Build the bridge between AI and your systems with MCP. We develop Model Context Protocol servers that give Claude and other AI models secure, structured access to your databases, APIs, and internal tools — the standard protocol for enterprise AI integration.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
Custom MCP servers that expose your internal APIs, databases, and tools to AI models. TypeScript or Python implementations with proper schema definitions, input validation, and error handling.
Design the right tool abstractions for your AI workflows — what to expose, how to parameterize, and where to draw security boundaries. Tools that are useful to the AI without being dangerous.
Multi-server MCP architectures with authentication, rate limiting, audit logging, and monitoring. Connect multiple data sources through a governed MCP gateway.
Configure MCP servers for Claude Desktop, Claude Code, and API-based agents. End-to-end integration from MCP server to production AI application.
Integrate MCP servers with LangChain, CrewAI, and custom agent architectures. Give your multi-agent systems standardized access to tools and data.
Convert existing ad-hoc LLM tool integrations to MCP standard. Reduce maintenance burden and gain compatibility with the growing MCP ecosystem.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
We adopted MCP from its Anthropic launch. Production experience building MCP servers for databases, CRMs, document systems, and custom business APIs.
Deep experience with Claude API, tool-use, and the Anthropic ecosystem. We understand how MCP fits into the broader Claude architecture.
MCP servers that enforce least-privilege access, validate all inputs, and log every tool invocation. Enterprise-ready from day one.
MCP server + agent architecture + frontend — we build the complete system, not just the protocol layer.
Use Cases
Each use case links to a dedicated implementation page so teams can review architecture patterns in detail.
Let AI models query your PostgreSQL, MongoDB, or data warehouse with natural language. Schema-aware, read-only by default, with query validation.
MCP servers for Salesforce, HubSpot, or custom CRMs. AI agents that look up contacts, update deals, and generate reports.
Expose Confluence, SharePoint, Google Drive, or S3 documents to AI. Semantic search and retrieval via MCP resources.
GitHub, Jira, CI/CD pipelines, and infrastructure management exposed as MCP tools for AI-assisted development workflows.
Wrap your internal microservices as MCP tools. AI agents that can check inventory, process orders, or trigger workflows through governed interfaces.
Pricing calculators, compliance checkers, scheduling engines — any business logic exposed as AI-callable tools with proper validation.
Execution Framework
Map your systems, APIs, and data sources. Identify which capabilities should be exposed to AI and design tool schemas.
Build MCP servers with proper tool definitions, resource endpoints, authentication, and error handling.
Connect to Claude or agent frameworks, test tool invocations, validate security boundaries, and load test.
Production deployment with logging, rate limiting, usage analytics, and alerting for tool failures.
FAQ
Explore related services
Tell us about your systems and AI goals — we'll design an MCP architecture that gives your AI secure, structured access to your data and tools.