Turn every merged PR into a published changelog, a release post, and the launch content that drives traffic back.
AI creates changelog entries from your Git commits, PRs, and merged feature requests automatically.
Changes are categorized into features, fixes, improvements, and breaking changes — with severity flags for releases.
Every changelog entry can be promoted to LinkedIn, X, a blog post, an email, or scheduled across all of them in one click.
Most teams ship the code and never get around to writing the launch. Changelog entries get drafted on Slack, never make it to the website, and the customers who would have benefited from the new feature simply never find out. Changelog-to-launch automation flips that: as soon as a feature lands in main, AI Expedite drafts a customer-facing entry, derives the launch assets from it, and stages a campaign you can publish with one click.
When a PR merges, AI Expedite reads the diff, the linked feature request, the commit messages, and any Linear or Jira metadata, then drafts a changelog entry in your house voice. The draft includes the customer-facing benefit (what changed for the user, not what changed in the code), the affected surface, and any caveats — flags for breaking changes, schema migrations, or behavior changes that need announcement. You review the draft in the workspace; approval publishes it to your changelog page and triggers the downstream launch flow.
The same draft becomes the seed for everything else: a LinkedIn post in your brand voice, an X / Twitter thread, a blog post if the change warrants it, an in-app announcement banner, an email blurb for the next release roundup. You pick which channels each entry goes to. The system maintains channel-specific tone and length — a 220-character X post is not just a truncated LinkedIn post — and remembers what's already been posted so you don't double-publish.
AI Expedite tracks how each launch performs: post impressions, link clicks, downstream conversions, and the lift in usage of the feature that shipped. Over time, the system learns which kinds of changes (UX improvements vs. integrations vs. AI features) get the most engagement on each channel, and tunes future drafts accordingly. That's the Launch loop: every shipped feature gets a chance to become pipeline, and the pipeline data informs which changes get prioritized next.
Changelog discipline is something teams plan to start doing 'when we have more bandwidth.' The automation is for the team that never has bandwidth — five engineers, no marketing seat, a Slack channel that fills up with merged PRs nobody outside the team will ever see. AI Expedite turns each one into a customer-visible story without anyone having to context-switch into the marketing tools.
AI Expedite hosts your changelog at a path on your marketing site (or a configured subdomain). You can also push entries to a GitHub releases page, a Notion doc, or any endpoint that accepts webhooks.
Yes. Every draft sits in the workspace for review. You can edit the copy, flag the severity, remove channels, or reject the entry entirely. Nothing publishes without explicit approval.
The draft model reads the diff, the PR description, and any linked feature request, plus the changelog history for your project to learn your bar. Internal-only changes — refactors, test additions, dependency bumps — are filtered out or rolled into a single 'under the hood' entry.
Only if you opt in for that channel. The default is draft-and-stage: posts wait for approval. Teams that ship many features per week typically auto-publish to internal channels (in-app banner, email roundup) and review-then-publish to external ones.
Yes. The changelog pipeline reads from your GitHub repos directly. You don't have to be running Claude Code or Codex orchestration to use changelog-to-launch automation — though the two are designed to compose.
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