Permission boundaries
Expose only the records and actions needed for the defined task.
AI integrations and MCP
Connect AI to the tools where work already happens.
An AI system becomes useful when it can reach the right information and, when approved, take an action in the right business tool. The integration layer defines exactly what it can read, write, and request.
Existing systems
01Use the interfaces the business already trusts.
We connect APIs, databases, CRMs, file storage, payment systems, and booking systems so information can move without duplicate entry. Each integration handles authentication, data shape, rate limits, errors, and recovery as part of the product, not as an afterthought.
MCP in plain language
02Give AI a standard way to request tools and context.
Model Context Protocol, or MCP, is a shared interface that lets an AI application discover approved tools and information. An MCP server can present a narrow set of business capabilities without exposing the underlying system broadly. The protocol does not decide permissions or approvals by itself, so those controls still belong in the integration design.
Action approval
Require human confirmation before payments, publication, deletion, or other consequential changes.
Audit logs
Record tool requests, outcomes, failures, and staff decisions in a form that can be reviewed.
Model independence
03Keep business logic outside a single model.
Stable tool contracts, structured outputs, and clear application rules reduce dependence on one model provider. The right model can change by task, cost, latency, or policy without rebuilding the whole operational system.