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Svorus LabInternal R&D concept

Enterprise RAG Copilot: document intelligence lab

Enterprise RAG Copilot is a Svorus Lab concept for private document intelligence with ingestion, hybrid retrieval, cited answers, governance, and evaluation loops.

Client label

Svorus Lab

Enterprise RAG Copilot: document intelligence lab

Challenge

Enterprise teams often have document-heavy workflows where useful answers depend on permissions, source freshness, citations, and retrieval quality. A simple chat interface over uploaded files is not enough when the system must be trusted for operational decisions.

Approach

The lab concept proposes a document intelligence platform with ingestion pipelines, hybrid search, reranking, access control, cited answer generation, source health monitoring, and evaluation datasets for groundedness and retrieval coverage.

Technical profile

Architecture, capabilities, and implementation surface

Platforms

  • Document ingestion service
  • Search and answer workspace
  • Admin governance console
  • Evaluation harness

Technology

  • LangChain
  • LlamaIndex
  • Next.js
  • FastAPI
  • PostgreSQL
  • pgvector
  • OpenSearch
  • S3-compatible storage
  • OpenTelemetry
  • Docker

Key features

  • PDF, policy, contract, SOP, spreadsheet, and knowledge-base ingestion
  • Chunking, metadata extraction, embeddings, hybrid search, reranking, and source citation
  • Permission-aware answer generation with source freshness and confidence signals
  • Document Q&A, clause lookup, comparison, summarization, and action-item extraction
  • Evaluation workflows for groundedness, retrieval quality, citation quality, and refusal behavior

AI capabilities

  • Retrieval-augmented generation over private enterprise documents
  • Hybrid semantic and lexical retrieval with reranking
  • Citation-grounded answer synthesis with source snippets and page references
  • Document classification, entity extraction, and comparison workflows
  • Regression evaluation for hallucination, stale context, and missing-source behavior

Architecture highlights

  • Ingestion pipeline separates parsing, normalization, chunking, embedding, indexing, and retention policy
  • Retrieval layer supports metadata filters, access-control checks, freshness checks, and source attribution
  • Answer service stores query, retrieved context, citations, model settings, latency, and user feedback
  • Admin console exposes collection health, failed ingestions, stale documents, evaluation runs, and permission gaps
  • Storage model keeps raw files, extracted text, chunk metadata, embeddings, and audit logs separately

Engineering challenges

  • Preventing answers from crossing user or department permission boundaries
  • Keeping citations useful when source files change, expire, or are re-indexed
  • Balancing retrieval recall with answer precision across messy enterprise documents
  • Designing evals that measure grounded answers instead of pleasing language alone

References

Research and project links

LangChain RAG conceptsLlamaIndex documentation

Results

What this entry is meant to prove

Lab concept

original Svorus R&D direction, not client work

Cited RAG

answers grounded in retrieved sources, metadata, and permission checks

Governed

document lifecycle, access policy, source freshness, and evaluation built into the workflow

Services involved

Capabilities behind this work

Agentic AI and Applied AI EngineeringProduction AI agents that plan, use tools, and act with evaluation, logging, and human oversight.Selected capabilitiesAgent design and orchestrationRetrieval and knowledge systemsEvaluation and guardrailsExplore serviceProduct Engineering and Custom SoftwareWeb, mobile, and platform engineering from discovery through production with one accountable team.Selected capabilitiesProduct strategy and discoveryWeb application engineeringMobile engineeringExplore serviceCloud, DevOps and Platform EngineeringCloud migration, CI/CD, observability, reliability, and platform foundations built for on-call reality.Selected capabilitiesCloud migration and modernizationCI/CD and DevOps automationSite reliability engineering and observabilityExplore serviceData Engineering and AnalyticsTrusted pipelines, data platforms, BI, and MLOps foundations for products, dashboards, and AI agents.Selected capabilitiesData pipeline and integrationData platform modernizationAnalytics and BIExplore serviceManaged Services and SupportApplication, cloud, and AgentOps support from the team that understands what was built and why.Selected capabilitiesApplication managed servicesCloud infrastructure managementAgentOps monitoringExplore service

Industry context

Market connected to this work

Logistics and Supply ChainOperational software, data, and AI for visibility, routing, and exception handling.

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