Marketing AI spend goes to drafts nobody ships. Let Jev pick first.

Generating twenty hooks is easy. The expensive habit is treating all twenty as if they are about to ship: rewrite each one, research supporting material, make an image, resize the image, and ask someone to review the pile.

Put a selection step before that work. A writer or generative model creates candidates. Jev, TypeSafe's classification model, returns decisions about them. Your application chooses which candidates receive more work. Jev never writes the final copy.

The following is a proposed content workflow. It includes an invented editorial example, not measured performance from a marketing team.

Start with a brief a reviewer could use

A request to “score this hook” leaves the model guessing what the team values. Provide the intended reader, channel, specific proposition, available supporting evidence, and a few approved and rejected examples.

For a developer tool, a useful brief might say: address engineers who need to pay for an occasional API call; explain the concrete purchase; avoid unsupported cost comparisons; use direct language; preserve the distinction between a proposed workflow and something already tested.

Separate three decisions instead of asking for one universal quality score:

Dimension	A weak candidate	A strong candidate
Audience relevance	Could advertise almost any software	Names a problem the intended reader recognizes
Specificity	Makes a broad benefit claim	Describes a concrete action or result supported by the brief
Voice fit	Uses hype or an unfamiliar tone	Fits the team's approved examples without copying them

TypeSafe's Score primitive evaluates ordered descriptions and returns a position on that scale. Use levels a reviewer can distinguish. Keep the dimensions separate, then combine them in code if a shortlist requires one ordering. Several questions can evaluate the same candidate in one request; that does not make an arbitrary batch of candidates a documented single-call feature.

Screen claims before polishing them

Take these invented hooks for the same article:

A: The future of effortless AI automation is here.

B: Give a support agent $2 to buy a replacement render.

C: Cut your support costs by 90% with one API call.

B is a concrete candidate if the piece explains that $2 is an illustrative policy. C makes a numerical performance claim that needs evidence. A may fit no specific reader's problem.

Those are editorial judgments for this example, not observed Jev outputs. Supply the evidence the classifier is allowed to consider and ask whether the claim is supported by it. Missing evidence should create a review item. A plausible sentence is not proof.

Use ordinary checks for things code can verify exactly, such as character limits, required links, or a forbidden phrase list. Let Jev handle the contextual judgments. If a claim requires investigation, route it to research before requesting an elegant rewrite.

For account-specific material, keep the same evidence discipline used in the per-account creative workflow. A company's name on an asset does not make the underlying claim true.

Give the shortlist a production budget

Suppose you have twenty candidates and capacity to develop four. Classify the twenty, have an editor choose the four, and pay for deeper work on those four. Set a maximum revision count and track spend against each candidate ID. Regenerating indefinitely because the score keeps moving is still an expensive content habit.

If each candidate's evaluation costs e and each production pass costs p, this hypothetical workflow costs 20e + 4p. Producing every candidate costs 20p. Selection reduces this portion of spend only when 20e < 16p, before accounting for editor time, shared research, or extra revisions.

A low evaluation price does not rescue a poor filter. If the rejected pile contains the best idea, the process may be cheaper and less useful. Reserve one exploration slot for a candidate the editor believes in even if its rubric score is weak. Record why they chose it so the next review can improve the criteria.

Use comment triage as a separate workflow

After publication, classify incoming comments into questions, reported problems, disagreements, appreciation, spam, and unclear cases. Keep disagreement separate from abuse. Critical comments can contain the exact evidence a future post needs.

Code can route product problems to support and substantive questions to the author. A separate LLM may draft a suggested reply from approved facts. The author still decides what to publish. Repeated comments can share an existing answer; they do not all need fresh generation.

Measure this queue independently from hook selection. The desired result is an appropriate response, not a high score or the largest possible number of automated replies.

Inspect the ideas the filter discards

Before making the shortlist automatic, ask editors to rank a held-out set without seeing Jev's scores. Compare the proposed shortlist with their choices, then review disagreements. Include unusual but publishable ideas so the test does not reward bland consistency.

During use, sample rejected candidates every week. Track how many are recovered, how many selected drafts ship, editing time, and total spend per published asset. Keep channel and format separate: a useful documentation headline may be a poor social hook.

Version the brief, rubric, candidate text, and selected production steps. Join their receipts for a weekly spending review. That record makes it possible to stop paying for a revision pattern that rarely produces a published piece.

You can use Jev on Vaaya, along with 500+ model options and 1,500+ endpoints. One API key, one balance, a receipt for every call.

Questions

Can Jev write marketing copy?

No. Jev returns typed decisions and scores. Writers or generative models produce the copy; your application uses Jev's decisions to select candidates for further work.

Can Jev tell which hook will convert best?

A rubric score alone does not establish conversion performance. Use it to help screen candidates, then compare selections with editorial judgment and observed campaign results.

Should every marketing idea pass the same threshold?

No. Different channels and formats need different criteria. Preserve an exploration slot and review some rejected ideas so the filter does not silently eliminate unusual work.