Traditional RAG Limitations
Traditional retrieval-augmented generation (RAG) often forces a choice between dense vector search (semantic meaning) and sparse keyword search (term frequency).
The Problem
More Data, More Problems
Anatypical unpacks every document into its entities and relationships, then connects them across everything your organization knows.
Your analysts have the data. They need the connections.
Intelligence products that don't reference each other. An analyst writes an assessment on a threat network without seeing a related product from a different unit completed two weeks earlier. The overlap only might on surfaces during a briefing, if at all.
Classified and unclassified data that can't meet. Critical context lives across classification boundaries. Analysts toggle between systems, manually carrying insights they can't formally connect, losing fidelity at every handoff.
Institutional knowledge locked in departing personnel. A senior analyst retires, and the operational understanding they built over a decade — sources, relationships between entities, historical context — leaves with them. The replacement starts from raw reporting.
Anatypical deploys into your infrastructure with full configurability, no external dependencies, and complete source traceability across every query and generation.
Technology
Open-Ontology
Our platform offers enhanced Hybrid GraphRAG capabilities, Knowledge Graph creation, and AI-memory management within deployable architecture with complete configurability.
Standard GraphRAG Challenges
GraphRAG utilizes Entity-Relationship extraction to create navigable Knowledge Graphs, but lacks scalability and requires defined ontologies.
Our Answer
Hybrid GraphRAG
Our Hybrid GraphRAG system utilizes each of these techniques while using an Open-Ontology method by clustering data and traversing through entity matching, semantic similarity, and relationship compression.
How We Build It
S.P.I.D.R. Architecture
1
Normalization Layer
Accepts over 50+ types of file extensions to be ingested into a single searchable data plane.
2
Modular Design
Allows for plugins such as video and audio transcribers to be utilized for multimodal inputs.
3
Cross-Platform Integrations
Pull directly from data sources such as Outlook, Teams, and Google Drive.
4
Vendor-Agnostic
Does not rely on any single provider for LLMs. Configure any commercial or open-source model.
Features
Built for Serious Research, Not Chatting
Long-term projects, ongoing investigations, data-intensive work with integrations, isolated memory, and entity-relationship detection, unlike one-off prompts that reset every session.
Source Traceability
Each generation is connected to the data points retrieved from the Knowledge Graph and Vector Space along with model perplexity, BM25, and cosine similarity scores.
Data-Centric Memory
Unlike ChatGPT or Claude, S.P.I.D.R. is data-centric. Every generation includes a Contextual Graph with full memory provenance control.
Real-Time Collaboration
Multiple users can work concurrently within the same "Space" to build and analyze data together.
Drag-and-Drop Canvas
The UI is built as a flexible canvas for intuitive interaction with your data and knowledge graphs.
Complete Configurability
Platform offers configuration parameters for LLM and retrieval. System orchestrated through k8s, auto-scales through KEDA.
Contextual Graphs
Users can view and edit which previous responses are linked to the current generation for full control on memory inputs.