JINAI — Production Multi-Agent AI Command Center
Architected from zero as the operational nervous system of a €10M+ B2B flooring enterprise. Production-grade. On-premise first. GDPR-native. 16/16 CI gates passing.
The Problem
A 20-person commercial team managed two national premium flooring brands (ARKITEK, SKUBA), 500+ B2B distributors, and €10M+ annual revenue across three disconnected systems: a legacy on-premise ERP, a cloud CRM, and two WooCommerce storefronts. Every order required triple manual re-entry. Price changes propagated via 5-person phone chains. Technical queries bounced through 3-day email threads. No automation. No single source of truth.
The Solution
JINAI is a production-grade multi-agent AI platform that became the single operational interface for the entire business — orchestrating inventory, orders, pricing, technical support, and marketing through a unified natural-language interface.
MCP Integration Layer
All external system access is mediated through a Model Context Protocol (MCP) gateway — a standardised, tool-callable interface that decouples AI agents from API schemas, auth flows, and data formats. Adding a new integration requires exposing a new MCP tool, not rewriting agent behaviour.
Zoho Bigin CRM
WooCommerce
Meta Business API
Google Ads (ready)
Qdrant RAG
Email IMAP
WhatsApp (ready)
Instagram (ready)
GDPR & EU AI Act Compliance
A deterministic sensitivity classifier intercepts every query before any LLM call. PII, financial data, and internal margins are hard-routed to Ollama (Llama 3.1) on-premise — zero cloud egress. Public queries cascade through a multi-provider AI Gateway with circuit breakers, automatic failover, and cost tracking per query. Dual audit trails: structured logs + human-readable knowledge vault. Article 12 compliant.
Hallucination as an Engineering Problem
Four compounding layers reduced hallucination from 26% to 6%:
- Corrective RAG (CRAG) — retrieves past errors before generation; semantic reranking by cosine similarity
- Episodic Memory — user preferences and corrections persist across sessions
- Bidirectional Guardrails — input sanitization (OWASP LLM01) + output numeric validation against PHC raw data
- Nightly RAGAS Evaluation — faithfulness, relevancy, precision metrics against golden dataset; 12 red-team tests on every CI push
Architecture Enforced by Code
Five fitness functions run as hard CI gates on every commit:
- No agent may import a database driver directly — AST-validated
- No agent may couple to more than three others — import graph analysis
@traceddecorator coverage >50% on sensitive modules- All client-facing agents must reference PII guardrails
- All ADR decisions must have implementation evidence
16/16 passing. Every deploy. Every time.
Business Impact
Production SLOs
| Metric | Target | Actual | Status |
|---|---|---|---|
| P90 Latency | <3000ms | ~420ms | ✓ |
| TTFT | <200ms | ~108ms | ✓ |
| MTTR | <5min | <1min | ✓ |
| Intent Accuracy | >90% | 92% | ✓ |
| Hallucination Rate | <10% | ~6% | ✓ |
Tech Stack
Backend: Python 3.12 · FastAPI · LangGraph · LangChain · Pydantic v2 · Asyncio
Data & AI: Redis · Qdrant (on-premise) · Ollama (Llama 3.1) · Gemini 1.5 Flash · DeepSeek · MLflow · RAGAS
Observability: OpenTelemetry · Jaeger · Langfuse · Structlog
Frontend: Next.js 14 · TypeScript · TailwindCSS · React Streaming
Infrastructure: Docker · Kubernetes · Terraform (AWS EKS) · GitHub Actions · MCP
Compliance: GDPR Article 12 · EU AI Act · OWASP LLM Top 10