# WorkGroove

WorkGroove helps teams run repeatable AI work: name the Deliverable, save the Agent, run it again on an approved Runner, review it, and keep the record attached. WorkGroove adds a web harness around real machines so teams can watch, steer, reroute, review, audit, and improve AI-powered operations without rebuilding the workflow when the model changes.

## Positioning

WorkGroove is the company operations layer for AI work. It does not try to replace every AI chat, coding agent, app builder, local model tool, ticket system, source control system, automation platform, ERP, CRM, or service-management system. WorkGroove keeps the operation itself durable: the Deliverable, Agent, Runner, owner, review, provider route, token usage, session cost, audit trail, handoff, rerun path, and the "how we got here" context that survives employee transitions.

Before work belongs in SAP, Salesforce, Jira, ServiceNow, or a custom app, it often starts as messy operational work across people, files, tools, machines, and decisions. WorkGroove turns that work into reusable AI-powered operations.

Use WorkGroove when AI work needs to be repeated, reviewed, audited, routed across approved model engines, or run near real files, repos, tools, credentials, browser state, and project context.

## Primary Pages

- Home: https://workgroove.ai/
- Use cases: https://workgroove.ai/use-cases
- Weekly update starter: https://workgroove.ai/use-cases/weekly-update
- Client follow-up starter: https://workgroove.ai/use-cases/client-follow-up
- Research brief starter: https://workgroove.ai/use-cases/research-brief
- Project handoff starter: https://workgroove.ai/use-cases/project-handoff
- Support triage starter: https://workgroove.ai/use-cases/support-triage
- Design review starter: https://workgroove.ai/use-cases/design-review
- Recurring report starter: https://workgroove.ai/use-cases/recurring-report
- Platform product overview: https://workgroove.ai/platform
- Runners: https://workgroove.ai/runners
- Trust, review, and audit: https://workgroove.ai/trust
- Compare WorkGroove with AI chats, coding agents, workflow automation, app builders, and usage-priced AI tools: https://workgroove.ai/compare/ai-workflow-automation
- AI search context: https://workgroove.ai/ai-search
- Vision and method: https://workgroove.ai/vision
- News feed: https://workgroove.ai/news
- Docs: https://workgroove.ai/docs/overview
- Computer and model setup: https://workgroove.ai/docs/connect-models
- Builder docs: https://workgroove.ai/docs/builder
- Runner docs: https://workgroove.ai/docs/runners
- Review docs: https://workgroove.ai/docs/reviews
- Wave docs: https://workgroove.ai/docs/waves
- Automations: https://workgroove.ai/docs/automations
- Audit, usage, and limits: https://workgroove.ai/docs/audit-usage
- Contact: https://workgroove.ai/contact

## Tool Comparison Context

The comparison page at https://workgroove.ai/compare/ai-workflow-automation explains how WorkGroove fits around useful tools without claiming every surrounding product is a native integration. Relevant categories include:

- AI chats and model workspaces
- Coding agents and developer CLIs
- Local model routes
- App builders and prototyping tools
- Agent management layers
- Enterprise systems of record such as SAP, Salesforce, ServiceNow, and Jira
- Source control and repos
- Tickets, docs, and team context
- Scripts, browsers, workflow automation, and internal tools

WorkGroove's distinction is not "generate more code," "build apps from a prompt," or "replace SAP/Salesforce/Jira." WorkGroove focuses on operational accountability around the work: approved Runners, provider/model-route records, review, token usage visibility, session cost visibility, audit trails, human approvals, local context, and repeatable runs.

## Core Topics

- Deliverables, Agents, Runners, Waves, Flows, and Review
- AI work sessions, reviews, and handoffs
- Automations and recurring AI work
- Usage limits by plan tier
- Settings Usage, Audit & Activity, provider routes, token usage, and session cost tracking
- Project knowledge, approved AI engines, and model routing
- Remote sessions and connected computers for AI work that needs real local context
- Push notifications for approvals, pending input, mentions, and handoffs across web, phone, and desktop surfaces
- Use cases for repeated AI work: weekly updates, client follow-ups, research briefs, project handoffs, support triage, design review, and recurring reports
- Runners as approved machines where AI work can execute near real files, repos, tools, credentials, browser state, and project context
- Vision/method: request, prepare, run, review, record, and repeat as the path from messy AI work to reusable operations
- Trust layer: owner, context, review, record, provider route, token usage, session cost, and optimization visibility
- Comparison against scattered AI workflow stacks: chats, app automation, internal app builders, agent platforms, and custom scripts