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.
Three in-house Rust tools that cover the full RAG pipeline — from raw documents to graph-augmented retrieval.
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.
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.
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.
Retrieval-Augmented Generation connects LLMs to your data — accurate answers grounded in your documents, not just general training data.
We combine multiple retrieval strategies to maximize relevance — no single approach works for all content.
We match the database to the problem — not the other way around.
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.
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.
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.
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.
RAG quality depends on ingestion quality. Our pipeline handles the hard parts — format detection, layout understanding, and intelligent splitting.
RAG systems we've built and deployed in production.
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.
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.
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.
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.
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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