Bhavin Mistry. Enterprise AI
AI Platform

Enterprise AI Gateway Architecture

A unified reverse-proxy platform layer enforcing security, multi-provider model routing, semantic caching, token quotas, and audit logging across all enterprise apps.

SYSTEM TOPOLOGY & DATA FLOW ENTERPRISE SPECIFICATION
Client Application RBAC Context Security & Gateway PII Sanitization Semantic Cache Check Rate & Token Budget Inference & Rerank BM25 + Vector Fusion Cross-Encoder Top-5 Grounded Synthesis Telemetry OpenTelemetry

The Core Problem Solved

Uncontrolled proliferation of direct provider SDK keys, lack of central usage visibility, sudden cloud quota exhaustion, and unmonitored data egress.

When To Deploy This Architecture

Any enterprise organization with more than one team or application consuming commercial or open-source LLM endpoints.

Architectural Components

  • API Gateway Ingress (Kong / Envoy / Custom Reverse Proxy)
  • PII & Secret Redaction Engine (Regex + lightweight NER)
  • Semantic Cache (Redis vector store for exact and near-match queries)
  • Smart Dynamic Router (Cost, latency, and quota-aware fallback)
  • Token Budget & Quota Manager (Per-team cost attribution)
  • Comprehensive Audit Exporter (SIEM & FinOps telemetry)

Data Flow Narrative

Client SDK -> Internal Gateway -> PII Sanitization -> Semantic Cache Check (Return on hit) -> Dynamic Provider Route (Bedrock/Azure/Anthropic) -> Post-response Guardrail -> Audit Log -> Client Return.

Security & Perimeter Control

API keys never leave the secure secret manager; all prompt payloads stripped of credit card, tax file, and social security numbers prior to external dispatch.

Governance & Telemetry

Unified enterprise FinOps dashboard attributing token spend down to specific cost centers and Jira project keys.

Identified Failure Modes & Mitigations

Gateway latency overhead (>40ms); cache poisoning if semantic threshold is too loose; upstream provider outage cascading across fallback endpoints.

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