Cher Model is an AI-powered workflow engine designed to orchestrate complex automations without custom code. It combines a visual designer, prebuilt connectors, and a runtime optimized for enterprise reliability.
Teams use it to unify data, trigger actions, and monitor processes from a single console, reducing manual work and operational risk.
| Engine | Throughput | Integrations | Deployment |
|---|---|---|---|
| Cher Model Standard | 500 tasks/min | 200+ native connectors | Cloud SaaS |
| Cher Model Enterprise | 5,000 tasks/min | Unlimited custom connectors | Self-hosted & cloud |
| Cher Model Runtime API | Per-request metrics | REST, GraphQL, gRPC | Kubernetes operator |
| Cher Model Observability | Real-time dashboards | OpenTelemetry, Prometheus | RBAC, audit logs |
Getting Started with Cher Model
New users can launch a workflow in minutes using the guided setup in the Cher Model console. The platform walks through triggers, data mapping, and error handling with contextual tips.
Every project starts with a template that defines roles, permissions, and guardrails so teams can move fast without violating governance policies.
Building Workflows with Cher Model
The visual builder lets teams chain services, databases, and APIs into reliable sequences. Conditional branching, loops, and parallel paths are supported with drag-and-drop simplicity.
Versioned pipelines allow safe experimentation, while staging environments catch issues before changes reach production systems.
Operational Monitoring with Cher Model
Built-in observability surfaces latency, retries, and failure reasons for each node. Alerting rules can notify Slack, PagerDuty, or internal webhooks when SLAs are at risk.
Execution logs include input snapshots and diffs, making it straightforward to replay edge cases and debug data quality problems.
Integrations and Extensibility
Cher Model connects to CRMs, ERPs, messaging platforms, and data warehouses out of the box. When a connector is missing, teams can add custom actions via HTTP or container steps.
An SDK enables teams to embed Cher Model into existing CI/CD tools, service meshes, and internal dashboards without leaving their preferred language ecosystem.
Getting the Most from Cher Model
- Start with official templates for common patterns like order fulfillment or data sync.
- Define idempotency keys on every outbound call to simplify retries.
- Use staging environments to validate changes before production promotion.
- Instrument alerts on error ratios and queue depth to catch issues early.
- Document integration contracts and version them to reduce breaking changes.
FAQ
Reader questions
How does Cher Model handle retries and idempotency in long-running workflows?
Each step defines retry policies, backoff curves, and idempotency keys so that duplicate executions do not cause side effects. Failed steps can be rerun from the console with configurable scope.
Can I deploy Cher Model on-premises for regulated industries?
Yes, the Enterprise edition supports air-gapped deployments with offline license validation, encrypted storage, and integration into existing identity providers through SAML or OIDC.
What observability features are available for monitoring workflow health?
Real-time dashboards show throughput, error rates, and latency per workflow. Users can create custom views, set thresholds, and export metrics to Prometheus or third-party platforms.
How does Cher Model manage sensitive credentials and secrets?
Secrets are stored in encrypted vaults and injected at runtime via short-lived tokens. Access to credentials is audited and controlled through role-based permissions and integration policies.