AI Automation

Your AI tools are isolated — they can't see your real data or take real actions

We build custom MCP servers that give your AI agents secure, structured access to your internal systems — making them genuinely useful instead of just conversational.

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The challenges you're facing

AI assistants that can't access your actual business data — giving generic answers instead of specific ones

Every AI tool requiring its own bespoke integration, creating maintenance debt with each new model

No standardised way to expose internal APIs to AI agents safely with proper access controls

Connect Your AI to Everything with MCP

Model Context Protocol (MCP) is Anthropic's open standard for AI-to-tool connectivity. We build MCP servers for your internal systems — databases, APIs, file stores, and SaaS platforms — that give any MCP-compatible AI agent structured access to real data and real actions. Build once, use with Claude, Cursor, Continue, or any MCP client.

What you get

1

Integration Architecture Design

Map which systems the AI needs to access, define resource types, tool schemas, and access control boundaries.

2

MCP Server Development

Build custom MCP servers for each integration target with proper authentication, error handling, and rate limiting.

3

Security & Access Control

Implement scoped permissions ensuring AI agents can only access data and actions they're authorised for.

4

Testing & Documentation

End-to-end integration testing with your AI client and developer documentation for your engineering team.

Technologies & tools

Model Context ProtocolTypeScriptPythonREST APIsGraphQLPostgreSQLRedisOAuth2

Case study — anonymised

Software Development Agency — 80 developers

Before

Developers using AI coding assistants that had no access to internal codebase, documentation, or Jira tickets — providing generic suggestions unaware of their specific architecture.

After

Custom MCP servers built for GitHub, Confluence, Jira, and internal API docs. AI assistants now give context-aware suggestions referencing actual team conventions and tickets.

Developer productivity up 40%, PR review time halved, onboarding of new developers to existing codebase cut from 3 weeks to 1 week

Further reading

What Is Model Context Protocol (MCP)? A Guide for Enterprises

Every AI assistant your team uses is only as useful as the data it can actually see. MCP is the standard closing that gap — and it comes with its own security questions.

Frequently Asked Questions

Common questions from enterprise and mid-market teams across India and internationally.

What is Model Context Protocol and why does it matter?
MCP is an open standard (created by Anthropic, now widely adopted) that defines how AI models connect to external tools and data sources. Instead of every AI tool having bespoke integrations, MCP creates a universal interface — like USB for AI. Build an MCP server once, and any MCP-compatible AI client can use it.
Which AI tools support MCP?
Claude (all versions), Cursor, Continue.dev, Cline, Zed, and many other AI development tools support MCP. The standard is growing rapidly. Microsoft Copilot and other enterprise tools are adding support in 2025.
Is it safe to give AI agents access to internal systems via MCP?
Yes, with proper implementation. MCP supports scoped permissions, read-only vs read-write tool separation, audit logging, and rate limiting. We design every MCP server with least-privilege access — the AI can only access what it specifically needs.
Can you build MCP servers for our custom internal applications?
Yes. We build MCP servers for any system with an accessible API or database. Common targets include internal REST APIs, legacy SOAP services, SQL databases, Elasticsearch, and file storage systems. If it has an interface, we can expose it to AI safely.

Ready to get started?

Tell us about your situation and we'll respond with a tailored assessment within one business day.

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