Optimize training data quality and prevent model collapse. Filter synthetic content from web-scraped datasets and ensure your training pipelines produce reliable, high-quality models.
As AI-generated content floods the web, training data quality becomes a critical engineering problem.
Training on AI-generated data causes models to lose diversity and degrade over generations. Detect and filter synthetic content before it enters your training corpus.
Audit web-scraped datasets for AI contamination. Understand what proportion of your training data is human-authored versus machine-generated.
Integrate AI detection as a preprocessing step in your data pipelines. Automatically classify and route content based on detection confidence scores.
Verify that evaluation datasets and benchmarks contain genuine human-written text. Prevent contaminated benchmarks from producing misleading model performance metrics.
Access raw confidence scores rather than binary labels. Set custom thresholds that match your data quality requirements and risk tolerance.
Our ensemble combines perplexity analysis, cross-model scoring, and fine-tuned classifiers. More robust than any single detection method against diverse generation sources.
Built by ML practitioners for ML practitioners.
Our ensemble approach resists common evasion techniques including paraphrasing, token substitution, and watermark removal. Multiple detection signals provide redundancy against adversarial inputs.
Combines DistilGPT2 perplexity analysis, Binoculars cross-model scoring, and fine-tuned classification. Each model catches patterns the others miss.
Get per-sentence confidence values and overall document scores. Use raw probabilities for threshold-based filtering or categorical verdicts for quick triage.
Production-ready API designed for engineering workflows.
Clean, well-documented REST endpoints that integrate with any language or framework. JSON request/response format works natively with data processing tools.
Our API supports concurrent requests for parallelized pipeline stages. For large-scale batch workloads we build a custom integration around your pipeline.
Works seamlessly with Python data tooling. Simple HTTP calls from requests, httpx, or async frameworks like aiohttp. Easy to integrate into Airflow, Prefect, or custom pipelines.
JSON responses include document-level verdicts, sentence-level scores, and model-specific breakdowns. Parse and store results in your preferred format.
Versioned API endpoints ensure your pipeline integrations remain stable across updates. Breaking changes are introduced only in new API versions.
For sensitive data pipelines, deploy our detection models on your own infrastructure. Maintain full control over data flow and processing.
Integrate AI detection into your data pipelines today. Protect training data quality with production-ready detection via our REST API.