MCP & Legacy System Integration
Connect AI agents to the ERP, CRM, databases and document stores you already run, through controlled interfaces with scoped permissions, logging and human approval on anything that writes back.
Why Integration Is Usually the Hard Part
Most agent projects stall not on the model but on access: older systems, inconsistent data and no safe way to let software act on records.
No clean APIs
Core systems often expose little more than batch exports, screens or a database that nobody wants touched directly.
Risky write access
Giving an agent broad credentials to a system of record is not acceptable to most security and audit teams.
Scattered data
The facts an agent needs sit across several systems with different formats, owners and update cycles.
What We Build
We use the Model Context Protocol (MCP) and conventional integration patterns to give agents narrow, observable access to the systems they need.
MCP servers and connectors
Purpose-built tools that expose only the reads and actions an agent needs, with inputs validated before anything reaches your system.
Read-first integration
Most integrations start read-only. Write actions are added one at a time, each behind an approval step.
Adapters for older systems
Wrappers around file drops, database views or existing APIs where a modern interface does not exist.
Access and secrets handling
Scoped service accounts, least-privilege permissions and credentials kept out of prompts and logs.
How It Works
Map the systems
We list the systems involved, who owns them, what data is needed and what the agent should never touch.
Design the interface
We define each tool the agent can call: inputs, outputs, permissions and what gets logged.
Build and test in a sandbox
Connectors are tested against non-production data, including failure cases and bad inputs.
Release with approvals
Write actions go live behind human approval, and are only relaxed where your team decides the evidence supports it.
Where People Stay in Control
Approval before action
Agents prepare drafts, matches and recommendations. A named person approves anything that changes a record, sends a message or moves money.
Clear escalation
When confidence is low, data is missing or a rule is unclear, the agent stops and routes the item to a person with its reasoning attached.
Full audit trail
Every input, source, action and approval is logged so you can see who decided what, and why.
What We Measure
Measures are agreed during the readiness diagnostic and reported against your baseline.
Integration reliability
Share of tool calls that succeed, and how failures are handled and reported.
Manual re-keying removed
Steps where staff no longer copy data between systems by hand.
Exceptions and approvals
Volume of items routed to people, and how quickly they are resolved.
Frequently Asked Questions
What is MCP?
The Model Context Protocol is an open standard for connecting AI models to tools and data sources. It lets us define exactly which actions an agent can take and log every call.
Do we need to replace our older systems?
No. The aim is to work with what you have. Where a system has no API we use the safest available route, such as a read-only view or a controlled file exchange.
Can the agent change data in our systems?
Only if you choose to allow it, only for specific actions, and by default only after a person approves each change.
Related
Governance & Trust Center
How we handle data, approvals, audit trails and access control on every build.
Operations & Supply Chain Agent
An illustrative use case drawing on ERP and supplier data with human sign-off.
Start With a Free 30-Minute AI Opportunity Review
We will look at one workflow with you and tell you honestly whether it is ready to automate, and what to fix first.
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