LLM assistants can't access your internal data and systems, making them less useful?
Want Claude or other AI assistants to take actions in your tools, not just answer questions?
Model Context Protocol engineers who build custom MCP servers that connect your internal systems, databases, and APIs to AI assistants -- giving LLMs the context and tools they need to do real work.
MCP server developers with Claude, Claude Desktop, and tool integration experience
Custom MCP servers for databases, APIs, ERPs, CRMs, and internal systems
Start in days. Fixed cost or monthly retainer.
We build MCP servers for teams that want AI assistants connected to their real systems.
Enterprises deploying Claude or other MCP-compatible AI assistants internally. Custom MCP servers connect the assistant to your ERP, CRM, data warehouse, and internal APIs -- so the AI can answer questions about your actual business data, not generic knowledge.
SaaS products adding AI assistant functionality with access to their platform's data. An MCP server exposes your product's data and actions to the LLM so the AI assistant can read user data, create records, and take actions within your platform.
Engineering teams that want AI coding assistants connected to their internal systems -- code repositories, CI/CD pipelines, monitoring dashboards, incident management, and documentation.
Consultants and agencies building AI solutions for clients that need custom MCP servers as part of the delivery. We build MCP servers that connect to your clients' existing systems.
MCP servers that expose your internal data and capabilities as LLM tools and resources. We implement the MCP protocol -- tools, resources, and prompts -- connecting to your databases, REST APIs, GraphQL endpoints, and internal services. The MCP server that gives your AI assistant access to your real systems.
MCP servers for querying databases -- PostgreSQL, MySQL, MongoDB, Snowflake, BigQuery -- with appropriate read access and query safety controls. The AI assistant can answer questions about your data without writing SQL or learning your schema.
MCP servers that connect to Salesforce, HubSpot, SAP, NetSuite, and other business systems via their APIs. Read customer records, update opportunity stages, create support tickets, and query order history through an AI assistant conversation.
MCP servers wrapping your existing internal APIs -- product backends, microservices, and internal tools. The AI assistant gains access to your platform's capabilities through a well-designed tool interface, without requiring the LLM to understand your API documentation.
MCP servers for accessing documents, codebases, and file systems. Integration with SharePoint, Google Drive, Confluence, and Notion to give AI assistants access to your organisation's knowledge and documentation.
MCP servers with proper authentication, authorisation, and access controls -- ensuring the AI assistant can only access data and take actions that the user is authorised for. Audit logging for all MCP tool calls.
Custom MCP servers for databases, APIs, CRMs, ERPs, and internal tools.
We've built MCP servers connecting AI assistants to real business systems. We understand the MCP protocol, tool definition best practices, and how to design tool interfaces that LLMs can use reliably.
MCP servers have access to sensitive business data. We build with least-privilege access, proper authentication, input validation, and audit logging -- MCP servers that are safe to deploy in enterprise environments.
We've integrated with the systems your business actually runs on -- Salesforce, HubSpot, SAP, PostgreSQL, MongoDB, Snowflake, Google Drive, Confluence, and custom REST and GraphQL APIs.
MCP server usage metrics, tool call success rates, and performance -- at the cadence you choose. You know how the AI assistant is using your systems.
Project-based for a specific MCP server build, or retainer for ongoing MCP development as your AI assistant's capabilities grow.
MCP is one layer of AI product development. We've built 20+ AI systems and understand how MCP servers fit into the broader AI architecture -- evaluation, cost management, and production reliability.
For a specific MCP server build connecting to 1--3 systems.
For an enterprise MCP deployment connecting to multiple systems with ongoing development.
A full AI team for complex MCP deployments with custom AI assistant development.
A custom MCP server connecting to one primary system -- database, API, or business tool -- with full tool and resource definitions.
Multiple MCP servers or one comprehensive MCP server connecting to several systems -- CRM, ERP, database, and internal APIs.
Enterprise-scale MCP infrastructure with many system integrations, SSO, role-based access, and ongoing management.
Tell us which systems you want to connect to your AI assistant and what actions it should be able to take.
A 30-minute call to understand your systems, the APIs or databases available, and what the AI assistant should be able to do.
A clear proposal with MCP server architecture, tool definitions, timeline, and fixed cost.
Engineers onboard in days. First MCP server tool definitions within one week. Working integration within two to three weeks.
Custom MCP server engineers available in days. Fixed project cost. Full source code ownership.
Frequently Asked Questions
MCP is an open protocol developed by Anthropic that standardises how AI assistants connect to external data sources and tools. Before MCP, every AI integration required custom code for each system connection. MCP defines a standard interface -- tools (actions the AI can take), resources (data the AI can read), and prompts (reusable instructions) -- that any MCP-compatible AI client can use. Claude Desktop, Claude API clients, and a growing ecosystem of AI tools support MCP. Building an MCP server once connects your system to all current and future MCP-compatible AI assistants.
Claude (Anthropic) has first-class MCP support -- Claude Desktop, the Claude API, and Claude.ai support MCP servers. The MCP ecosystem is expanding rapidly: other AI assistants, coding tools, and developer environments are adding MCP support. Building an MCP server is an investment in interoperability across the AI ecosystem, not just one provider.
Security is central to MCP server design. We implement: authentication for the MCP server (API keys, OAuth, or internal auth), user-level authorisation so the AI only accesses what the current user can access, input validation to prevent injection attacks through tool parameters, read-only access for data retrieval tools, explicit confirmation flows for mutating operations, and complete audit logs of every tool call. MCP servers in enterprise environments need the same security rigour as any other system integration.
Yes. MCP servers run as local processes or on your own infrastructure -- they don't require public internet access. For on-premise ERP systems, internal databases, or air-gapped environments, we build MCP servers that run within your network perimeter, connecting the AI assistant to your internal systems without any data leaving your infrastructure. The MCP client (Claude Desktop or your AI application) communicates with the MCP server over a local connection.