An integration plan
Identify the AI client, data sources, allowed queries, and access model before building.
Your business data. A clearer conversation.
Connect AI tools to the systems your team already uses through Model Context Protocol (MCP), with scoped permissions and clear boundaries around data access.
Leave with a clearer first use case, a view of feasibility, and a practical next step.
Ask an operational question. Get a grounded answer.
A team asks which leases renew this month. A permissioned MCP server checks access and queries the relevant PostgreSQL records, so the answer comes from current business data.
MCP provides the connection. Access rules, allowed tools, and the data exposed through that connection are designed around your workflow.
What a first release includes.
A scoped MCP server
Expose the agreed tools and data sources with permissions appropriate to the task.
Documentation & setup
Deliver the custom integration code, configuration guidance, and a walkthrough for your team.
A focused scope. An agreed timeline.
A focused pilot may take 2–6 weeks as a planning guide. The proposal confirms deliverables, dependencies, costs, and schedule. You keep the custom code and documentation; any ongoing support is scoped separately.
Questions about mcp integrations.
What is MCP in plain language?
It is a way for an AI application to use tools and access contextual information from connected systems. We build the connection for the data and actions your team needs.
Does the AI get unrestricted database access?
Access is scoped during discovery. We agree what the integration can query or change and include permission checks in the implementation.
Can the integration be read-only?
Yes. A first release can focus on retrieving information. Any write actions require explicit scoping and an agreed approval model.
Start with the task that costs you time.
Tell us about your workflow and existing systems. We’ll discuss a useful first release and what is needed to scope it.