OptiMCP — Your business, made available to AI

White-label MCP servers, under your brand

Turn your product or your internal systems into a governed AI capability. The AI tools your team and your customers already use can work with your data — authenticated per user, under your rules, with your name on it.

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Your brand, your domain
Per-user authentication
Policy on every call
Full audit trail

Your people already use AI on your data. Right now they do it by pasting it into a chat window.

Every export, every screenshot, every copied customer list is data leaving your perimeter with no record of where it went. Banning it does not work. Giving it a governed path does.

How it works

From internal API to governed AI capability

An OptiMCP server goes live in four steps: choose the capability you want to expose, wire it to your systems, set authentication and policy, then ship it under your own name. Most of the work is deciding what should be possible, not writing the connection.

Choose the capability
Not the whole API. The handful of jobs an AI tool should be able to do — look up an order, check stock, raise a ticket, pull a statement. Start with the ones people ask your team for daily.
Wire it to your systems
We connect the server to the APIs and databases behind those jobs, using the same authenticated paths OptiSync already uses. No new copy of your data.
Set auth and policy
Each user signs in as themselves. The AI tool sees exactly what that person is allowed to see — no more. Policy decides what can be read, what can be changed, and what is off the table.
Ship it under your name
Your brand, your domain, your terms. Your customers connect their AI tool to your server, not to ours. Every call and every result lands in the audit trail.

What is MCP, actually?

MCP — the Model Context Protocol — is an open standard that lets an AI tool call functions in another system through a fixed, documented interface. You publish one MCP server and any MCP-capable AI client connects to it the same way, instead of you building a bespoke integration for every tool.

It works like a power socket, and the socket is yours. The shape is fixed, so anything built to that shape plugs in. You decide which functions the socket offers — those are called tools — and each one has a name, a plain description and a defined set of inputs.

The AI client reads those definitions, works out which tool fits the question in front of it, and calls it. It never touches your database. It sees only the tools you published, and it runs them as the person who signed in.

Tool definition

JSON

{
  "name": "get_customer_orders",
  "description": "Return recent orders for a customer. Scoped to the accounts the signed-in user can already see.",
  "input_schema": {
    "type": "object",
    "properties": {
      "customer_id": {
        "type": "string",
        "description": "Customer record ID in the CRM."
      },
      "since": {
        "type": "string",
        "format": "date",
        "description": "Only return orders placed on or after this date."
      },
      "limit": {
        "type": "integer",
        "default": 20,
        "maximum": 100
      }
    },
    "required": ["customer_id"]
  }
}

Proof

What changes on the day it goes live

On the day an OptiMCP server goes live, the people, the AI tools and the questions all stay the same. What changes is the path the data takes to reach the AI tool, and whether you can see that path afterwards.

Comparison of working without OptiMCP and with OptiMCP across six dimensions.
Dimension	Without OptiMCP	With OptiMCP
How AI reaches your data	Copy and paste, screenshots, or an exported spreadsheet someone drops into a chat window.	A direct, authenticated connection from the AI tool to the systems of record.
Who controls access	Whoever has the file. Access ends where the export begins.	You do. Per-user authentication — the AI tool sees what that person sees.
What happens at security review	Hard questions with no good answers. Data left your perimeter and nobody can say where it went.	A documented boundary, named permissions, and a log you can hand to the reviewer.
Build time	A bespoke integration per AI tool, rebuilt every time a new one arrives.	One server. Every MCP-capable tool connects to the same interface.
Whose brand the customer sees	A third-party tool's, if the integration exists at all.	Yours. Your name, your domain, your terms of use.
Audit trail	None worth the name.	Every tool call, every argument, every result — who asked and when.

The layer

Nothing reaches the bottom row without passing through the middle one. That is the whole point of the layer.

Capabilities

What you get, plainly

Every OptiMCP server exposes your systems as a small set of named tools, authenticates each user as themselves, applies policy on every call, carries your brand, logs every tool call, and runs either hosted by us or in your own cloud.

Your systems as tools

Internal APIs, CRM objects, ERP endpoints and databases exposed as a small set of named tools an AI client can call.

Per-user authentication

Users authenticate as themselves. Permissions follow the person, not a shared service account with the keys to everything.

Policy on every call

Rate limits, field masking, read-only modes and write approvals. You decide what a tool can do before anyone calls it.

White-label by default

Your brand, your domain, your documentation. Nothing in the connection flow tells your customer who built it.

Full audit trail

Every tool call and result is logged with the user, the arguments and the timestamp. Exportable for your own review.

Runs where you need it

Hosted by us or deployed into your own cloud, in the region your data rules require. The interface is the same either way.

Questions

The things buyers actually ask

Access scope, security review, and why an existing API is not the same thing — answered the way we would answer them on a call.

Does this mean an AI tool gets access to our whole database?

No. It gets the tools you define and nothing else. A tool is a named job with a fixed shape — look up orders for a customer, raise a ticket. There is no open query interface, and every call runs as the signed-in user, so it inherits that person's permissions.

What happens when our security team reviews this?

They get a specific boundary to review rather than a vague one. A list of tools, the systems each one touches, how users authenticate, what policy applies, and a log of every call made. We do not claim a certification we do not hold — we give your reviewers the detail to make their own call.

We already have an API. Why not just point the AI at that?

You can point an AI tool at a plain API, and some teams do. What an API on its own does not give you is per-user authentication for AI clients, per-call policy, a log tied to a person, and a set of tools scoped to sensible jobs rather than every endpoint you have ever shipped. That layer is the work.

What if the MCP standard changes, or something replaces it?

The value sits in the layer, not the wire format — the auth, the policy, the tool definitions and the audit trail. If the protocol moves, we move the interface and your systems underneath stay as they are.

Can we offer this to our own customers, not just our staff?

Yes — offering it to your own customers is the white-label case, and it is the one OptiMCP was built for. Your customers connect their own AI tool to your server, under your brand and your terms, each authenticated as themselves and seeing only their own data.

Give your data a governed path to AI.

Bring one question your team keeps answering by hand. We will show you what it looks like as a tool call — and what your security reviewer would see.