S-07 AI solutions
MCP Server Development
Secure Model Context Protocol servers that let AI assistants and agents work with your real business tools, data, and processes — without exposing what they shouldn't touch.
01 What we build
From connector to production-grade MCP infrastructure
Custom MCP servers
Purpose-built servers exposing your internal APIs, databases, and documents to AI clients in a controlled way.
SaaS tool connectors
Let assistants act inside the tools you already run — CRMs, ticketing, billing, document stores.
Auth & permissions
Scoped access, audit logging, and role-aware tooling so AI only sees and does what policy allows.
Agent workflows
Multi-step agentic flows that combine your MCP tools into reliable business automations.
Evaluation & monitoring
Test harnesses and usage monitoring that keep tool-calling accurate as models and prompts evolve.
Hosting & maintenance
Deployment on your cloud (we run on AWS ourselves), with long-term support from a full-cycle team.
02 Why Invatechs
API engineering first, AI second
MCP servers are integration software. We've spent a decade building APIs and integrations for fintech, payroll, and logistics — that's exactly the muscle MCP work needs.
- 10+ years of API & integration delivery — open banking, payment APIs, ERP/CRM integrations
- Security-minded by default — built for regulated domains like lending and payroll
- Full-cycle team — discovery, architecture, build, deployment, support
- Practical AI engineering — agents, RAG, and evaluation pipelines in production, not demos
03 How it runs
Five steps from idea to running MCP server
Identify the tools, data, and actions AI should reach.
Define resources, tools, auth model, and risk boundaries.
A working MCP server against one real workflow.
Hardened, monitored, deployed to your cloud.
Expand tools, improve accuracy, support long-term.
04 Questions
Frequently asked questions
What is an MCP server?
A Model Context Protocol server is integration software that exposes your tools, data, and actions to AI assistants and agents in a controlled, permission-aware way — so an AI client can do real work without direct, unrestricted access to your systems.
Is it safe to connect AI to our internal systems?
Yes, when it is built as scoped integration software. We use least-privilege access, audit logging, validated tool inputs, and approval gates so AI only sees and does what your policy allows.
Do we need to be on a specific cloud?
No. We deploy to your cloud of choice; we run on AWS ourselves and support the common providers, with long-term hosting and maintenance available.
