MCP Servers & Integrations
Give your AI agents access to the real world.
AI models are powerful — but isolated.
Model Context Protocol (MCP) servers are the bridge. We build the infrastructure that lets AI agents query databases, call APIs, execute actions and access real-time information — securely and at scale.
MCP is how AI stops guessing and starts knowing. Every server we deploy is secured by IBM technology with full governance and audit trails.
Why It Matters
- Hallucinate when they should retrieve.
- Guess when they should verify.
- Apologize when they should act.
Model Context Protocol (MCP) solves this:
- Real-time data access — AI queries your systems, not its training data.
- Tool execution — AI doesn't just suggest actions; it performs them.
- Dynamic context — every response grounded in current, accurate information.
- Unified interface — one protocol connecting AI to all your tools and data.
MCP transforms AI from a knowledgeable assistant into an operational agent.
Our Approach
We build MCP infrastructure using a principle we call Context-First Intelligence — where AI accuracy and capability are determined by what it can access, not just what it can generate.
Three pillars define our methodology:
Standardized Protocol, Custom Connections
MCP provides a universal interface for AI-to-tool communication. We implement the standard while building custom connectors to your specific systems, databases and APIs.
Security at the Protocol Level
Every MCP connection is authenticated, encrypted and logged. AI agents operate within defined permission boundaries — they can only access what you explicitly allow.
Stateful Context Management
We build MCP servers that maintain conversation context, cache relevant data and optimize retrieval — ensuring AI responses are fast, consistent and grounded in reality.
Industries Using MCP Servers
reduction in AI hallucination through grounded responses
average context retrieval latency
traceability of AI data access and tool usage
of AI queries resolved with real-time data (vs. training knowledge)
through protocol-level security
reduction in AI hallucination through grounded responses
average context retrieval latency
traceability of AI data access and tool usage
of AI queries resolved with real-time data (vs. training knowledge)
through protocol-level security
Key Capabilities
Expert Playbook
MCP Architecture Patterns
Implementation Path
Discover2–3 weeks
Inventory AI use cases, map required data sources and tools
Design3–4 weeks
Define MCP architecture, security model, connector specifications
Build4–6 weeks
Deploy MCP servers, develop connectors, implement governance
Integrate & Scaleongoing
Connect AI agents, monitor usage, optimize performance
Field Notes
Security & Compliance
Frequently asked questions
What’s new?
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Most teams shipping their first AI agent discover the same uncomfortable truth: the demo that wowed everyone in the all-hands meeting falls apart the moment real users touch it. LLM orchestration in production is not a harder version of prototyping — it is a fundamentally different discipline.
Managing AI Development Projects: Timelines, Risks, and What's Different
Imagine you are three weeks away from a major product launch. The frontend is sleek, the APIs are lightning-fast, and the stakeholders are already popping champagne. But at the center of your architecture sits a "Black Box"—a machine learning model that worked perfectly in the lab but is currently returning 40% accuracy on real-world data.
Let's build the MCP infrastructure that turns AI potential into operational reality.
Your AI is only as good as the context it can access.
