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Email examplesSequencesRoboflow

Roboflow onboarding

5 emails over 9 days, counted from the day we signed up.

Clone this sequencePreview it for your brand
  1. Day 0First email
    Email 1 · WelcomeWelcome! Let's buildPrompt. Build. Deploy.
  2. Day 11 day later
    Email 2 · OnboardingBuilt on Roboflow latelyReal projects built with Roboflow across manufacturing, robotics, drones, and sports.
  3. Day 21 day later
    Email 3 · OnboardingFree credits and GPUs to label data and train modelsFree tier core workspaces include 10 credits each month at no charge. Here are three great ways to spend them.
  4. Day 31 day later
    Email 4 · OnboardingGive your coding agent access to RoboflowVision AI MCP
  5. Day 96 days later
    Email 5 · Onboarding5 common mistakes that keep models from being greatStart with 50-100 images, train early, and fix your data before your hyperparameters.

What each email does

  1. Day 0First emailWelcomeWelcome! Let's buildIntroduces Roboflow Agent as a way to build and deploy vision applications from a prompt. It outlines supported tasks and capabilities, then invites the reader to try or ask the Agent.
  2. Day 11 day laterOnboardingBuilt on Roboflow latelyIntroduces computer vision projects built with Roboflow across several industries. It points new users to example use cases they can explore and clone as a starting point.
  3. Day 21 day laterOnboardingFree credits and GPUs to label data and train modelsShows new users how to spend their monthly free credits on vision AI workflows, model training, and auto-labeling. Each use case is paired with a button to try it.
  4. Day 31 day laterOnboardingGive your coding agent access to RoboflowIntroduces the Roboflow MCP Server and shows how a coding agent can help with labeling, workflows, and inference. It guides new users to connect the server or read the setup documentation.
  5. Day 96 days laterOnboarding5 common mistakes that keep models from being greatRoboflow shares five common mistakes that can weaken computer vision models and offers practical advice on choosing a model, preparing data, and training early. It links readers to a full troubleshooting guide.

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