Running RunAI as a service, AI Code Tools

RunAI Coder Agency Implementation, Productized Code Delivery

Learn how to package RunAI's natural-language-to-PR automation into retainer services for development clients. This course covers setting up approval gates, structuring evidence receipts for client sign-off, and scaling parallel agent workflows across multiple concurrent projects to maximize per-delivery billing.

Open the decision record for RunAI

What does running RunAI for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Not published (RunAI does not publish pricing tiers)
Time to first value
Not published
Payback
Not modeled
Guided implementation
8 hours

Is RunAI worth running as a client service?

RunAI's evidence-based delivery and parallel workers offer promise, but its lack of published pricing and limited client-facing features require careful evaluation. The evidence supports technical viability, but financial viability remains unknown.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • API access to RunAI
  • Client's code repository credentials
  • CI/CD pipeline integration (e.g., GitHub Actions)

People and inputs

  • Technical staff to manage RunAI implementations
  • Example tasks to validate capability
  • Repository access for testing

Included with the course

7 working documents for delivering this service.

  • RunAI Client Onboarding Checklistchecklist
  • Evidence Receipt Template for Client Approvaltemplate
  • Parallel Agent Task Breakdown Worksheetworksheet
  • Approval Gate Configuration SOPsop
  • Monthly Delivery Metrics Dashboard Guideguide
  • Retainer Service Pricing Model (Usage-Based)template
  • Repository Integration Runbooksop

Listed by name. These documents are not yet published as individual downloads.