Services \u00b7 Infrastructure
Infrastructure
How do our systems use AI?
Your team is enabled. Your data is validated. The question now is how your systems work with AI — not just your people.
Infrastructure is where Claude stops being a productivity tool on someone’s laptop and starts being an operational capability connected to the systems that actually run your business.
Custom MCP servers — the connective tissue between Claude and your internal systems. Autonomous agents. On-premise LLMs. Fine-tuned models. Built in production, not in demos.
OAuth 2.0
Security standard on every MCP server we deploy
Full audit
Every tool invocation logged and traceable
Production
All agents built to production engineering standards — not demos
What the first 30 days look like
Week 1
Requirements and architecture — we define the systems Claude needs to connect to, the data access patterns, the security and governance requirements, and the right build sequence.
Weeks 2 to 3
Build — custom MCP server development, security configuration, tool and API integration. Agent development begins in parallel for clients pursuing that track.
Week 4
Integration testing, production deployment, knowledge transfer. Your team leaves with full technical documentation and the capability to extend what we built.
Day 30 — what you have
Claude connected to your live operational systems through a production-grade MCP server. Full audit logging, OAuth 2.0 security, governed access. Your team using Claude against real data, not exports.
What we deliver
Custom MCP servers
Model Context Protocol servers connecting Claude to your internal databases, APIs, CRM, ERP, data warehouses, and proprietary systems. Governed, auditable, real-time access — with OAuth 2.0, role-based access controls, and full audit logging.
Custom AI agents
Purpose-built systems executing defined operational workflows end-to-end. Each agent connects through MCP, operates in an isolated cloud sandbox, and routes through human-in-the-loop approval gates at the decision points your team has defined.
Agent Command Centre
Our observatory dashboard for the full agent estate. Real-time visibility into what every agent is doing, waiting on, completing, and flagging. Your team stays in control without inspecting logs.
On-premise LLM deployment
Llama, Mistral, and other capable open-source models deployed on your own infrastructure. For clients where cloud AI is ruled out by data sovereignty requirements, security classifications, or regulatory mandate.
Fine-tuned custom LLMs
A domain-specific model trained on your proprietary data — your terminology, your document structure, your institutional knowledge — for use cases that require depth a general-purpose model cannot provide.
Right for you if
- Your team is enabled and data is validated — ready to connect AI to live operational systems.
- You have validated workflows through enablement that are worth automating end-to-end.
- Data sovereignty or security policy rules out cloud AI for your environment.
Not right for you if
- Your team is not yet using Claude consistently — infrastructure built before enablement produces systems nobody uses.
- Your data foundation has not been validated — we enforce Data Readiness before any integration or agent work begins.
Frequently Asked Questions
Timeline
4 to 36 weeks depending on scope