Direct prompting
- No access to private or internal knowledge
- Limited domain and organisational context
- Plausible but unverifiable statements
- Bounded by the model's training cutoff
- No permission model over enterprise data
Prototype · Concept validation · Not a production build
From Enterprise Data to Actionable Intelligence
An architecture-first approach to building grounded, context-aware LLM applications.
- LLM
- RAG
- Vector Search
- AI Agents
- Semantic Retrieval
- Enterprise Data
- Pipeline stages
- 10
- Retrieval strategy
- Hybrid + rerank
- Grounding
- Citation-enforced
retrieval_lattice.viz
simulated
sources
embeddings
grounded output
What we are building
A Context-Aware Intelligence Layer
The platform sits between enterprise data sources and the AI reasoning layer. It is responsible for knowing what exists, who may see it, which fragments matter for a given question, and how much of it should ever reach a language model. The model is the last step, not the product.
Interaction model
AI Intelligence Workspace
A representative workspace showing how a grounded answer is assembled: sources are scoped first, context is retrieved and ranked, then the model reasons over that context and streams a cited response. All content below is illustrative sample data.
Run the query to see how a grounded response is assembled, streamed and cited.
Chunks retrieved and reranked before generation. Only these fragments — not the full corpus — are placed in the model's context window.
Execution trace for the request. Timings are illustrative prototype values.
Pipeline
How the Intelligence Pipeline Works
Systems view
Production-Oriented Architecture
Two paths run through the platform: a synchronous request path that answers a user in real time, and an asynchronous ingestion path that keeps the knowledge layer current. Cloud infrastructure can be adapted to the deployment environment and client requirements.
Implementation concepts in scope
Comparison
Retrieval-Augmented vs. Standard LLM
The difference is not model quality. It is whether the model is allowed to answer from memory, or required to answer from your data.
Grounded generation
- Answers drawn from current, authorised enterprise content
- Every claim traceable to a retrievable source
- Knowledge updates by re-indexing, not retraining
- Access control enforced before the model sees anything
- Model provider can be swapped without losing knowledge
| Dimension | Standard LLM | RAG architecture |
|---|
Planned capability
Beyond RAG — Agentic Workflows
Retrieval answers questions. Agents complete tasks. In later phases a supervisor decomposes a request, delegates to specialised workers, and composes a verified answer from their outputs. This layer is designed but not yet implemented.
supervisor
Supervisor Agent
Interprets intent, plans the task graph, delegates, and decides when the result is complete.
Security posture
Isolation, Permissions and Minimum Context
Security architecture is designed around the principle that the LLM should only receive the minimum authorized context required to answer a request.
Observability
What We Measure
A retrieval system fails quietly: the answer still sounds fluent. These are the signals the platform is instrumented to expose so failures become visible.
Requests per minute
last 24 intervals · simulatedAll figures on this page are illustrative prototype values generated for demonstration. They are not measurements from a production deployment.
Where we are
Current Development Status
Each item is labelled by its actual maturity. Nothing conceptual is presented as delivered.
Rationale
Why This Architecture
One view
The Architecture in One View
An LLM is only one component.
The value comes from the system surrounding it.