Maxiconn Technologies

Enterprise AI Transformation

From private LLM deployment to production agents — governed AI built for regulated and high-scale environments.

Why Private Enterprise AI

Public cloud AI is not always an option. Regulated industries, telecom-scale customer data, and internal knowledge require AI that stays inside your perimeter — with governance, audit trails, and integration into existing systems.

AI Service Portfolio

  • Private Enterprise AI — Air-gapped / VPC deployment
  • AI Agents — Guardrailed automation with human approval
  • On-premise LLM Deployment — Ollama, Hermes Agent, Mac Studio stacks
  • MCP Server Development — Typed tool integrations for agents
  • Knowledge Assistants — RAG over enterprise documents
  • Workflow Automation — AI-assisted operational processes
  • Document Intelligence — Extract and route unstructured data
  • Customer Support AI — Deflection with escalation paths
Security by design: Zero-trust boundaries, role-based access, full audit trails for agent actions, and compliance alignment from architecture through deployment — not bolted on after launch.

Reference Architecture

Business Applications — Web · Mobile · Portals
API Gateway — Auth · Routing · Observability
Microservices & Domain Services
Integration Layer — ESB · iPaaS · MCP Connectors
Enterprise Systems — ERP · CRM · DWH · AI Agents · Knowledge Base
Cloud — AWS · Azure · GCP · On-premise

Case Study: Mac Studio Hermes Agent Server

Client: Multi-brand operator (anonymized) · Industry: Local AI Infrastructure

Challenge: Teams needed private AI capabilities without sending data to public clouds — with secure remote access for distributed operators.

Approach: Dedicated Mac Studio AI server running Ollama local LLMs, Hermes Agent orchestration, Obsidian MCP integrations, mesh VPN remote access, and per-team macOS account isolation.

Outcomes:

  • Private AI with secure remote access — no public cloud data exposure
  • Multi-tenant isolation with separate agent configurations per team
  • Production-ready local LLM stack (Gemma, Qwen) on owned hardware

Delivery Approach

Discover → Architect → Build → Harden → Operate. Typical entry points: Architecture Assessment (2–4 weeks) or 90-Day Pilot on one high-value workflow.

Leadership

Jovani Wayne Ogaya, Founder & Chief Solutions Architect — 15+ years across enterprise software, cloud architecture, AI solutions, and systems integration.