The Orchestration Layer That Connects Intelligence to Action.

MCP Servers & Integrations

Give your AI agents access to the real world.

Trusted by global partners, startups and enterprises

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

Most products fail not because they were built wrong — but because they stopped evolving.

The market changes. APIs update. Users shift behavior. Without a structured support ecosystem, growth stalls and innovation decays.

Our philosophy is simple: Maintenance is not about fixing what's broken — it's about preventing it from breaking. We combine engineering discipline, AI monitoring and data-driven improvement cycles to make sure your digital infrastructure remains stable, efficient and ready for what's next.

See how continuous optimization drives product longevity.

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 Knowledge Base Engineering

A national financial institution connected 60+ fintech partners through one secure AI-enabled platform.

Finance

Healthcare

Government

Insurance

Manufacturing

Legal

KPIs

90^%

reduction in information retrieval time

ZERO^{hallucination}

policy through retrieval-grounded responses

95+^%

answer accuracy with proper citations

80^%

decrease in "knowledge not found" failures

100^{% audit trail}

for compliance and governance

Key Capabilities

MCP Server Architecture & Deployment

We design and deploy MCP server infrastructure optimized for your scale, latency requirements and security policies — whether cloud-native, hybrid or on-premise.Example: High-availability MCP cluster handling 10,000+ concurrent AI agent connections with <100ms context retrieval across 15 integrated systems.

Database & Data Source Connectors

We build MCP connectors that let AI agents query SQL databases, data warehouses, document stores and real-time data streams — with proper access controls and query optimization.Example: MCP connector enabling AI agents to query 5 years of customer transaction history, inventory levels and pricing data in real-time — without exposing raw database access.

API & SaaS Tool Integration

We connect AI agents to your existing tools — Salesforce, HubSpot, Slack, Jira, SAP, custom APIs — through standardized MCP interfaces that abstract complexity.Example: AI agent that checks order status in ERP, updates ticket in CRM and notifies customer via Slack — all through unified MCP calls.

Custom Tool Development

We build specialized tools that AI agents can invoke — calculations, validations, document generation, external lookups — extending AI capabilities beyond conversation.Example: Custom MCP tools for HS code classification, duty calculation and customs document generation — invoked by logistics AI agents processing international shipments.

Context Caching & Optimization

We implement intelligent caching layers that reduce latency and system load — pre-fetching relevant context, maintaining session state and optimizing retrieval patterns.Example: Context caching that reduced average AI response time by 60% by pre-loading customer profile, recent interactions and relevant policies at conversation start.

MCP Governance & Monitoring

We build observability into every MCP deployment — tracking which agents access which tools, monitoring performance, alerting on anomalies and maintaining audit trails.Example: Real-time dashboard showing MCP usage patterns, identifying that 80% of context requests go to 3 systems — enabling targeted optimization.

What could your systems achieve together?

A healthcare network connected patients, doctors and clinics through one intelligent agent ecosystem.

Expert Playbook

When to Use

AI agents giving outdated or generic answers because they lack real-time data.
Need for AI to take actions in your systems, not just provide information.
Multiple tools and data sources that AI needs to access coherently.
Compliance requirements demanding audit trails for AI data access.
Scaling from AI demos to production — where accuracy and reliability matter.

Not a Fit If

AI use case requires only general knowledge (no proprietary data needed).
No systems or data sources to connect (build the foundation first).
Organization not ready to grant AI any data access (address governance first).
Single, simple integration (direct API might be simpler than full MCP).

MCP Architecture Patterns

Single-Server Hub

One MCP server connecting AI to multiple tools. Best for: smaller deployments, simpler governance.

Distributed MCP Mesh

Multiple specialized MCP servers for different domains. Best for: large enterprises, domain separation.

Hierarchical MCP

Tiered servers with different access levels. Best for: regulated industries, multi-tenant environments.

Edge MCP

Local MCP servers for latency-sensitive or air-gapped environments. Best for: manufacturing, healthcare, government.

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

Real World Evidence

99.99 ^%

Mashu AI Platform

Built the core multi-agent orchestration engine powering enterprise automation worldwide — achieving 100% agent governance with 99.99% uptime SLA and full audit traceability across all agent interactions.

220 ^{+ countries}

Shipper Global (Logistics)

Integrated specialized agents for route optimization, customs compliance, carrier selection and price comparison. Agents coordinate autonomously to create optimal delivery plans across 220+ countries — with 90%+ end-to-end automation.

100 ^%

NeuroLab (Healthcare)

Deployed a multi-agent system connecting patients, doctors and clinics in real-time. Separate agents handle appointment scheduling, medication reminders, symptom monitoring and clinical alerts — achieving 100% coverage of care blind spots and <5 minute anomaly detection.

70 ^%

EL AL Airlines (Aviation)

Multi-agent orchestration for refund processing — document extraction agent, validation agent, payment agent and notification agent working in concert. Reduced manual case handling by 70% while maintaining full regulatory compliance.

Security & Compliance

Secured by IBM Technology

Protocol-level authentication — every MCP connection requires valid credentials and permission scope

Granular access control — define exactly which tools and data each AI agent can access

Encryption everywhere — TLS for transport, encryption at rest for cached context

Query auditing — complete log of every data request, tool invocation and response

Data minimization — AI receives only the context needed, not full database access

Compliance frameworks — ISO 27001, SOC 2, GDPR, HIPAA, PCI-DSS aligned infrastructure