Use cases

For AI content tools

You have solved the writing. Publishing is the other half, and it is more tedious than it looks.

The problem

Generating a post is the interesting problem. Getting it onto nine networks, each with its own limits, media rules and failure modes, is not, and it is where the roadmap quietly goes.

What PostLake does about it

Generate, then publish

Hand the output straight to one call. No per-network formatting layer of your own.

Validate before you spend

Check a draft against a network's real rules for free, before publishing, and get back the same codes and fixes a real publish would return.

Model-readable failures

Errors carry a code and a fix written so a model can correct the draft and try again, rather than surfacing a status code to your user.

Measure what worked

Analytics come back comparable across networks, so your tool can learn which of its own outputs performed and generate accordingly.

How it fits together

Generate, call validate to check it fits, publish, then read analytics back into whatever decides what to make next.

curl -X POST https://api.postlake.dev/v1/posts \
  -H "Authorization: Bearer $POSTLAKE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"text": "…", "accounts": ["acc_…"]}'

Common questions

Can I check a generated post before publishing it?

Yes, and it costs nothing. Validation runs the same checks as a real publish and returns the same codes and fixes, without creating a post or spending anything.

How do I handle a caption that is too long for one network?

You get told which network, by how many characters, and that you can set a shorter caption for that network alone. Your model can act on that without a person reading it.

Can the tool learn from performance?

Analytics return in one normalised shape across networks, so comparing outputs is arithmetic rather than a per-platform mapping job.

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