Three-Layer AI Architecture
Our AI stack spans knowledge-connected reasoning and production-grade edge inference — each engineered for its operational environment. A sanitized intelligence feed connects them in real time. Self-hosted. Security-first. No customer data exposure.
AI Security Engine: Three Layers
Purpose-built systems for knowledge reasoning, production security inference, and real-time intelligence
Layer 1 — Knowledge Reasoning (RAG)
Retrieval-augmented generation grounded in versioned documentation, solutions content, release notes, and the live intelligence feed.
- Automatic re-indexing from versioned docs
- Self-hosted Mistral-class LLM (Ollama)
- pgvector semantic search
- Context-grounded responses only
- Stateless — never trains on user input
Layer 2 — Production Edge ML
ONNX inference on QuickSecure endpoint agents. Detects threats, classifies behaviors, and executes autonomous decisions at the edge.
- ONNX Runtime on endpoint agents
- Telemetry → labeling → retraining pipeline
- Canary + Stable promotion system
- Autonomous decision projection
- Drift detection and model governance
Layer 3 — Live Intelligence Feed
A real-time, read-only intelligence plane connecting the two layers via aggregated, sanitized signals. Updated every 3 minutes.
- Active model + canary version status
- 7-day precision, recall, FPR metrics
- Drift severity + PSI score
- 24h event volume + containment rate
- No raw telemetry or customer data
Intelligence Feed — Not Training on Customer Data
How the two layers inform each other without compromising data boundaries
/api/ai/intel/feed) from the production ML pipeline — aggregated metrics, model status, and anonymized signals. Updated every 3 minutes.
Docs → Knowledge Index
Static website content, product documentation, and public guides are automatically re-indexed into the engine's vector store on every content version change.
Live Intelligence Feed
The Edge ML pipeline publishes a real-time, aggregated intelligence summary — active model version, drift status, detection metrics, containment rates, and fallback distribution. The AI Security Engine ingests this feed as context, enabling it to answer questions about current system state.
Data Boundary Enforcement
Customer endpoint telemetry remains in the security pipeline. The intelligence feed contains only aggregated, anonymized summaries — no file hashes, command lines, feature vectors, or customer-identifiable fields cross this boundary.
Feedback Loop Architecture
Detection accuracy metrics and threat landscape summaries flow back to documentation and knowledge bases. This keeps the AI Security Engine current on capabilities — without exposing operational data.
Production-Grade AI, Not Prototypes
All three AI layers run in production on Corxor infrastructure. The knowledge reasoning layer serves real users with grounded, source-cited responses. The Edge ML system processes live telemetry and makes autonomous security decisions. Every component is monitored, governed, and auditable.
- Self-Hosted: Mistral-class LLM on our own infrastructure. No third-party API calls for inference. Full data sovereignty.
- Governed ML Pipeline: Model registry, drift detection, canary deployment, signed model versions, and immutable audit logs.
- Explainable Decisions: Every AI judgment — from knowledge answers to threat verdicts — carries a reasoning chain that can be reviewed and audited.
- Continuous Indexing: The knowledge layer automatically re-indexes on content changes. The security ML pipeline retrains on curated, labeled data.
AI in Production
Operational systems delivering measurable results
Knowledge Reasoning (Live)
RAG-powered engine answering questions from versioned documentation, product guides, curated resources, and the live intelligence feed. Self-hosted Mistral model with pgvector retrieval.
Endpoint Threat Detection (Live)
ONNX models running on QuickSecure agents classify process behaviors, file operations, and network patterns in under 15ms. Autonomous quarantine and remediation.
ML Governance Pipeline (Live)
Full model lifecycle: versioned registry, shadow evaluation, canary deployment, drift monitoring, and automated rollback. Every model change is signed and auditable.
Live Intelligence Feed (Active)
A real-time, sanitized API feed publishes aggregated system state — model versions, drift status, detection metrics, and operational signals — directly into the engine's context. No raw customer data crosses this boundary.
AI Security Engine — Try It Now
Our AI Security Engine is powered by a self-hosted Mistral-class model, grounded in indexed documentation, and connected to our live production intelligence feed. Click the icon in the bottom-right corner.
Self-Hosted LLM
No third-party API dependency. Runs on our infrastructure with Ollama. Full data sovereignty.
RAG-Grounded
Responses are grounded in indexed documentation and versioned content. No hallucination-prone open generation.
Multilingual
Supports English, Turkish, and additional languages with natural conversation flow.
Security Guarantee
Designed for operational environments. Engineered for trust.
Ready to Integrate AI Into Your Security Operations?
Whether you need a knowledge-connected reasoning engine, an edge ML pipeline, or a live intelligence feed — let's discuss how our AI Security Engine can address your specific requirements.