Connect AI to the systems that run your business.
MCP servers and integrations that let AI assistants and agents work with your CRM, search, mail, analytics and internal tools, securely. The Model Context Protocol is how AI reaches real systems. We build MCP servers that expose your tools and data with proper permissions, authentication and audit trails, and integrate them with the platforms you already run. Compel exposes its own tools through an MCP server in production.
The integration gap
AI is only as useful as the systems it can safely reach.
Assistants that cannot touch your data
A smart assistant with no connection to your CRM, files or tools can only give generic answers.
One-off integrations that do not scale
Every new assistant means another custom connector, and each one has to be secured and maintained separately.
Broad access with no oversight
Handing an AI wide credentials is a risk. Without scoping and audit trails, nobody can say what it touched.
Legacy systems that have no API
Older tools and databases hold the data that matters, but they are hard for modern AI to reach.
A standard connection layer turns every new AI tool into a plug-in, not a project.
One well-built MCP server lets many assistants and agents use the same governed access to your systems.
Integration done properly
Why AI integrations often become a security problem.
A connection layer should make AI safer, not riskier. Here is the difference.
Usually
Shared admin keys.
One powerful credential is reused everywhere, so any mistake has maximum reach.
Lunova
Least-privilege access.
Each tool exposes only what it needs, with scoped authentication.
Usually
No record of actions.
Nobody can reconstruct what an assistant read or changed.
Lunova
Audit trails.
Every call is logged so actions can be reviewed.
Usually
Brittle glue code.
Custom scripts break silently when a vendor changes an API.
Lunova
Maintainable architecture.
Clean, tested and documented integrations that you can extend.
Usually
Locked into one assistant.
The connector only works with a single AI product.
Lunova
Built on an open protocol.
MCP works across assistants, so your investment is not tied to one vendor.
The Journey is a seven-step process from discovery through growth, with clear outcomes at each stage.
How we work
The Journey
From vision to velocity — precision and ownership at every step.
-
DiscoveryDiscovery
Map systems and use cases
We list the systems, data and actions your AI should reach, and who should be allowed to use them.
A prioritised integration map -
IdeationIdeation
Decide what to expose
We choose which tools and resources become MCP capabilities, and which stay closed.
A scoped, minimal surface area -
DesignDesign
Design tools, schemas and permissions
Tool definitions, input and output schemas, and permission rules are designed before code.
A reviewable server design -
EngineeringEngineering
Build the server and connectors
MCP server, API connectors and test suites, demoed every week against real systems.
Working integrations at each checkpoint -
SecuritySecurity
Test access and abuse paths
Our security team tests authentication, token handling and third-party connections.
Findings fixed and retested before launch -
DeploymentDeployment
Deploy with monitoring
Zero-downtime rollout with logging, rate limits, documentation and repository handover.
A controlled, observable launch -
GrowthGrowth
Extend as you add tools
Support, monitoring and new capabilities as your AI use grows.
A connection layer that grows with you
How We Build MCP Servers & Integrations
Systems & Access
What we deliver
- System and data inventory
- Capability selection
- Permission model
- Risk review
Tools & Schemas
What we deliver
- MCP tool definitions
- Input and output schemas
- Authentication design
- Error and rate-limit behaviour
Server & Connectors
What we deliver
- MCP server
- API and legacy connectors
- Automated tests
- Logging and tracing
Test, Deploy, Extend
What we deliver
- Independent security testing
- Zero-downtime deployment
- Monitoring and alerting
- Priority post-launch support
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Proof in production
Built, delivered and still running.
Real systems we built and still run, chosen for what they prove about our MCP server and integration work.
P06 · AI SaaS
Compel
Compel · Engineering led by our CTO
Helps brands get seen where search is heading, inside AI assistants, with AI agents and an MCP server automating the workflow.
Why it matters here → Compel exposes its tools through an MCP server so AI assistants can work with it directly.
- AI visibility
- Link building
- AI agents
- MCP server
Why Organizations Choose Lunova for MCP & Integrations
MCP in production, not on a slide
Compel exposes its tools through an MCP server, and NOBS Marketplace uses MCP servers for CRM, search, mail and analytics.
Security tested before launch
Our independent cybersecurity team tests authentication, tokens and third-party connections.
Legacy and modern, any stack
.NET, Java, Node.js, Python, Go and Rust, across SQL and NoSQL data.
Scoped, logged access
Least privilege and audit trails are part of the standard build.
100% code ownership
Server code, schemas and documentation are yours at handover.
Senior engineers, direct access
You work with the people building the connection layer.
FAQ
Frequently Asked Questions – MCP Servers & Integrations
Straight answers on what MCP is, how access is controlled, which systems it can reach and who owns the result.
Still have questions? Contact us
What is an MCP server?
MCP, the Model Context Protocol, is an open standard that lets AI assistants and agents use tools and data in a consistent way. An MCP server exposes your systems to them through well-defined, permissioned tools.
Why not just give the AI direct database access?
Direct access is hard to scope and hard to audit. An MCP server limits the AI to specific actions, enforces authentication and logs every call.
Which systems can you connect?
CRM, search, mail, analytics, internal databases and legacy systems, plus payments and third-party APIs. If it has an interface, or can be given one, we can usually connect it.
Can one server work with several assistants?
Yes. Because MCP is an open protocol, one well-built server can serve different assistants and agents, so you are not tied to one AI vendor.
How do you keep it secure?
Least-privilege tools, scoped authentication, rate limits and audit logs, followed by independent penetration testing before launch.
Who owns the server and code?
You do. Code, schemas and documentation transfer to you at launch, with no lock-in.
Still have questions? Contact us →
Ready to connect your AI to the systems that run your business, safely?
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Speak with an integration engineer, not a sales script.
No commitment required
Get a clear feasibility answer on the first call.
Fixed price after Discovery
Scope, cost and timeline in writing before build.