A poisoned training set turns your model into an attacker's tool. A single mislabeled class changes a prediction, a decision, an outcome your customer trusted. None of it shows up in your logs.
Antidote monitors your AI pipeline at the data layer —
detecting, healing, and documenting poisoning, mislabels, and silent corruption
before they shape your model, your outputs, or your reputation.
| Name | Type | Samples | Status | Issues |
|---|---|---|---|---|
| 5008_Federalist_Papers.docx | Text | 1,840 | ⚠ Warning | 4 |
| chest_xray_pneumonia | 2D Image | 11,712 | ⚠ Warning | 420 |
Clean data doesn't just reduce risk — it directly improves model performance, security posture, and compliance readiness.
Removing mislabeled, poisoned, and low-quality samples raises the ceiling of what your model can achieve — and clean data means fewer epochs to get there. Less tuning wasted on noise. Faster iteration cycles. You reach your target accuracy sooner and at lower compute cost.
Detect and eliminate backdoors, adversarial patches, prompt injections, and poisoning attacks before they reach production. A model trained on clean data is a model without hidden triggers — and a security posture your investors can verify.
Catch data issues before training begins. Eliminate costly retraining cycles caused by corrupted datasets discovered post-deployment. Every hour of GPU time spent on bad data is budget you don't get back.
Fully configurable audit reports aligned with GDPR and the EU AI Act. Each report section is a Data Field you control — define which fields appear, structure, and export format. Ready for regulators, investors, and enterprise procurement.
Our proprietary engines work in concert across your pipeline — from the moment data enters your system to the audit log that evidences its integrity. Each stage is configurable, explainable, and built for AI-specific threat vectors that general data quality tools were never designed to catch.
Statistical engines learn the internal structure and relationships of your data. Samples that deviate from learned representations are flagged — and sequential engines classify the type: mislabeled, poisoned, or low quality.
Unsupervised · No baseline neededAntidote can automatically relabel based on feature cluster position, remove poisoned or degraded samples, or queue for human review — configurable by confidence threshold to fit your workflow.
Auto · Manual · HybridEvery flagged sample is reviewable in the UI. Your team sees exactly what was changed, why it was changed, and the data's position in latent space. No black box. Complete visibility into the decision.
Latent space explorer · Sample previewFully configurable audit reports — each section is a Data Field you control. Choose which fields appear, define the structure, and export in your preferred format. Aligned with GDPR and EU AI Act requirements.
GDPR · EU AI Act · Configurable exportEvery report is composed of Data Fields — you choose which fields appear, define the structure, and select your export format. Designed for compliance teams, security audits, and enterprise procurement. Every finding is traceable, every remediation documented, every report exportable on demand.
Runtime and model-layer tools catch problems after the fact — after your model has already learned from compromised data. By then, the damage is inside the system.
Antidote operates at the data layer — before anything reaches your model. Threats are caught at ingestion, during training, and across your RAG corpus. Prevention, not reaction.
Explore, split, and validate your datasets visually before committing to training. Antidote flags bias, coverage gaps, and class imbalance — so you know what you're building on.
Import tabular datasets (CSV, Parquet, Arrow) and explore them visually before committing to training. Split into train/validation/test sets — and Antidote will tell you if the split can be created without introducing bias.
Six universal threat categories apply across every sector, pipeline, and model type. Each one is a failure mode Antidote is purpose-built to detect, remediate, and document.
Malicious inserts, compromised suppliers, insider changes, synthetic data abuse. Detects subtle and intentional alterations that mimic normal data and evade traditional anomaly detection.
Human mistakes, inconsistent guidelines, label leakage, class imbalance distortions. Sequential engines identify deviation type — mislabel vs. poison vs. quality degradation.
Pipeline bugs, sensor changes, seasonality, new populations, domain shift. Continuous monitoring with "why changed" signals to identify the source before it affects training.
PII/PHI, secrets, regulated data crossing boundaries, retention violations. Detection and redaction before data reaches model training or LLM summarization pipelines.
Hidden instructions, malicious documents, contradictory sources, authority spoofing. Corpus monitoring flags suspicious inserts and contradiction patterns across your knowledge base.
Models learning the wrong pattern — performing in testing while failing silently in production. Antidote's dedicated Bias & Shortcuts scan reveals what your model actually learned and why.
Audit trails documenting every detection, remediation, and access event — supporting GDPR's requirements for explainability and data traceability in automated decision systems.
The EU AI Act mandates monitoring of data integrity and data drift for high-risk AI systems. Antidote's reporting module is purpose-built to satisfy these requirements with configurable, export-ready reports.
Integrates directly into ML pipelines via API or web interface. Webhooks, CI/CD compatibility, and MLOps toolchain interoperability — no disruption to your existing workflow.
Most teams see their first findings within hours.
No disruption to your stack. No data leaves your environment.