Sovereign Agentic Document Ai
RAGNR gives regulated teams local inference, hybrid retrieval, graph reasoning, and compliance-ready answers without sending documents or prompts to outside AI APIs.
Why RAGNR exists
The market keeps asking you to choose between capability and control
Cloud RAG platforms ask for your documents, your prompts, and your operational trust. Open-source frameworks ask for months of integration work and permanent maintenance overhead. RAGNR closes the gap with a production-ready platform that stays on your infrastructure.
Cloud risk
Keep the model traffic inside your boundary
Every answer, embedding, and safety decision runs locally. Sensitive data stays on your network even when retrieval gets complex.
Product, not kit
Skip the framework assembly project
Document ingestion, re-ranking, graph extraction, observability, compliance, exports, and UX already fit together.
Operator confidence
See what the system is doing
Pipeline progress, GPU metrics, query history, citations, violations, and performance tuning are visible and actionable.
Deployment realism
Run on the hardware you actually own
Development on Apple Silicon, enterprise inference on NVIDIA, and air-gapped delivery are built into the operating model.
Platform capabilities
The stack behind every grounded answer
RAGNR is not a wrapper around scattered components. It is a single product that ships retrieval, graph reasoning, safety, and deployment control as one system.
Sovereign deployment
Local inference without the trust leak
Inference, embeddings, and multimodal processing stay in your environment. RAGNR auto-selects the right model posture for the hardware profile instead of pushing requests to outside providers.
vLLM-first inference with hardware-aware model selection
Apple MLX development flow and multi-GPU enterprise deployment
Offline export and air-gapped deployment support
Hybrid retrieval
More than semantic search
RAGNR combines vector recall, keyword search, document summaries, query expansion, decomposition, and LLM re-ranking into one retrieval engine tuned for answer quality.
Parallel vector, keyword, and summary retrieval with RRF merge
LLM re-ranking and MMR diversification
Hybrid, vector-only, keyword-only, and graph-based modes
GraphRAG
Entity-aware reasoning across documents
The ingestion pipeline extracts entities and relationships so retrieval can follow people, organizations, concepts, and communities rather than relying on chunk similarity alone.
Knowledge graph triplets and community detection
Interactive graph exploration and relationship traversal
Useful when exact wording differs but the concept is connected
Guardrails + compliance
Grounded answers with enforceable controls
RAGNR does not stop at retrieval. It checks for prompt injection, redacts risky output, scores factual consistency, and maintains an audit-friendly system posture.
Prompt injection detection plus configurable topic controls
Hallucination scoring, span highlighting, and correction paths
Compliance mode with append-only audit trails
System proof
Architecture that ships with the product
System topology
Ingress
React frontend
Vite + Tailwind + shadcn/ui
Control plane
Express.js backend
TypeScript + Prisma ORM
REST API
Highlights, webhooks, cloud bucket connectivity
Pipeline
6-stage ingestion
Retrieval
Hybrid + RRF + rerank
Agent Engine
ReAct loop + tools + Lambda functions
Storage + queue
Inference layer
Complete guard rails
Ingress path
HTTP + SSE from frontend into the control plane
Local inference
All LLM, embedding, and vision work stays on operator hardware
Quad index
Vector, keyword, graph, and tree retrieval live inside the same platform
Trust model
No external AI APIs, append-only auditability, self-hosted boundary
Ingestion path
Upload → 6-stage pipeline → quad index: vector, keyword, graph, and tree.
Query path
Hybrid recall → RRF merge → LLM rerank → context assembly → answer generation.
This architecture image comes directly from the existing RAGNR collateral and anchors the site in the real product topology: frontend, API, retrieval engine, sidecars, Postgres/pgvector, Redis, and local inference services.
Deployment posture
Operator mode
Self-hosted by design
Run the platform locally in development and deploy the full enterprise stack behind your own proxy in production.
Deployment mode
Air-gapped ready
Export projects and deploy them offline when the environment does not allow internet-connected inference.
Visibility
Live monitoring
Track GPU, CPU, memory, pipeline progress, query history, and performance tuning from the product itself.
Quality
Evaluation framework
Measure retrieval quality with built-in runs for precision, recall, groundedness, and relevance.
pgvector + GIN
Dual index core
Vector retrieval and full-text search run side-by-side instead of forcing one recall strategy.
SSE-first
Response delivery
Streaming is built into the answer path so conversational UX stays responsive under heavier workloads.
Append-only
Audit posture
Compliance mode preserves traceability for organizations that need answer provenance and reviewability.
GPU-aware
Model selection
Hardware-adaptive sizing keeps deployment grounded in available VRAM rather than wishful defaults.
Trust posture
Built for buyers who need conviction, not another AI promise
The RAGNR pitch is simple: keep the speed and power of modern RAG, but stop outsourcing your security model, governance model, and data boundary.
Alternative
Cloud RAG platforms
Fast to start, but the trust model rarely matches regulated workflows. Sensitive corpora, prompts, and operational metadata still cross your boundary.
Vendor dependency at the exact layer you may need to control most
Harder to satisfy data residency and air-gap requirements
Security review expands to include someone else’s inference stack
Alternative
DIY framework stacks
Frameworks give you ingredients, not a finished platform. Teams still need to assemble retrieval, orchestration, safety, observability, and UI.
Long integration path before business users can evaluate anything
Operational ownership spreads across too many moving parts
Every improvement becomes another engineering project
RAGNR posture
One product, one perimeter
RAGNR is for teams that need modern RAG capability and an answerable security story at the same time.
Purpose-built for self-hosted retrieval, graph reasoning, and guardrails
Deployment model supports enterprise, edge, and air-gapped realities
UI, APIs, evaluation, and operations are already part of the system
Where it fits
The shortlist for regulated, security-minded, high-context teams
RAGNR is designed for organizations where document intelligence is valuable, but external model dependencies are unacceptable.
Healthcare
Clinical and operational knowledge access
Support internal research, policy lookup, and document-grounded assistance without exposing protected information to external model providers.
Legal
Matter intelligence with traceable citations
Use hybrid retrieval and graph connections to navigate clauses, precedents, and entity relationships while keeping documents under firm control.
Defense + public sector
Air-gapped and security-led environments
Deploy where outbound model traffic is unacceptable and where infrastructure decisions must align with sovereignty requirements.
Financial services
Policy-heavy, compliance-aware workflows
Bring together handbooks, procedures, customer documents, and operational data under one governed retrieval layer.
Join the Waitlist
Bring your architecture, your data boundary, and your toughest questions
We will use the call to map your deployment posture, retrieval needs, and compliance constraints to a concrete RAGNR rollout path.
Map your document sources, model boundary, and deployment posture.
Review where GraphRAG, hybrid retrieval, and guardrails matter most in your workflow.
Leave with a concrete evaluation path rather than a generic product pitch.