Ingestion
PDF, Office, text and HTML content and connected sources are imported, normalised and enriched with provenance and metadata.
Knowledge systems with verifiable sources
Make business knowledge precisely accessible
We develop RAG systems that semantically index distributed documents and data, respect access rights and deliver answers with traceable sources.
Discuss your knowledge base ↗More than uploading documents
Quality starts before the prompt
Retrieval-Augmented Generation connects a language model with approved business knowledge. Beyond the model, content must be captured fully, segmented meaningfully, permissioned correctly and retrieved precisely for each question.
evival considers the entire chain, from source to substantiated answer and ongoing quality measurement.
RAGsubstantiated answers
Vector DBsemantic search
ACLpermissions in retrieval
Technical pipeline
From raw document to reliable answer
Structure-based chunking
Content is segmented along headings, sections, tables and lists, rather than a fixed character count.
Embeddings & index
Sections are represented as semantic vectors and indexed in a vector database for semantic search.
Retrieval & re-ranking
Relevant sections are retrieved, ranked and assembled according to user permissions.
Answer & sources
The language model responds using the selected context. Source references enable expert verification.
Evaluation
Reference questions, retrieval metrics, answer quality and error patterns are measured; changes undergo controlled testing.
Architecture decisions
What determines RAG success in practice
Data quality
Duplicate detection, versions, freshness, tables and scanned documents affect results more than switching models.
Permissions
Permissions must restrict the search itself. A model must not see content the requesting user cannot access.
Context management
Chunk size, filters, re-ranking and context windows are adapted to document types and question patterns.
Updates
Changed sources are reindexed incrementally; outdated knowledge must remain identifiable and replaceable.
Model selection
Open or commercial models are selected by quality, data protection, latency, cost and operating model.
Verifiability
Answers without sufficient evidence must express uncertainty or escalate to a human instead of implying certainty.
Nexus Knowledge
The knowledge foundation of Nexus Intelligence Suite
Nexus Knowledge implements this RAG architecture as the platform foundation: semantic search, conversational queries, sources and role-based access. Other Nexus modules build on this knowledge base.
- Multi-format ingestion
- structure-aware segmentation
- Vector search and configurable re-ranking
- Source references linked to the original passage
- Document-level permissions and roles
- incremental updates
Your knowledge as a system
Which sources should finally be able to answer?
We assess your data, permissions and a suitable first use case.
Arrange a call ↗