Unlock the power of AI with MCP servers
The Model Context Protocol (MCP) is reshaping how applications talk to AI models. We build custom MCP servers that create seamless integration between your systems and state-of-the-art AI capabilities.
What we offer
Custom MCP server development
We build robust, production-ready MCP servers around your requirements:
- API integration: connect AI models to your existing APIs and services.
- Data source connectors: link databases, files and external services.
- Custom tools: develop specialised tools for domain-specific tasks.
- Resource management: efficient handling of prompts, data and model interactions.
MCP implementation services
End-to-end delivery of your MCP infrastructure:
- Architecture design for scalable MCP deployments.
- Security and authentication systems.
- Performance optimisation and caching strategies.
- Monitoring and logging setup.
Integration and migration
Seamless adoption of MCP into your existing stack:
- Migration from legacy AI integrations.
- Multi-provider support (cloud APIs, open-source and local models).
- Hybrid cloud and on-premise deployment.
- Backwards compatibility support.
Key benefits
Standardised integration A single protocol for connecting to multiple AI providers, reducing complexity and vendor lock-in.
Extended capabilities Augment AI models with custom tools, data sources and the business logic of your domain.
Security and control Full control over data flows, granular access management and compliance with your requirements.
Scalability Build once, scale endlessly with efficient resource management and caching.
Developer experience Simple, well-documented APIs that make AI integration easy for your engineering team.
Technologies we use
- MCP SDK: TypeScript and Python implementations.
- API frameworks: FastAPI, Express.js, NestJS.
- Authentication: OAuth 2.0, JWT, API keys.
- Deployment: Docker, Kubernetes, serverless.
- Monitoring: Prometheus, Grafana, custom dashboards.
Use cases
- Corporate knowledge base: connect AI models to internal documentation for real-time, secure, context-aware answers.
- Data analytics platform: natural-language queries over business data, automated report generation and insight extraction from complex datasets.
- Developer tooling: AI-assisted coding, automated testing and documentation, and intelligent debugging support.
- Customer support: access to product information and history, contextual customer data and multi-channel support.
How we work
- Requirements analysis: understand your integration needs, identify data sources and tools, and define security requirements.
- Architecture design: design the MCP server architecture, plan the tool and resource structure, and build an integration roadmap.
- Development and testing: build the custom MCP servers, implement tools and resources, and run comprehensive testing and validation.
- Deployment and integration: deploy to your infrastructure, integrate with existing systems and tune performance.
- Documentation and training: full API documentation, team training sessions and best-practice guidance.
Ready to put MCP to work for your AI integration? Get in touch to discuss your requirements and see how MCP servers can transform your AI capabilities.