Neural Bus Architecture & Specs
A robust intelligence layer featuring <10ms Redis Stream ingestion, cost-optimized SLM pattern detection, and autonomous Model Context Protocol (MCP) execution.
System & AI Metrics
Event Ingestion
< 10ms (Redis XADD)
Rule-Based Detection
< 5ms (In-Memory Matching)
SLM Analysis Latency
< 500ms (Claude Haiku with Cache)
Remediation Execution
< 100ms via MCP Tool Dispatch
1. Event Ingestion & SLM Analysis
The Neural Bus replaces passive database logging with an active, real-time orchestration layer. - Real-Time Event Stream: Every module publishes its events onto a shared stream the moment they happen. Events are ingested in under 10ms and kept strictly isolated per tenant, preserving temporal context without any cross-tenant bleed. - Cost-Optimized Detection: Not every event requires expensive AI processing. Fast, deterministic rules catch known issue patterns — like an unusual burst of failed logins from a single source — instantly. Only novel, complex, or multi-module event clusters get routed to a Small Language Model (SLM) like Claude Haiku for deeper semantic intent classification. - Data Sanitization: Before any event reaches the SLM, the payload passes through a strict sanitization pass. Sensitive fields — passwords, tokens, credit-card-like strings — are redacted, guaranteeing zero PII leakage to external AI providers.
2. Autonomous Remediation (MCP)
Detecting a cascade failure is useless if you can't act on it. The Neural Bus connects detection directly to action via the Model Context Protocol (MCP).
- Remediation Plans: Once a pattern is confirmed, the Neural Bus drafts an execution plan mapping directly to available MCP tools (e.g., rate_limiter.block_ip or firewall.block).
- Approval Levels: Execution is strictly governed across three tiers. Low-risk actions (clearing cache, blocking an IP) auto-execute with no approval. Medium-risk actions (e.g., users.suspend) require standard admin approval via the UI queue. High-risk actions (e.g., data deletion, system configuration changes) require super-admin approval before they run. The threshold that separates auto-execute from queue-for-approval is configurable per tenant, so a security-conscious deployment can require human sign-off on actions that another tenant is comfortable automating — the AI's reasoning is attached to every queued plan so the approver decides with full context, not a blind yes/no.
- Rollback Resilience: Every remediation action ships with a rollback path. If an autonomous fix causes unintended degradation, the platform can instantly revert the state.
Frequently Asked Questions
Cost and latency. The large majority of patterns — known sequences like a burst of failed logins — are caught by deterministic in-memory rules in single-digit milliseconds with zero AI tokens. Only novel or multi-module event clusters that don't match a known rule get routed to an SLM like Claude Haiku for intent classification, so you pay for AI exactly where it adds value.
No. Before any event reaches an SLM, the payload passes through a strict sanitization pass that redacts sensitive fields — passwords, tokens, credit-card-like strings — so PII never reaches an external provider. AI usage also flows through the same platform governance, cost caps, and rate limits as every other AI call, with no standalone API key for the monitoring layer.
Only within the tier you configure. Low-risk actions (clearing cache, blocking a clearly-malicious IP) auto-execute; medium-risk actions like `users.suspend` need admin approval; high-risk actions like data deletion need super-admin approval. Every remediation also ships with a rollback path so an autonomous fix can be reverted if it causes unintended degradation.
Yes. Any module can register its own correlation rules and pattern responses on the shared bus — for example, the CRM module registers an "AI Deal Coaching Alert" rule that watches for its own `crm.deal.rotting` event on deals over $10,000 and escalates them for SLM analysis. Domain-specific intelligence lives in the module that owns it, not in a central config.
No. Pattern history, event windows, and the approval queue are held in distributed tenant state, so they survive restarts and stay strictly partitioned per tenant — nothing is lost and nothing bleeds across tenants.