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How teams use Spark

Choose a repeated job, gather examples and keep a person responsible for approving changes.

Start with one repeated job, real examples and a person who signs off. Your team chooses the job, provides the examples and approves changes.

These are possible uses, not customer stories or measured results. Spark Loops has no published results yet.

Team by team

Sales and business development

Train an agent to sort inbound leads by fit or draft follow-ups. Start with past leads and replies that show what good work looks like. A person checks and sends each message.

Support

Train an agent to route tickets or draft first replies. Use past tickets, including difficult cases. A person reviews replies before they reach a customer.

Operations

Test instructions for onboarding steps or reviewing process changes. Include examples of steps that must not be missed. Your team decides what to use.

Marketing and content

Train an agent to check drafts against your tone and claim rules. Include examples of wording you want to avoid. A person approves publication.

Document review

Test an assistant that checks documents against your own scoring guide. Review its findings and the proposed instructions. Keep a person responsible for the decision.

Solo founders

Choose a job you repeat often, such as sorting leads or drafting welcome notes. Start with a small spending limit and review the results yourself.

vibe/vibe builders

Choose a repeated job in your project. Start in the desktop app and share what you learn with the community. Grok Bot chat training is coming soon.

Your first run

  1. Agree on one job and who owns its examples.
  2. Gather real examples, including known mistakes.
  3. Choose AI reviewers from different model providers.
  4. Run a short session on one computer with a small spending limit.
  5. Read the results and instructions before approving an upgrade.

See Your first agent, Running a training run and Reading results.

Current limits

Spark Loops is a desktop app, not a hosted team service. It changes instructions rather than retraining the model. Agents draft; people send. Test results cannot guarantee how an agent will answer every new case.