Most enterprise RAG deployments treat context as an automated pipeline output and are invisible to the domain experts who must act on the result. enterpriseRAG makes context a transparent, versioned, auditable artefact assembled through an iterative and structured human-in-the-loop workflow.
Transparent · Versioned · Audit-ready · IterativeSource content
Process
processing
LLM ready
Build context
Production RAG deployments fail at scale for reasons that retrieval technology alone cannot resolve.
The standard RAG pipelines which embed documents, store vectors, retrieve by cosine similarity, inject into prompt is straightforward and works for prototypes. The failure modes emerge in production, and each traces to a structural gap in automated context assembly.
Precision degrades at scale
As corpus size grows from thousands to millions of documents, similarity search precision degrades. Retrieved chunks fill the prompt context with noise rather than signal.
Retrieval has no knowledge of intent
The same query issued for two different business purposes should retrieve different documents. An automated retrieval system cannot make that distinction without human guidance.
Chunk boundaries sever business meaning
Chunk boundaries set at fixed token counts rarely align with business meaning. Key facts are split; relationships between figures in adjacent paragraphs are severed.
No audit trail, no accountability
No audit trail connects a generated output to the document fragments that informed it, making quality assurance and regulatory compliance difficult to demonstrate.
The people best positioned to judge whether retrieved content is relevant, accurate, and appropriately scoped are domain experts and executives, the same people who will act on the output. Most RAG implementations exclude them entirely from context construction. This structural mismatch, not retrieval technology, is the primary cause of unreliable results on enterprise tasks.
A six-step workflow from knowledge ingestion to versioned, human-curated context delivery.
enterpriseRAG structures retrieval as a deliberate, human-governed process from initial knowledge processing through stakeholder curation and versioned output. Steps 1–4 and 6 run within the platform; generation and evaluation (step 5) happen in your LLM orchestrator of choice.
The platform implements eight discrete capabilities, each tracing to a documented enterprise requirement.
Features are not additive. Each capability addresses a specific failure mode in automated RAG or a constraint imposed by regulated enterprise deployments.
Access control and IAM integration
Integrates with Microsoft Entra ID and Google Workspace. Document-level permissions for Read, Modify, Delete, Destroy, Share, Process, etc are inherited rather than re-implemented. Any output inherits the union of restrictions on all source documents that contributed to it.
Enterprise content management
Two complementary tiers: a centrally governed repository and user-owned private workspaces. Documents pass through a five-stage processing pipeline for chunking, summarisation, keyword extraction, entity extraction, and image/table extraction before any user interacts with them.
Project-based task management
A Project captures a single intent and contains all artefacts associated with pursuing it: document scope, collaborators, context build history and generated outputs. Projects are deliberately isolated from one another for audit integrity.
Context construction and retrieval
Rather than injecting top-k results, the platform provides a structured workspace to build the context. Users select, reject, reorder, and annotate context elements. A context summary can be generated before submission, making explicit what the LLM will see.
Versioning and state management
Each saved state captures: selected context elements and generated output. States can be compared side-by-side. Any prior state can be restored. Branching allows parallel exploration of different context strategies for the same task.
Collaborative workflows
Projects can be shared with other stakeholders who contribute to the context construction within their own access boundaries. A user with access to a restricted collection can contribute from it; collaborators without that access see that those elements exist but cannot access the underlying documents.
LLM and orchestration flexibility
No constraint on model or orchestration layer. Works with public LLM APIs such as OpenAI, Anthropic, Google OR with privately hosted open-weight models within the enterprise boundary, hybrid configurations, and any orchestration framework via standard interfaces.
Audit, traceability, and observability
All actions including document access, context selection, output generation are logged with user identity and timestamp. Every generated output carries a provenance record linking it to the specific chunks, images, and tables that contributed to it.
Four principles, each a direct response to a documented failure mode in enterprise RAG deployments.
Human primacy
Retrieval is a starting point for human judgment, not a replacement for it. Stakeholders inspect, approve, curate, and construct the context before it reaches the LLM. The platform does not attempt to automate this judgment but it creates the conditions under which it can be exercised efficiently.
Transparency
Users see exactly what will be submitted to the model. There are no black-box retrieval steps whose outputs are invisible to the person who initiates the query. Every generated output carries a provenance record linking it to the specific documents that informed it.
Permission fidelity
Access controls on source documents propagate to every derived artefact: context snapshots, prompt configurations, and generated outputs. A report compiled from a Confidential document cannot be shared with a recipient lacking that clearance. Such restrictions are enforced at the platform layer.
Model/Prompt agnosticism
The platform imposes no constraint on which Prompt, LLM or LLM orchestration engine processes the final context. Teams choose the prompts and models that fits their requirements and capabilities. This can be public APIs, privately hosted open-weight models, or hybrid routing based on context sensitivity.
enterpriseRAG operates as the context engineering layer between enterprise knowledge and AI inference.
It is not an LLM, an embedding model, a vector database, or an orchestration framework. Its function is to ensure inference operates on the right information, assembled by the right people, within the access boundaries already governing those source documents.
Automated retrieval cannot access the domain knowledge that is not encoded in any document.
Domain experts and executives carry knowledge about their organisation that no document captures: which data sources are reliable, which figures are preliminary, which analyses require corroboration from a second source. This judgment is currently excluded from the AI pipeline entirely.
The Human-in-the-Loop workflow is the mechanism by which this tacit knowledge enters the AI pipeline in a controlled, documented, and repeatable way. The platform does not attempt to automate this judgment. It creates the conditions under which it can be exercised efficiently, with a full audit record of every decision made.
Five constraints that distinguish enterprise RAG deployments from research and prototype implementations.
Access control, multi-stakeholder workflows, auditability, model routing, and data variety are requirements that enterprise organisations cannot defer. enterpriseRAG was designed around these constraints from the outset, not retrofitted to them.
Source documents carry confidentiality levels. Any output derived from a restricted document must inherit that restriction.
Permission inheritance calculator derives output access level from the full set of source permissions. Enforced at the platform layer, not left to user discretion.
Complex analytical tasks require input from multiple people with different roles and clearances.
Projects as shared workspaces with per-user and per-group role assignment. Stakeholders contribute from their own access domain and no permission escalation is possible.
Regulated industries require a durable record of what information was provided to an AI system and who authorised its use.
All actions logged with user identity and timestamp. Every output carries a provenance record. Audit export API for enterprise SIEM and compliance tooling.
Data residency or confidentiality requirements may prevent routing sensitive context to public LLM endpoints.
Full support for locally hosted models and hybrid routing. Sensitive context stays within the enterprise boundary; routing is configurable per context sensitivity level.
Enterprise corpora span structured databases, unstructured documents, images, tables, and metadata which are often in the same query.
Multi-perspective document representations: vectors, knowledge graphs, keyword indexes, extracted images and tables, structured data connectors, temporal and geographical facets.
| Constraint | Enterprise requirement | How enterpriseRAG addresses it |
|---|---|---|
| Access control | Source documents carry confidentiality levels. Any output derived from a restricted document must inherit that restriction. | Permission inheritance calculator derives output access level from the full set of source permissions. Enforced at the platform layer, not left to user discretion. |
| Multi-stakeholder workflows | Complex analytical tasks require input from multiple people with different roles and clearances. | Projects as shared workspaces with per-user and per-group role assignment. Stakeholders contribute from their own access domain and no permission escalation is possible. |
| Auditability | Regulated industries require a durable record of what information was provided to an AI system and who authorised its use. | All actions logged with user identity and timestamp. Every output carries a provenance record. Audit export API for enterprise SIEM and compliance tooling. |
| LLM flexibility | Data residency or confidentiality requirements may prevent routing sensitive context to public LLM endpoints. | Full support for locally hosted models and hybrid routing. Sensitive context stays within the enterprise boundary; routing is configurable per context sensitivity level. |
| Data variety | Enterprise corpora span structured databases, unstructured documents, images, tables, and metadata which are often in the same query. | Multi-perspective document representations: vectors, knowledge graphs, keyword indexes, extracted images and tables, structured data connectors, temporal and geographical facets. |
Discuss deployment requirements with a solutions engineer.
enterpriseRAG supports cloud, VPC, and air-gapped deployments. Bring your data source inventory, access control model, and LLM preference, we will scope the deployment accordingly.