Enterprise AI Orchestration — Governed, Auditable, Production-Ready
Inflexis develops the AIXaaS™ platform (AI-as-a-Service) — enterprise AI orchestration infrastructure that gives organizations governed, auditable, multi-agent intelligence on their own data, deployed inside their own Azure tenant.
Where most AI tools give you a chatbot, AIXaaS™ gives you an orchestration layer: multiple specialized agents, a structured compliance engine, a hybrid RAG knowledge base, and a full audit trail — with your data never leaving your control.
MAO (Multi-Agent Orchestration) is the core engine behind AIXaaS™ — a production-grade system designed around a single principle: aviation-grade dual redundancy. Every agent handoff produces an auditable decision record. Every stage has a validated fallback. No single point of failure.
🏗️ 10-Layer Architecture
Layer 0 Foundation Deterministic compliance detection (23 frameworks, zero LLM cost)
Layer 1 Agent Architecture 8-role RBAC: admin → founder → cro → marketing → developer → member → client → demo
Layer 2 LLMs & APIs 6-tier cost routing: Ollama → Gemini → GPT-4.1-nano → Haiku → GPT-4.1-mini → GPT-4o → Sonnet
Layer 3 Tool Use Universal Ingestor (20+ file types), Compliance Engine, PII masking
Layer 4 Agent Frameworks FastAPI, Claude Agent SDK, MCP server (8+ tools for Claude Desktop)
Layer 5 Orchestration DAG parse → PII mask → compliance scan → chunk → store (ADR logged at every stage)
Layer 6 Memory Management 4-tier hybrid storage, disk persistence, semantic response cache (30–60% cost reduction)
Layer 7 Knowledge & RAG Hybrid BM25 + dense vector retrieval, sector governance, namespace isolation
Layer 8 Deployment Azure Container Apps, GitHub Actions CI/CD, custom domains, managed TLS
Layer 9 Monitoring ADR audit trail, guardrails (token budget + loop caps), App Insights
Layer 10 Security & Gov. Change control with diff review, archive/rollback, PII masking, sector RBAC
📦 Core Capabilities
| Capability | Description |
|---|---|
| Multi-Agent Orchestration | Specialized agents coordinated via a structured DAG pipeline |
| Hybrid RAG | 4-tier knowledge retrieval: keyword → semantic vector → cloud vector → Azure AI Search |
| Zero-Token Compliance | 23 regulatory frameworks detected deterministically — no LLM API cost |
| 20+ File Types | PDF, DOCX, PPTX, XLSX, audio recordings, YouTube, web pages, and more |
| ADR Change Control | Every agent instruction change is proposed, reviewed, versioned, and recoverable |
| Meeting Intelligence | Auto-transcription, SPICED sales scoring, per-attendee summaries, CRM sync |
| Cost-Aware Model Routing | Routes to cheapest model meeting quality requirements — ~98% gross margin at scale |
| Azure-Native | Container Apps, Key Vault, Entra SSO, AI Search, Blob — production from day one |
| Sub-Agent Isolation | Namespace-scoped execution with RBAC boundaries — structured task decomposition across agent roles |
| Token-Aware Payloads | Zero-token compliance detection + semantic cache reduce LLM spend by 30-60% without quality loss |
| ZeroTrusted SOAR Integration | 57+ AI security agent compatibility — anomaly detection, threat classification, automated response orchestration |
| URL | Purpose | Status |
|---|---|---|
| app.inflexis.ai | Employee portal — internal KB, compliance, meeting intelligence |  |
| app.aixaas.org | Client portal — isolated tenant environments | |
| Demo Hub | Interactive demos — runs in browser, no login required | |
Both portals run on Azure Container Apps with Azure-managed TLS, Entra SSO, and GitHub Actions CI/CD (~5 min deploy on every git push main). The demo hub is hosted on GitHub Pages.
| Repo | Type | Description |
|---|---|---|
| aixaas-docs | 📖 Public | Platform documentation, API reference, architecture guides, integration specs |
| mao-examples | 💻 Public | Integration examples, Python scripts, Jupyter notebooks, interactive demos |
| mao-platform | 🔒 Private | Core AIXaaS™ engine — proprietary MAO platform source |
| team-workspace | 🔒 Private | Internal design docs, architecture pages, revenue models, client materials |
Cloud Azure Container Apps · ACR · Key Vault · AI Search · Blob · Files · Entra · App Insights
AI / LLMs Anthropic Claude · Azure OpenAI · Google Gemini · Ollama · Azure Speech · Azure Doc Intelligence
Backend Python 3.12 · FastAPI · Uvicorn · Jinja2
Knowledge Qdrant · Pinecone · Azure AI Search · BM25 keyword (4-tier hybrid)
Auth HTTP Basic Auth · Azure Entra SSO (OIDC) · Signed session cookies
CI/CD GitHub Actions → Azure Container Registry → Container Apps (~5 min)
Compliance 23 frameworks: GDPR · HIPAA · PCI-DSS · SOC 2 · NIST · ISO 27001 · NERC CIP · IEC 62443 · +15 more
AIXaaS™ is built on and alongside the best open-source AI infrastructure available. These are the projects we watch closely, draw from, and complement:
| Project | What It Is | Relationship to MAO |
|---|---|---|
| anthropics/anthropic-sdk-python | Official Anthropic Python SDK | Primary LLM integration |
| anthropics/anthropic-cookbook | Claude API usage patterns | Reference for agent prompt patterns |
| modelcontextprotocol/servers | MCP server implementations | MAO ships its own MCP server (8 tools) |
| microsoft/autogen | Microsoft's multi-agent framework | Architectural comparison — MAO's ADR system fills AutoGen's governance gap |
| langchain-ai/langgraph | Agent orchestration via DAG | MAO uses a similar DAG pipeline pattern, custom-built without framework overhead |
| joaomdmoura/crewAI | Role-based agent crews | MAO's 8-role RBAC is the enterprise governance layer CrewAI lacks |
| BerriAI/litellm | Unified LLM API routing | MAO's model router solves the same problem with per-role cost governance built in |
| qdrant/qdrant | High-performance vector database | MAO's Tier 2 semantic store |
| run-llama/llama_index | Data framework for LLM applications | RAG architecture reference |
| NVIDIA AI-Q + NeMo Agent Toolkit | Hierarchical planner→researcher agent pattern, NIM microservices, LangGraph orchestration | Architectural alignment — MAO implements the same sub-agent isolation, structured task decomposition, and 3-tier LLM strategy independently |
| langchain-ai/langsmith | LLM observability, agent tracing, and evaluation platform | MAO's ADR audit trail solves the same agent observability problem — LangSmith integration is a roadmap item |
| huggingface/smolagents | Lightweight code-first agent framework (~1,000 lines) supporting any LLM provider | Model-agnostic agent design — complements MAO's multi-provider LLM routing and namespace isolation pattern |
| huggingface/transformers | 800K+ models, datasets, Inference Endpoints, Enterprise Hub (SOC 2, AWS/Azure/GCP) | MAO's local semantic store (Tier 2) uses HuggingFace sentence-transformers for embedding; HF Enterprise Hub is a candidate for private model hosting |
Bryan Shaw — CTO & Founding Partner, Inflexis 📧 bryan.shaw@inflexis.ai · 🌐 inflexis.ai · 💼 LinkedIn
© 2026 Inflexis · AIXaaS™ is a trademark of Inflexis · MAO Platform source code is proprietary and confidential