Our AI content QA service grades your model's generated articles, posts, emails, and ad copy against your rubric, so a wrong fact or an off-brand line gets caught before your audience sees it. Send 20 to 50 samples and get a graded report in 2 to 3 business days once scope is confirmed.
We grade outputs. We do not train models, run fine-tuning, or sell experts we cannot verify.
AI content generation QA is the systematic review of text your model writes before it reaches readers. Content teams use it for blog drafts, product copy, email campaigns, social posts, and anything else generated at scale. Good QA means every claim checked against your facts, the tone matched to your brand, and the formatting right. It is the same discipline as human evaluation, aimed at the content pipeline.
Generated copy looks finished. That is exactly the problem: the errors hide inside polished sentences.
The model writes a product stat, a date, or a quote that sounds exact and is simply made up. Readers trust clean formatting, so a fabricated number in a blog post travels further than an obvious typo. This is classic hallucination, and it is the first thing we grade.
A luxury brand ends up sounding like a meme account. A serious B2B report opens with slang. The words are all real and the facts are fine, but the voice is wrong for the audience. Tone drift is hard to catch with automated checks, which is why trained reviewers read for it.
Five hundred words that repeat the headline four ways. Generated content loves padding: generic openers, circular transitions, and conclusions that restate the intro. It reads fine on a skim and says nothing on a read. We grade substance, not word count.
Broken markdown, half-closed HTML tags, JSON with a trailing comma, headings in the wrong order. The copy is fine and the container is broken, so the page renders wrong or the downstream system rejects it. We check structure as well as sentences.
Every checkable statement in the sample gets compared against the sources you provide: product specs, pricing pages, help docs, press releases. A claim that contradicts your source is a P0 or P1 depending on how bad the damage would be. A claim with no source backing gets flagged as unverified, so you know where your blind spots are.
Send us your style guide or three pieces of copy you love, and we build the voice criteria into the rubric. Reviewers then grade each sample on diction, sentence rhythm, and register. This is the part automated evaluation struggles with most, and it is where human readers earn their keep.
Headings in order, links that resolve, lists that make sense, code blocks that are actually code. For content that feeds a CMS or an email tool, we also check the mechanical layer: character limits, subject line length, and any template placeholders the model was supposed to fill or leave alone.
One sample can be fine while the batch is stale: the same opening line in twelve articles, the same three adjectives in every product description, the same conclusion recycled everywhere. Readers notice the pattern even when no single piece is wrong. We read across the batch, not just within each sample, and report sameness as its own finding. If your pipeline generates variants, this is where you learn whether you have twenty ideas or one idea twenty times.
You send 20 to 50 model outputs plus your rubric, or we help you write one. Include your style guide and fact sources if you have them.
Trained reviewers grade every sample against the rubric, backed by automated checks for structure, repetition, and basic factual consistency.
Sample-level grades, issue patterns, recommended fixes, and a walkthrough call with your team.
A pilot runs on 20 to 50 samples. That is enough to surface the repeating failure patterns in most content pipelines. If you publish in many formats, we spread the samples across them so each format gets real coverage.
Yes. Send the guide, or send three pieces of copy that nail the voice and we will extract the criteria into the rubric. Voice grading is one of the main reasons teams choose human review over a purely automated check.
We check claims against the sources you provide. If a sample says your plan costs $29 and your pricing page says $39, that gets a P1 with the source cited. Claims with no source to check against are flagged as unverified rather than graded wrong.
Blog posts, product descriptions, email copy, ad variants, social captions, scripts, and help articles. Send whatever your pipeline produces: docs, CSV exports, markdown, or plain text. We adapt the rubric to the format.
No. We grade and recommend fixes, but we do not rewrite your copy. The report tells your team exactly what to change in prompts, templates, or the review workflow, and the walkthrough call covers how to apply it.
A pilot is a one-time deep grade. If the results are useful, many teams move to continuous monitoring, where new batches get graded on a schedule and you see quality trends over time.
Send 20 to 50 samples. Get every one graded, with the patterns and the fixes.
Not sure where to start? Talk to us and we will point you at the right evaluation.