MCP Integration & Deployment
Model Context Protocol (MCP) is the bridge that gives AI systems secure, structured access to your business data and tools. It’s how you go from “AI that knows things” to “AI that can actually do things” in your business.
We implement MCP so your AI agents and assistants can connect to databases, CRMs, file systems, APIs, and internal tools—safely and with proper controls.
What MCP Enables
Without MCP, AI assistants are limited to what’s in their training data or what you paste into them. With MCP, they can:
- Query your databases — Pull real-time data from SQL, PostgreSQL, MongoDB, and other databases
- Access your CRM — Look up customers, deals, and history from Salesforce, HubSpot, or other systems
- Read your files — Access documents, spreadsheets, and data from Google Drive, SharePoint, or local storage
- Use your APIs — Connect to internal services, third-party tools, and custom applications
- Take actions — Create records, send messages, trigger workflows, and update systems
This is what makes AI agents actually useful for your specific business—not just generic assistants.
MCP Services
MCP Server Implementation
We build and deploy MCP servers that expose your data and tools to AI systems securely.
- Database MCP servers (SQL, PostgreSQL, MongoDB, etc.)
- CRM MCP servers (Salesforce, HubSpot, Pipedrive)
- File system MCP servers (local, Google Drive, SharePoint, S3)
- Custom API MCP servers for your internal tools
- Third-party integration servers (Slack, email, calendar)
Security & Access Control
AI access to business data requires careful controls. We implement security at every layer.
- Authentication and authorization
- Role-based access control
- Data filtering and masking
- Audit logging and monitoring
- Rate limiting and abuse prevention
- Credential management
Custom Tool Development
MCP isn’t just about reading data—it’s about giving AI the tools to take action.
- Custom functions for your business processes
- Workflow triggers and automation
- Data transformation and enrichment
- Integration with proprietary systems
- Multi-step operation orchestration
Integration with AI Systems
We connect your MCP servers to the AI systems that will use them.
- Claude and other LLM integrations
- Custom AI agents and assistants
- Chat interfaces and copilots
- Automated workflows and pipelines
Common MCP Implementations
Customer Service AI
Give your support AI access to customer records, order history, and knowledge bases so it can actually help—not just apologize and escalate.
Sales Assistant
Connect AI to your CRM so it can look up accounts, summarize deal history, draft personalized emails, and update records.
Operations Copilot
Let AI query inventory, check schedules, look up procedures, and coordinate across systems.
Analytics Assistant
Enable AI to query databases, generate reports, and answer business questions with real data.
Why MCP vs. Just Using APIs?
| Direct API Integration | MCP Integration |
|---|---|
| Custom code for each AI system | Standardized protocol, works with any MCP-compatible AI |
| Security implemented case-by-case | Built-in security and access control |
| Complex prompt engineering | Structured tools AI understands natively |
| Brittle when APIs change | Abstraction layer handles changes |
| Hard to audit and monitor | Centralized logging and oversight |
Our Approach
- Inventory — Map your data sources, tools, and systems
- Design — Architect which MCP servers you need and how they’ll connect
- Secure — Define access controls, authentication, and data policies
- Build — Implement and test MCP servers
- Integrate — Connect to your AI systems and validate end-to-end
- Monitor — Set up logging, alerting, and ongoing oversight
Who This Is For
- Businesses deploying AI agents that need access to internal data
- Teams building custom AI assistants or copilots
- Organizations wanting to use Claude or other LLMs with their data
- Developers looking for a secure, scalable way to connect AI to business systems
Get Started with MCP
Schedule Your MCP Consultation →
We’ll assess your data landscape, identify MCP opportunities, and outline an implementation plan.
