RAG Systems & Semantic Search
We don't just integrate RAG — we built our own toolchain for it. CogniGraph for knowledge graphs, Cognitive-OCR for document extraction, CogniGraph Chunker for intelligent text splitting. All Rust-native, all production-tested. When your project needs RAG, we bring tools we built and understand at the source code level.
Our RAG Toolchain
Three in-house Rust tools that cover the full RAG pipeline — from raw documents to graph-augmented retrieval.
CogniGraph
In-house · Rust Knowledge Graph Engine
Knowledge graph engine combining document storage, graph relationships, and vector embeddings in a single service. Pluggable backends (ArangoDB for production scale, embedded CozoDB for zero-config deployments). Four search modes: vector, semantic, hybrid BM25+RRF, and graph-augmented with multi-hop expansion. Sandboxed Lua scripting for server-side graph operations.
Cognitive-OCR
In-house · Cognition-Aware Document Extraction
Document extraction engine that triages before processing — a semantic map analyzes each page's role and content type, then applies the right strategy. Local ONNX models for layout detection (25 region classes) and OCR, vision-language models for complex pages. Supports PDF, DOCX, PPTX, Markdown, and images. A 9-page document that needs 9 VLM calls with brute-force tools may need only 3-5 with ours.
CogniGraph Chunker
In-house · Cognition-Aware Text Chunking
Four chunking strategies in one tool — fixed-size, delimiter-based, semantic, and cognition-aware with 8-signal boundary scoring (entity continuity, discourse structure, heading context, topic shift, orphan risk). Markdown-aware, 70+ languages, graph export for knowledge bases. Available as CLI, REST API, Python bindings, and Docker.
What is RAG?
Retrieval-Augmented Generation connects LLMs to your data — accurate answers grounded in your documents, not just general training data.
Why It Matters
- Responses based on your actual documents and data
- Up-to-date information without model retraining
- Source citations for every answer
- Sensitive data stays within your infrastructure
Our Approach
- Graph-augmented retrieval, not just vector similarity
- Cognition-aware chunking that preserves document structure
- Hybrid search combining dense vectors with BM25 sparse retrieval
- Source attribution and confidence scoring on every response
Hybrid Search Architecture
We combine multiple retrieval strategies to maximize relevance — no single approach works for all content.
Dense Vectors (Semantic)
- Captures meaning and context beyond keywords
- Handles synonyms, paraphrasing, cross-language queries
- OpenAI, Ollama, ONNX, and Cloudflare embedding providers
Sparse Vectors (Lexical)
- Exact keyword and terminology matching
- Product codes, identifiers, domain-specific terms
- BM25 via ArangoSearch, SPLADE where applicable
Graph-Augmented Retrieval
- Semantic search finds seed documents
- Graph traversal expands through typed relationships
- Context-aware results that follow the shape of your knowledge
Vector Databases
We match the database to the problem — not the other way around.
ArangoDB
Our primary choice for complex projects
Graph + document + vector in one database. AQL for complex queries, Foxx Microservices for server-side logic, native clustering. When your data has relationships that matter for retrieval, ArangoDB is where we start.
Qdrant
When pure vector performance is critical
Rust-based vector similarity search with payload filtering, distributed deployment, and exceptional throughput. Our choice for high-volume retrieval workloads where graph relationships aren't the primary concern.
LanceDB
Embedded, serverless-friendly
Zero-config vector database that runs embedded in your application. Ideal for edge deployments, prototyping, and use cases where running a separate database server adds unnecessary complexity.
Others
Weaviate · AstraDB · CozoDB
Weaviate for AI-native workflows with built-in vectorization. AstraDB for global distribution on Cassandra. CozoDB as an embedded alternative via CogniGraph's pluggable backend. Chosen based on your scale and infrastructure.
Document Processing Pipeline
RAG quality depends on ingestion quality. Our pipeline handles the hard parts — format detection, layout understanding, and intelligent splitting.
Format Support
- PDF with layout detection, OCR, and table extraction
- DOCX and PPTX with formatting preservation
- Markdown roundtrip parsing
- Images via VLM description
- HTML, CSV, JSON, and structured data
Chunking Strategies
- Cognition-aware with 8-signal boundary scoring
- Semantic chunking via embedding similarity
- Fixed-size with delimiter-aware boundaries
- Markdown-aware (tables, code blocks stay atomic)
- Cross-chunk entity tracking and graph export
Advanced Retrieval
- Query expansion and HyDE (Hypothetical Document Embeddings)
- Cross-encoder reranking for precision
- Contextual compression of retrieved passages
- Self-query metadata filtering from natural language
- Reciprocal Rank Fusion across retrieval strategies
Proven Implementations
RAG systems we've built and deployed in production.
Pharmaceutical Research
Fortune 500 — Clinical Document Intelligence
RAG system for drug development — clinical document analysis, healthcare professional insights, regulatory compliance checking, and real-world evidence extraction. Built to clinical-grade quality standards with GDPR and HIPAA compliance.
WolfGPT
WOLF GmbH — Enterprise Knowledge System
Multi-LLM knowledge system with intent analysis and dynamic routing. RAG over company documentation with tool-calling for data retrieval and complex document processing workflows.
TranscriptIntel
In-house — Conversation Intelligence
RAG-powered analysis of interview transcripts — speaker identification, behavioral pattern extraction, and executive report generation with evidence trails across thousands of documents.
PLU Finder
Incubated — Produce Intelligence
Knowledge base of 1,540+ PLU codes with AI-powered semantic search and MCP server integration. Retrieval-augmented answers about storage guidelines, nutritional data, and produce identification.
Ready to build your AI-powered knowledge base?
Contact us to discuss how RAG — built on tools we wrote ourselves — can transform how your organization accesses and utilizes information.
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