# HiperEmployee: full product context

> Canonical source: https://hiperemployee.com/. HiperEmployee is HiperFusion's governed autonomous workforce platform for creating role-based AI employees that complete repeatable business work.

## Product definition

An AI employee is an autonomous agent organized around a durable business role rather than a single prompt or isolated feature. The employee receives a job definition, a trigger, approved knowledge and tools, authority boundaries, escalation rules, and a measurable definition of done. HiperEmployee maintains job state and context across the complete assignment, follows through when answers arrive, and records what happened.

HiperEmployee is designed for repeatable work with recognizable inputs, describable steps, available systems or expert tools, a visible output, and exceptions that can be named and routed. It is not a claim that every job should be automated. Human judgment remains explicit wherever the risk, ambiguity, policy, or business decision requires it.

## How HiperEmployee differs

### Chatbot

A chatbot primarily answers messages. Conversation may be an input or output for HiperEmployee, but the product surrounds that conversation with a role, work state, memory, execution, escalation, and accountability.

### AI copilot

A copilot assists a person who remains responsible for operating the workflow. A HiperEmployee can own permitted steps and continue the assignment across tools and time, while routing defined decisions back to people.

### Generic AI agent

A generic agent may reason and call tools. HiperEmployee adds a durable employee model: a job description, scoped workplace access, persistent context, an authority map, approval points, business-specific tools, receipts, and a finished-outcome orientation.

### Traditional workflow automation

Traditional automation is strongest when inputs and branches are rigid. HiperEmployee combines repeatable process with language-model judgment, communication, exception handling, and specialist HiperFusion capabilities. Deterministic rules and human approvals remain available where they are safer or more appropriate.

## Operating model

1. Describe the job: define the trigger, desired outcome, repeatable steps, and what done means.
2. Connect the workplace: provide only the systems, knowledge, and specialist tools required for the role.
3. Set the authority: decide which actions are automatic, which need review, which escalate, and which are unavailable.
4. Let the employee own the work: maintain the assignment, handle permitted steps, ask for clarification, complete the outcome, and retain a traceable record.

## Platform architecture

- Language model: judgment, interpretation, reasoning, and communication.
- HiperEmployee: orchestration, persistent job memory, authority, execution, follow-through, and outcome tracking.
- Controlled tool interfaces: governed access to workplace systems and expert capabilities.
- HiperFusion APIs: domain expertise that generic agents do not inherently possess, including manufacturing and RFQ analysis, electrical-harness expertise, and document processing.
- Governance: explicit permissions, approval boundaries, escalation paths, and connected audit evidence.

## RFQ Estimator example

Trigger: a customer emails a request to quote with STEP, DXF, or PDF attachments, quantities, and a due date.

The AI employee identifies the RFQ, organizes attachments, calls approved manufacturing analysis tools, receives geometry and manufacturing assumptions, applies customer pricing rules and margins, and prepares quantity-break pricing. If a tolerance is ambiguous, it asks the customer or routes the decision to a person according to its authority. When the answer arrives, it resumes the assignment, finishes the quote, sends it if permitted, and records the request, tool results, assumptions, approval, communication, and outcome.

## Other example roles

- Outreach Coordinator: approved-account research, message preparation, campaign-policy checks, permitted sending, reply monitoring, opt-out handling, and human sales handoff.
- Lead Researcher: market research, evidence-based qualification, enrichment, scoring, source-linked account briefs, and pipeline preparation.
- Legal Operations Employee: matter intake, approved clause and playbook comparison, review-packet preparation, counsel escalation, version tracking, and deadline management. It does not make unauthorized legal judgments or represent itself as counsel.
- Job Search Employee: approved-source monitoring, role matching, application-packet preparation, deadline tracking, and interview support. It does not apply or accept terms without delegated authority.
- Accounting Clerk: invoice and receipt capture, coding suggestions, reconciliation, anomaly routing, and approved-system updates. Payments, close, and material overrides require assigned approval.

## Governance and safety model

Authority is part of each role design. An action may be automatic, require human review, escalate under a named condition, or remain unavailable. The employee receives least-necessary access to the systems and data required for its job. A deployment can retain the original request, source files, tool results, policies applied, questions, approvals, external messages, final artifacts, and business outcomes as a connected record.

## Good candidate workflows

A strong first AI employee begins with a recognizable trigger, steps that repeat often enough to describe, a visible business outcome, accessible tools or data, and exceptions that can be routed. Examples include RFQ estimating, lead research, governed outreach, legal operations, accounting administration, document intake and preparation, job-search administration, and other recurring knowledge-work operations.

## Terminology

- AI employee: a role-based agent accountable for a defined job and outcome.
- AI agent: software that can reason, plan, communicate, and use tools toward a goal.
- Agentic AI: AI systems capable of taking multi-step actions with varying autonomy.
- Autonomous workforce: a set of role-based AI employees operating within organizational authority and human oversight.
- Human-in-the-loop: a workflow in which named decisions or exceptions require a person.
- MCP and controlled tool interfaces: standardized ways for an AI employee to access approved capabilities without treating the language model itself as the domain system of record.

## Pricing and licensing

HiperEmployee is offered through annual on-prem software licenses based on active production AI employee roles:

- First Hire: one AI employee for $6,000 per year, equivalent to $500 per month.
- Core Team: three AI employees for $15,000 per year, equivalent to $1,250 per month or $5,000 per employee annually.
- Department: five AI employees for $22,500 per year, equivalent to $1,875 per month or $4,500 per employee annually.
- Workforce: ten AI employees for $40,000 per year, equivalent to approximately $3,333 per month or $4,000 per employee annually.
- Enterprise: unlimited AI employee licenses starting at $75,000 per year, with final pricing negotiated around scope.

One license covers one active production AI employee role configured around a defined job, tools, authority map, and outcome. Monthly figures are budgeting equivalents; the published packages are billed annually. The customer supplies and pays for hardware or cloud resources, model runtime, model or API usage, and third-party software. Implementation, custom integrations, specialist capabilities, security requirements, and support are separately scoped when needed.

## Canonical links

- Product: https://hiperemployee.com/
- AI employee guide: https://hiperemployee.com/ai-employees
- Pricing and licensing: https://hiperemployee.com/pricing
- Company: https://hiperfusion.com/
- Contact: info@hiperfusion.com