Enterprise RAG & Knowledge Systems.
Ground artificial intelligence in your verified enterprise knowledge.
AKREVON engineers enterprise RAG and semantic search systems that retrieve exact answers from millions of documents, maintain source citations, and enforce strict role-based document security.
PRIVATE SOURCES · VECTOR INGESTION · GROUNDED ATTRIBUTION
PRODUCTION-READY CAPABILITIES
Production-ready rag & knowledge ai built for real-world use.
Eliminate hallucinations and surface trusted answers by connecting frontier LLMs to your documents, databases, and institutional knowledge.
Multi-Format Document Ingestion
High-throughput parsing of PDFs, Word docs, spreadsheets, code repositories, Notion pages, and enterprise wikis.
Hybrid Semantic & Keyword Search
Combining dense vector embeddings with sparse BM25 lexical search and reciprocal rank fusion for pinpoint retrieval accuracy.
Hierarchical Vector Storage & Indexing
Optimized vector databases (pgvector, Pinecone, Qdrant) partitioned by tenant, department, and document version.
Role-Based Document Permissions
Strict document-level access control ensuring employees only receive answers grounded in files their credentials permit.
Verifiable Citations & Source Linking
Every generated claim includes interactive footnote citations linking directly to exact page numbers and highlighted paragraphs.
RAG Triad & Context Evaluation
Continuous automated benchmarking evaluating Context Relevance, Groundedness, and Answer Relevance (Ragas / TruLens).
SEMANTIC GROUNDING PIPELINE
From enterprise knowledge to trusted answers.
Eliminate hallucinations by grounding conversational intelligence in your authoritative enterprise data. A 7-stage retrieval pipeline engineered for verifiable citations and strict access control.
Ingestion
→Automated connectors
Continuous ingestion from SharePoint, Google Drive, Notion, Confluence, PDFs, databases, and internal ticket repositories.
Chunking
→Hierarchical chunking
Document structure preservation that keeps tables, headers, and section context intact rather than blind token-splitting.
Embeddings
→Domain embeddings
Dense vector representations generated with models tuned for corporate terminology, acronyms, and industry syntax.
Retrieval
→Dense + sparse retrieval
Reciprocal Rank Fusion (RRF) marrying semantic vector similarity with exact BM25 keyword matching for superior recall.
Reranking
→Cross-encoder reranking
High-precision neural rerankers evaluate the top 50 candidates, filtering down to the most relevant context windows.
Citations
→Sentence-level grounding
Every generated claim is cryptographically linked to the exact source document, paragraph, and page timestamp.
Permissions
Role-based access filtering
User identity is passed to the retrieval layer so queries only retrieve and generate answers from documents the user is authorized to read.
STRATEGIC GUIDANCE
Four questions before grounding AI in enterprise knowledge.
Crucial guidelines for vector database selection, chunking strategies, access control list (ACL) propagation, and hallucination reduction.
How We Build Enterprise RAG Systems
We build hybrid vector-lexical search indices, recursive semantic chunking pipelines, re-ranking models, and contextual retrieval architectures with citation verification.
Why Build with Retrieval-Augmented Generation
RAG grounds LLM answers directly in your live enterprise documentation, spreadsheets, and databases, eliminating hallucinations and enabling real-time updates.
Why RAG & Knowledge AI for Your Organization
Institutional knowledge trapped across thousands of documents and communication channels becomes instantly searchable and actionable for every employee and customer.
Why AKREVON for RAG & Knowledge AI
We engineer hybrid dense-sparse retrieval systems with document-level security ACLs, dynamic re-ranking, and mathematically verifiable grounding.
FREQUENTLY ASKED QUESTIONS
RAG & Knowledge AI, answered.
How does RAG compare to fine-tuning a model?+
Fine-tuning teaches a model style, tone, or specific formatting; RAG gives the model factual knowledge and documents. RAG is cheaper, updates in real time, and provides verifiable citations.
Can RAG handle complex tables and charts inside PDFs?+
Yes. We use multimodal vision extraction to parse tables into structured Markdown and JSON, ensuring numerical values and comparisons remain intact.
How do you enforce security and permissions?+
We store document ACLs (Access Control Lists) directly alongside vector chunks. During retrieval, queries are pre-filtered to include only documents the authenticated user has permission to view.
Which vector databases does AKREVON recommend?+
We prefer pgvector for teams already on PostgreSQL for simplicity; Pinecone, Qdrant, or Weaviate for standalone enterprise vector scale.
Architect production AI with AKREVON.
Discuss enterprise architecture, vector database selection, token latency budgets, and security parameters with our principal AI engineers.