Your team uses AI assistants but can't connect them to your actual business data?
Want to build AI-powered workflows that act on your systems, not just generate text?
MCP Server Development Services
Model Context Protocol (MCP) is the standard that lets AI assistants like Claude connect to your tools, data sources, and systems. Without an MCP server, your AI assistant can only answer questions about what it already knows. With one, it can act on real data -- your CRM records, your database, your APIs, your files.
We build custom MCP servers that give AI assistants secure, structured access to your specific data and tools. So your team can use AI to interact with your systems in natural language -- not just generate text.
Custom MCP servers that connect Claude and other AI assistants to your specific systems
Secure access controls so AI can only read or modify what it's permitted to
Integration with your databases, APIs, file systems, and business tools
Experience building AI integrations and agentic systems for enterprise use
Trusted by startups & global brands worldwide
Your AI assistant is only as useful as the data it can reach
A language model trained on public data can generate text, summarise documents, and answer general questions. That's useful. What's more useful is an AI that can look up your actual customer record, check your real inventory level, or create a ticket in your actual project management tool.
MCP is the standard that makes this possible. We build the MCP server -- you get an AI assistant that knows your business, not just the internet.
What we build
Database and data access tools
MCP tools that give AI assistants read access to your databases -- SQL queries with parameter validation, result formatting, and access controls. Your AI can look up records, run reports, and cross-reference data without writing a single query. Read-write tools where permitted, with explicit logging of every modification.
API and system integration tools
MCP tools wrapping your internal APIs and third-party services. Your AI can call your CRM to get customer data, query your ERP for inventory, or trigger actions in your workflow systems -- all through natural language. Structured error handling and response validation so the AI knows when a call succeeds or fails.
File and document access tools
MCP tools for reading and searching your document repositories -- local files, cloud storage (S3, GCS, Azure Blob), document management systems, and knowledge bases. Semantic search integration so the AI can find relevant documents by meaning, not just filename. Content chunking for large documents.
Action and workflow tools
MCP tools that let AI take actions -- create records, send messages, update fields, trigger workflows. Built with the appropriate guardrails -- confirmation steps for destructive actions, rate limiting for high-volume operations, and audit logs for every action taken. The tools that turn your AI from a reader into an operator.
Authentication and security
API key and OAuth authentication for MCP clients. Tool-level permission controls -- each tool specifies what it can access and what it can modify. Sensitive field masking before data reaches the AI. Request logging and audit trail. Rate limiting and abuse prevention. Security architecture that your compliance team can review and sign off on.
Agentic workflow orchestration
Multi-step AI workflows that use your MCP tools to complete tasks end-to-end. An AI agent that researches a customer, pulls their order history, checks their support tickets, and drafts a response -- all without human intervention for each step. The orchestration layer that connects your MCP tools into automated workflows.
Tell us which systems you want your AI assistant to work with.
We'll design the MCP server and give you a fixed cost.
Frequently asked questions
Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI assistants connect to external tools, data sources, and systems. Before MCP, each AI integration required custom code on both sides. MCP standardises the interface -- an MCP server exposes a set of resources and tools that any MCP-compatible AI client can discover and use. Claude (claude.ai and API), and other AI assistants that support MCP, can connect to your MCP server and use the tools you've defined to interact with your systems.
An MCP server exposes three types of capabilities: (1) Resources -- data your AI can read, like database records, file contents, or API responses. (2) Tools -- actions your AI can take, like writing a database record, sending a message, or calling an external API. (3) Prompts -- pre-built interaction patterns for common tasks. A custom MCP server for your business might let your AI assistant query your CRM for customer information, look up inventory levels, create support tickets, read from your knowledge base, or trigger workflows in your internal systems -- all in response to natural language requests.
MCP servers can connect to any system your infrastructure can reach. Common integrations we build include relational databases (PostgreSQL, MySQL, SQL Server), REST and GraphQL APIs, file systems and document storage, ERP and CRM systems, communication platforms (Slack, email), ticketing systems (Jira, Linear), and custom internal tools. The MCP server acts as a secure intermediary -- the AI never connects directly to your database or API. Access is mediated through the tools you define.
Security is the primary design consideration for any MCP server. We build MCP servers with explicit tool-level permissions -- each tool defines exactly what it can read and what it can modify. Authentication uses API keys or OAuth depending on the client. Sensitive data can be filtered or masked before it's returned to the AI. All tool calls are logged for audit. We apply the principle of least privilege throughout -- the AI assistant gets access to exactly what it needs to do its job, and nothing more.
Yes. We build MCP servers for Claude (via Claude Desktop, Claude.ai, or the Anthropic API with MCP support), and for any other MCP-compatible AI client. If you're building an AI product that uses Claude under the hood and want to give it access to your specific tools and data, we build the MCP server layer. We also build the orchestration layer -- the agentic workflows that use MCP tools to complete multi-step tasks automatically.
A focused MCP server covering 5--10 tools connecting to 2--3 systems typically runs $15,000--$40,000. More complex MCP servers with many tool types, complex authentication requirements, and multiple system integrations run higher. The cost depends primarily on the number of systems to integrate and the complexity of the access control requirements. We scope every project before pricing it.