AI Content Checker for Marketing

This tool uses deterministic pattern analysis — not a black-box neural network. It examines 30+ linguistic signals including sentence structure variance (burstiness), lexical predictability, hedge-word density, benefit-stacking patterns, transitional adverb usage, and formulaic opening/closing structures. Each signal is weighted by content type (ad copy, blog, landing page, etc.) because legitimate marketing copy naturally uses different patterns than academic text.

No AI detector is perfectly accurate — this is an inherently probabilistic problem. Research shows that text-only detectors can misclassify certain human writing (particularly non-native English) as AI-generated. Our approach uses calibrated confidence bands and never claims certainty: we show likelihood scores with explicit uncertainty, passage-level evidence for each flag, and content-type-specific baselines. High-stakes decisions should never rely on any detector alone.

Most AI detectors are calibrated for academic text. Marketing content naturally uses patterns that overlap with AI tells — benefit stacking, CTAs, organized structure — which creates false positives in generic detectors. Our tool calibrates thresholds per content type: a landing page legitimately uses benefit language, so we weight that signal lower for landing pages. This reduces false flags on legitimate marketing copy.

Model resemblance indicates which AI model family the writing style most closely matches based on lexical and structural patterns — ChatGPT-like (polished, transition-heavy), Claude-like (nuanced, caveat-heavy), or Gemini-like (factual, Wikipedia-like). This is stylistic similarity, not forensic identification. Human editing and mixed workflows reduce resemblance confidence significantly. The research consensus is that reliable model attribution from text alone is not dependable at high confidence.

Yes. Detection research consistently shows that light editing, paraphrasing, and human-AI collaboration reduce detector accuracy. Our tool accounts for this by classifying content into four categories: Likely AI-Assisted, Human-Edited AI, Mixed/Unclear, and Likely Human-Written. The 'Mixed/Unclear' category explicitly acknowledges when the signal is ambiguous rather than forcing a binary decision.

No. Content is processed in-memory during analysis and not stored, logged, or used for training. The analysis runs entirely within the API request lifecycle. We hash content for deduplication but do not retain the original text.

Our scoring is calibrated per content type. Ad copy, landing pages, blogs, emails, product pages, social posts, and case studies each have different baseline thresholds because they naturally use different writing patterns. A landing page with benefit stacking scores differently than a blog post with the same patterns, because benefit stacking is expected in landing pages.

All three use the same analysis engine but present results for different workflows. The AI Content Checker gives you the full diagnostic report — classification, scores, evidence, and recommendations. The AI Humanizer focuses specifically on rewrite suggestions for improving flagged passages. The Pattern Analyzer shows the raw coefficient breakdown for advanced users who want to understand the detection signals in detail.

No. AI detection is probabilistic and produces false positives — especially on non-native English writing. Using it punitively creates fairness risks. Instead, use it as a quality tool: does this content have the specificity, voice, and evidence that builds trust with readers? The recommendations focus on improving copy quality regardless of how it was produced.

Pattern coefficients are the individual signal scores from our detection engine. Each coefficient (e.g., 'benefit_stacking', 'hedge_word_density', 'transitional_adverb_frequency') measures a specific linguistic pattern on a 0-100 scale. High coefficients indicate the pattern is present at levels typically associated with AI generation. The advanced view lets you see exactly which signals contributed to the overall score — full transparency, not a black box.