Start freeA useful AI employee needs a clear role, approved knowledge, intentional tool access, visible activity, and a human handoff path. Review those decisions before expanding the workflow.
Control layers
Start from approved knowledge sources, define what each AI employee can answer, and keep rollout scope clear.
Connect tools intentionally with OAuth, API keys, bearer tokens, custom headers, or scoped MCP servers.
Design the moments where Aivah should collect context, summarize, and route the conversation to your team.
Use conversations, leads, calls, transcriptions, usage, and agent-level insights to review quality and performance.
Use the enterprise conversation to review data handling, retention, access, procurement, and compliance expectations.
Launch one contained workflow first, prove the operating model, then expand into more roles and channels.
Review checklist
List the approved documents, pages, policies, scripts, and media that define the employee's working knowledge.
Scope each channel, integration, tool, credential, and action to the workflow it is meant to support.
Define escalation moments, required context, routing rules, and the teammate responsible for the next step.
Choose the conversations, calls, outcomes, usage, and feedback your team will inspect during the pilot.
Choose one repeat function with clear demand, source material, owners, and a measurable business outcome.
Define trusted documents, URLs, policies, scripts, media, and escalation rules before launch.
Deploy a focused role to one or two channels so teams can validate quality, safety, and handoff behavior.
Add MCP tools, phone, Slack, WhatsApp, CRM, calendar, or custom workflows once the operating boundary is clear.
Review outcomes, conversation quality, captured leads, call logs, usage, and team feedback before scaling.