Airno started with a simple observation: every AI detection tool we tested relied on a single model and refused to show its work. When those tools were wrong, users had no way to know why. We built something different.
Existing AI detection tools were black boxes. A single neural classifier would return a percentage with no explanation of what triggered it, no confidence interval, and no acknowledgment of its own limitations. Educators were making consequential decisions based on a number they could not interrogate.
Worse, those tools were brittle. A single paraphrasing pass would drop detection rates dramatically. Models fine-tuned on GPT-3 data failed on GPT-4 output. New models from Anthropic or Google were invisible to detectors trained before their release.
We built Airno to be transparent about what it knows, honest about its limits, and structurally resistant to the failure modes that plague single-model detectors.
7 independent detectors vote on every submission. No single model controls the outcome. When detectors disagree, that disagreement is visible in the confidence interval, a signal that you should treat the result with more caution.
Statistical analysis (perplexity, burstiness, Zipf distribution), two fine-tuned transformer classifiers (RoBERTa and DeBERTa), 190+ linguistic pattern rules, and, for images, CNN artifact detection, frequency domain analysis, and metadata forensics. Each targets a different failure mode.
Airno does not return a binary pass or fail. It returns a calibrated confidence score with a per-detector breakdown and a reliability indicator based on text length. Short texts receive lower reliability scores because the statistical signal is thin.
We publish our accuracy numbers: 98%+ on benchmark test sets for unedited AI text, 85-92% in real-world conditions. We publish our false positive rate (~8-15%). No detection platform should claim perfect accuracy.
Transparent, accurate AI detection for anyone who needs it. Free, with no account required.
AI-generated content is not inherently bad. But in contexts where authenticity matters (academic submissions, journalism, job applications, legal documents) people deserve reliable tools to verify what they are reading. The person pasting text into Airno is making a consequential judgment. We owe them honesty about how reliable our output is.
That means publishing accuracy numbers, showing confidence intervals, acknowledging the limits of detection on short or paraphrased text, and never overstating what the technology can do.
Zipf distribution, sentence entropy, vocabulary richness, and burstiness patterns that differ between human and AI text.
RoBERTa and DeBERTa transformer models fine-tuned on large datasets of human vs. AI-generated text from multiple model families.
190+ rules targeting hedging language, transition overuse, vague citations, and formulaic structures common in LLM output.
Metadata analysis, frequency domain artifact detection, CNN-based classification, and noise consistency checks for images.
We are actively improving our detection models and expanding support to audio and video content. Results should be treated as informational, not definitive. Detection accuracy improves significantly with longer text samples. 30+ words is the minimum; 100+ is recommended.