AI Employees
An Ema AI Employee is an agentic AI mesh designed to automate complex enterprise tasks end-to-end. AI Employees work independently across your organization, connect to over 200 SaaS applications and internal APIs, and continuously improve through human collaboration and feedback.
Unlike traditional RPA bots or copilots, AI Employees can understand context, formulate plans, request clarification, and leverage feedback loops to improve over time. Every AI Employee is powered by the Generative Workflow Engine (GWE) and EmaFusion™, Ema's proprietary Mixture-of-Experts model.
AI Employee Lifecycle
An AI Employee follows a four-stage lifecycle:
1. Create
Start from a pre-built template or create a custom AI Employee from scratch. Templates are available for common domains (Customer Experience, Sales, HR, Healthcare, BFSI, and more). Each template includes a GWE workflow that can be further customized.
To create an AI Employee:
- Navigate to the AI Employees page.
- Click Create to open the Templates screen.
- Browse by category or search by keyword.
- Click + Create on a template card, or select Custom AI Employee to start from a blank workflow.
Only users with the Admin or Builder tenant role can create AI Employees. See Workspaces and Tenants for role details.
2. Configure
After creation, configure the AI Employee:
- Workflow: Open the Workflow Builder to define or customize the GWE workflow -- the sequence of agents, data flows, and branching logic.
- Shared Configuration: Add resources used by multiple agents, such as connected applications, data sources, and default EmaFusion settings.
- AI Employee Outputs: Define the named outputs this AI Employee produces (see Named Inputs and Outputs).
- Integrations: Connect to enterprise applications for data retrieval and action execution.
3. Deploy
Once configured, the AI Employee is available to end users through one or more interaction interfaces. Deploy by saving and publishing the workflow. AI Employees can also be shared to child tenants via template sharing.
4. Monitor
After deployment, monitor AI Employee performance through:
- Audit logs: Review configuration changes, workflow publishes, and user access events for the AI Employee. Audit logs are queryable per-AI-Employee and per-tenant.
- Metrics: Track usage, accuracy, and response times.
- Feedback loops: Collect per-response feedback from end users to drive continuous improvement.
- Version history: Review and compare previous workflow versions. See Workflow Version History.
Interaction Interfaces
AI Employees can be surfaced through purpose-built interfaces or embedded directly in existing tools. Each interface runs the same underlying workflow, so guardrails, audit logs, and learning loops remain consistent everywhere.
| Interface | Description |
|---|---|
| Web-app Chat | Browser-based conversational workspace at app.ema.co (or customer-specific domains). Supports "Show Work" explanations, paragraph-level source citations, and per-response feedback. |
| Ema Chat | Embeddable conversational interface for Slack, Teams, Google Chat, and website embedding. |
| Voice | Real-time voice interaction via telephony or browser-based calling. Supports configurable voice settings, conversation parameters, call handling, data storage, VAD (Voice Activity Detection), and feedback collection. |
| Agentic Dashboard | Spreadsheet-style view for high-volume, repeatable workflows (triage queues, data enrichment). Supports structured outputs, API/CSV export, and per-cell source inspection. |
| Document Editor | Collaborative editor for long-form documents (300+ pages) with iterative regeneration, section-level collaboration, version control, and status management. |
| Application Integrations | Integration with existing systems such as ticketing and case management platforms. |
| API | Endpoints for invoking AI Employee capabilities from any enterprise system or custom application. |
The interface is selected at creation time and cannot be changed afterward.
Template Categories
Ema provides ready-to-deploy templates organized by industry and function:
- BFSI -- Banking, financial services, and insurance automation
- Customer Experience -- Support ticket resolution, FAQ handling, escalation
- Employee Experience -- HR tasks, onboarding, internal helpdesk
- General -- Knowledge base Q&A, document summarization, data enrichment
- Healthcare -- Clinical documentation, patient communication
- Sales Experience -- Lead enrichment, SDR outreach, proposal generation
Each template card shows the template name, a short description, and a + Create button. After clicking Create, the subsequent configuration steps are the same regardless of the template chosen.
EmaFusion
EmaFusion is the proprietary Mixture-of-Experts model that underpins all AI Employees. For each subtask, EmaFusion:
- Assesses complexity and selects the most appropriate LLM from 40+ models across 9+ providers.
- Falls back to the next-best model if the initial response does not meet a confidence threshold.
- Continuously refines its routing based on real-time telemetry.
Key capabilities:
- Accuracy enhancement -- Routes each subtask to the best-suited model, often exceeding single-model benchmarks by double-digit percentages.
- Cost and latency control -- Enforces per-request policies to minimize unnecessary use of expensive models.
- Model agility -- Supports seamless integration of new open-source, proprietary, or customer-hosted models.
AI Employees vs. Traditional Automation
| Capability | RPA | LLM Copilot | AI Employee (Ema) |
|---|---|---|---|
| End-to-end workflow automation | No | No | Yes |
| Handles unstructured data (PDF, email) | No | Partial | Yes |
| Self-introspection and continuous learning | No | No | Yes |
| Orchestrates multiple specialized agents | No | No | Yes |
| Deployment speed | Months, brittle | Weeks, scripts | Days, composable |
Next: AI Employee Groups | GWE Overview