture and how it maps onto your environment.