Dedicated AI Employee
Scope a role →- Scope
- One person
- Context
- Own tools
Solutions
AI employees shaped around the work your business repeats.
Prairie Labs builds, deploys, and manages AI employees for one person, one team, or the whole company. Each one gets a defined role, approved tools, permitted knowledge, and clear rules for when a person decides.
Start with a single recurring job and grow into a coordinated workforce. We confirm systems, permissions, legal requirements, and integration methods before anything goes live, then keep managing the work as it changes.
Book a demoDeployment scale
One role or a coordinated workforce.
We separate roles and permissions even when AI employees draw from shared company context.
Team AI Employee
Scope a team →- Scope
- One team
- Context
- Shared sources
Company AI Workforce
Map the workforce →- Scope
- Whole company
- Context
- Role views
White-Label AI Platform
Discuss fit →- Scope
- Your clients
- Context
- Your brand
The managed environment
Everything needed to do bounded work.
Each deployment is scoped. Available channels, tools, model choices, and controls depend on the role and the customer's environment.
01 Identity
A dedicated email identity and, where appropriate, a business phone number for approved calls and texts.
02 Computer
A managed place to work with approved tools, recurring jobs, monitoring, maintenance, and recovery.
03 Knowledge
Approved policies, procedures, and documents become working context, with role-based views.
04 Memory
Permitted context carries across tasks. Retention, correction, and deletion matter as much as recall.
05 Tools
CRMs, calendars, email, documents, and accounting workflows, scoped one confirmed integration at a time.
06 Action
Recurring queues, reports, reminders, and checks run on a schedule or respond to approved events.
07 Skills
Specialized capabilities and repeatable workflows the role can use.
08 Communication
Email, phone, SMS, Slack, Teams, Telegram, and other approved channels.
09 Guardrails
Permissions, approvals, security rules, spending limits, and boundaries.
10 Model catalog
Choose from approved AI models and providers for different jobs.
11 Budgeting
Set spending limits and track model, tool, and infrastructure costs.
12 Provider fallback
Automatically switch models or providers when one fails or becomes unavailable.
13 MCP gateway
One controlled layer for connecting tools, APIs, and business systems.
14 Observability
Monitor activity, errors, latency, usage, and agent performance.
15 Secrets & credentials
Securely manage API keys, OAuth tokens, passwords, and service credentials.
16 Compute & runtime
Dedicated cloud, VM, or on-premises infrastructure where the agent actually runs.
17 Data & storage
Persistent storage for files, databases, logs, working state, and retrieved information.
18 Access & isolation
Role-based permissions, environment isolation, network controls, and limits on what the agent can access.
FAQ
Questions before you hand off the work.
How scope, systems, approvals, and records work in a Prairie Labs deployment.
01What work should we hand to an AI employee first?
Work that repeats. The best first role has a recognizable trigger, reliable source material, approved systems, an accountable owner, and a clear line between automatic action and human judgment.
02What starts the work?
A request, event, schedule, status change, completed meeting, or new document. Each role is scoped to the triggers it should respond to.
03Which systems will it work in?
Only confirmed applications, channels, sources, and accounts. We confirm each integration and choose the safest supported method before deployment.
04What can it complete on its own?
Specific reversible and irreversible steps are separated. Routine work proceeds; exceptions reach a person.
05When does a person decide?
Sensitive messages, money, unusual requests, and material consequences can require review. Approval points are agreed during scoping.
06What does finished work look like?
A booked next step, prepared document, current record, resolved request, or clear exception.
07What can we review afterward?
The sources consulted, actions taken, approvals received, and final status. Important actions leave a trace for oversight.
08Which deployment model fits us?
A Dedicated AI Employee supports one person, a Team AI Employee serves a group, a Company AI Workforce coordinates multiple roles, and the White-Label AI Platform is for organizations delivering managed AI employee services. We help you choose during scoping.
09Who manages the AI employee after launch?
Prairie Labs does. We build, deploy, and manage each AI employee, and keep adjusting it as the work changes.