We build MCP server layers on top of your current systems so AI agents can read your data, trigger your workflows, and act on your behalf, without replacing anything you already have.
The Model Context Protocol is a universal adapter between AI agents and software. Think of what USB did for hardware: before USB, every device needed its own proprietary cable and connector. USB gave devices a standard connection that just works. MCP does the same thing for AI agents and software. Instead of building custom integrations for every AI model or agent framework, MCP provides a single, open standard that lets any compatible agent connect to any compatible system. One protocol, universal access.
Pilot to validate value, full Production Build to ship, Enterprise Scale for ongoing capability expansion.
Validate that MCP fits one workflow on your real system.
From $30k USD
3 weeks
Ship the production MCP layer your agents need.
From $80k USD
8–12 weeks
Expand MCP across multiple systems and agents.
From $22k USD/mo
Ongoing retainer
Budget figures reference engagements we delivered in the last 12 months. Your specific scope and price are finalized at the discovery call.
Every MCP server exposes three types of capabilities that agents use to interact with your software.
Tools are the actions an agent can take within your system. Creating a record, sending a notification, running a calculation, approving a request: if your software can do it, an MCP Tool lets an agent do it too, with the same validation and permissions your human users have.
Resources are the data an agent can read from your system. Customer records, inventory levels, financial reports, configuration settings: Resources give agents structured, read-only access to the information they need to make decisions and complete tasks.
Prompts are pre-built instructions that guide agents through complex workflows in your system. They encode your business logic and best practices into reusable templates so agents follow the same procedures your best employees do, consistently and at scale.
8 wk
Median time to first agent in production
From kickoff to a working agent operating on your existing software.
0
Underlying app rewrites required
We add the MCP layer on top — your current app continues to work unchanged.
5+
Agent integrations supported per build
MCP is provider-agnostic. One layer, multiple agents (Claude, GPT, custom).
“MCP is what USB was for hardware. The teams that adopt it now will spend the next two years compounding on every new agent capability — without rebuilding the underlying systems.”
Lorena Campos
Director, Humind Labs AI
No. MCP layers run alongside your app, not inside it. We instrument with observability so you can see the agent traffic, but the human-facing performance of your software is unaffected.
Yes. Every MCP Tool has explicit auth scopes; you control what an agent can read, write, or trigger. We typically start with read-only Resources, then graduate to Tools as confidence builds.
Common — we map it as part of discovery. We've worked with undocumented monoliths, GraphQL services, and legacy SOAP. Discovery includes capturing the implicit contracts your engineers know but haven't written down.
Sometimes — if the SaaS has a documented API. For Salesforce, HubSpot, or NetSuite, we build MCP layers on top of their APIs. For closed systems with no API, we recommend Process Consulting first to assess feasibility.
AI agents connect to MCP servers the same way browsers connect to websites. Your browser doesn't need custom code for every site; it uses HTTP, a universal protocol, to request pages from any web server. MCP works the same way. An AI agent uses the Model Context Protocol to discover what Tools, Resources, and Prompts a server offers, then interacts with them through structured requests. The agent doesn't need to know how your software is built internally. It just needs to know what the MCP server exposes. This means you can swap AI providers, add new agents, or upgrade your internal systems without breaking the connection.
We'll assess your current systems and show you exactly how an MCP layer gives AI agents secure, structured access.
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