Most collaboration tools stop at the introduction. They help you find someone, exchange contact info, and then leave you with a blank document and the vague pressure of a shared deadline.
DraftKit has two AI features that pick up exactly where that blank document problem starts: SMART Match surfaces what you should write together before you commit, and SMART Draft builds the first structured outline after you do.
Here is exactly how both work under the hood.
SMART Match: AI Before the Commitment
SMART Match runs on the public booking page. When someone visits your DraftKit profile and enters their Substack URL into the collaboration request form, the AI kicks in before they even hit submit.
The analyze-collab-match edge function fires as soon as the URL is entered. It fetches the RSS feeds from both newsletters: yours and your potential collaborator’s. It sends both content libraries to the AI through the Lovable AI Gateway, which routes through Google Gemini and OpenAI models depending on load.
The function returns three specific collaboration topic ideas, each with a format recommendation and the source articles it used to generate the suggestion.
A typical result looks like this: a topic, a one-sentence explanation of why it works given both audiences, a suggested format (co-written article, interview, newsletter shoutout), and pointers to the specific posts from both newsletters that informed the suggestion.
Why RSS Feeds and Not Something Fancier
I could have built a richer content analysis layer. Profile scraping, engagement data, subscriber overlap estimates. I did not, for two reasons.
First, RSS feeds are public and reliable. Every Substack publication has one. The content is clean, structured, and requires no authentication. I do not need to scrape anything or handle authentication failures.
Second, topic matching based on what someone has actually written is more useful than matching based on anything else. If someone has published 12 posts about AI tooling and you have published 8 posts about product strategy, the overlap is obvious from the text. No engagement data needed.
The rate limiting reflects the cost model. Unauthenticated visitors get 10 SMART Match requests per hour. Authenticated users get 50. That is enough for real usage and keeps the edge function budget manageable.
SMART Draft: The AI Layer After the Yes
SMART Match happens before a collaboration is confirmed. SMART Draft happens after.
When a host approves a collaboration request, they can trigger a first draft through the generate-collab-draft edge function. The function does three things:
✅ Fetches RSS content from both participants’ newsletters
✅ Combines the content with the collaboration context: the type of collab, the date, any message the requester included
✅ Returns a structured draft object, not freeform prose
The returned draft is not a finished article. It is a scaffolded outline with a title, a hook, an ordered section breakdown with contributor assignments per section, talking points for each section, format suggestions, tone notes, and an estimated read time. The host reviews it in the workspace sidebar and decides whether to apply it to the editor.
The Re-Apply Guard
This is the detail I am most glad I built. The first version of SMART Draft had a straightforward “Apply to Workspace” button. Click it, the draft populates the editor, done.
The problem is what happens on the second click. If you have applied a draft, made edits, written two paragraphs of real content, and then accidentally click “Apply” again, you lose everything you wrote.
The re-apply guard prevents that. After a draft is applied for the first time, the button becomes a confirmation step. A prompt tells you that the workspace has been edited since the draft was last applied and asks you to confirm before proceeding. The re-inject only happens on explicit confirmation. Manual edits are never overwritten silently.
This was a bug I caught before it became a support ticket. Building the guard took less than an hour. Not building it would have cost someone a draft they could not recover.
No API Key Required
Both AI features run through the Lovable AI Gateway. This is worth explaining because it is a meaningful architectural choice.
Most AI product builds require you to manage your own API keys: OpenAI, Anthropic, Google. You handle authentication, rate limits, billing, and model selection. The Lovable AI Gateway abstracts all of that. The edge functions call the gateway endpoint with no external API key required.
The tradeoff is reduced model control. I am not selecting a specific model version or tuning temperature settings. For content matching and first-draft generation, that tradeoff is fine. The quality is consistent and the operational overhead is near zero.
If I ever need more control over the AI layer, the gateway is a single abstraction point to replace. One edge function to update, not a dozen places where API keys and model names are scattered.
What SMART Draft Does Not Replace
SMART Draft is not a ghostwriter. It is scaffolding.
The output is useful because it solves the specific problem of starting a collaborative document from scratch. Two writers, one blank editor, and the implicit question of who writes what. The draft answers that question with section assignments and talking points. Both parties know where to start.
The writing is still yours. The SMART Draft is a starting structure, not a finished article. In practice, most collaborations that use SMART Draft end up with content that looks nothing like the original draft by the time it is published. That is exactly what it is supposed to do: reduce the friction of starting without replacing the work of finishing.
The Storage Model
SMART Draft outputs are stored as JSON in collab_requests.ai_draft. The field preserves the full structured draft independently of the workspace content. If the host applies the draft and then the collaborator rewrites everything, the original SMART Draft is still in the database.
This matters for two reasons. First, it gives me product intelligence on which SMART Draft suggestions actually made it into published collaborations. Second, it means the draft can always be re-applied or reviewed from the sidebar without regenerating it.
The ai_suggestion_used field tracks which elements of the SMART Match output the collaborator actually included in their request form. That data feeds back into improving the matching logic over time.
Try DraftKit
SMART Match and SMART Draft are live on every DraftKit account. Try them at draftkit.app
If you are building AI features into a collaboration product and want a review of your AI interaction design before launch, book a product audit at promptledproduct.com/audits.