AivahStart free
Enterprise

Deploy AI employees with the control business buyers need.

Plan a focused pilot around one repeat workflow, approved source material, clear tool boundaries, measurable outcomes, and the security review your team expects before rollout.

01Pilot plan
02Security review
03Tool boundaries
04Outcome tracking
Scoped pilotApproved knowledgePermissioned actionsHuman escalationMeasured outcomes
Deployment blueprint

Put governance around the work, not just around the model.

Aivah connects approved business context to defined actions and channels, with review and human ownership surrounding the complete workflow.

Explore the trust model
AAI employeeRole + instructions
01KnowledgeApproved sources
02ToolsPermissioned actions
03ChannelsWeb, phone, team
04OversightReview + handoff

One review, shared answers

Give every enterprise stakeholder the evidence they need.

A successful pilot aligns value, technical boundaries, and operational ownership before implementation begins.
01Business owner

Will this improve a measurable workflow?

Define one outcome, baseline it, and review the pilot against agreed success signals.

02IT & security

What can the AI access and do?

Approve knowledge sources, tools, channels, permissions, and handoff boundaries before launch.

03Operations

How will the work be managed?

Review activity, transcripts, exceptions, ownership, and the path for continuous improvement.

Business outcomes

Start where the enterprise value is obvious.

Aivah works best when the first deployment maps to a measurable business moment: missed demand, support triage, onboarding, training, or operational follow-through.
01

Recover missed demand

Answer, qualify, and capture intent after hours, during campaign spikes, and across high-volume website or phone traffic.

24/7
02

Reduce repeat team work

Move common sales, support, onboarding, and training questions into managed AI employees trained on approved source material.

Less manual triage
03

Shorten time to response

Give customers a useful first answer immediately, then route context to the right human, tool, or next step.

Instant first response
04

Make AI measurable

Review conversations, leads, calls, transcripts, quiz results, usage, and workflow performance instead of guessing what happened.

Operational visibility

Governed rollout

Give every stakeholder a clear answer before launch.

Enterprise adoption depends on more than a good demo. Teams need to understand data scope, tool access, monitoring, human handoff, and the review path before AI employees go live.
01

Data boundaries

Start from approved knowledge sources, define what each AI employee can answer, and keep rollout scope clear.

02

Permissioned tools

Connect tools intentionally with OAuth, API keys, bearer tokens, custom headers, or scoped MCP servers.

03

Human handoff

Design the moments where Aivah should collect context, summarize, and route the conversation to your team.

04

Monitoring and logs

Use conversations, leads, calls, transcriptions, usage, and agent-level insights to review quality and performance.

05

Security review

Use the enterprise conversation to review data handling, retention, access, procurement, and compliance expectations.

06

Pilot-first rollout

Launch one contained workflow first, prove the operating model, then expand into more roles and channels.

Pilot path

From one contained pilot to a managed AI workforce.

01

Map the workflow

Choose one repeat function with clear demand, source material, owners, and a measurable business outcome.

02

Approve the knowledge

Define trusted documents, URLs, policies, scripts, media, and escalation rules before launch.

03

Pilot one AI employee

Deploy a focused role to one or two channels so teams can validate quality, safety, and handoff behavior.

04

Connect tools carefully

Add MCP tools, phone, Slack, WhatsApp, CRM, calendar, or custom workflows once the operating boundary is clear.

05

Measure and expand

Review outcomes, conversation quality, captured leads, call logs, usage, and team feedback before scaling.

Plan a pilot around one measurable workflow.