EvidenceLock
Audit dashboard for enterprises running vision and document AI in production that flags silent evidence-channel drift, when a model keeps producing the right answer but stops reading the same lines, regions, or charts it used to.
Compliance, risk, and MLOps leads at insurance, legal, and healthcare firms deploying document-AI
- Counterfactual evidence probes that re-route the model's attention across input regions and surface drift deltas
- Per-model reliance profile showing which page, chart, or field each answer is grounded in over time
- Alerts when a fine-tune or prompt update flips the dominant evidence channel without changing accuracy
- Evidence-grade report exportable for SOC 2, model risk, and internal audit reviews
Enterprises increasingly fine-tune multimodal AI on internal documents, but their governance teams cannot see when the model has silently shifted to a different region or table to reach its answer.
Strong enterprise pain signal: AI drift is repeatedly called 'the silent risk no one's managing' and a regulatory mandate for healthcare/insurance; drift detection demand is well-documented across multiple 2024-2026 guides.What Is AI Drift — And Why It's the Silent Risk No One's Managing ↗AI Model Drift & Performance Risk: Detection & Governance Guide ↗
Crowded adjacent space — Arize AI, Fiddler AI, Evidently AI, Vertex AI, and full MLOps suites already offer model monitoring and drift detection; no clear standalone tool focuses specifically on evidence-channel/grounding drift for document AI, but incumbents could add it as a feature.A Technical Leader's Comparative Analysis of AI Observability Platforms ↗Best MLOps Platforms Compared 2025 ↗
Enterprise observability/drift tools command premium pricing (Arize, Fiddler, etc. sell six-figure annual contracts to regulated buyers), and compliance teams have dedicated budget under ISO 42001 and NIST AI RMF mandates.AI Model Drift & Performance Risk: Detection & Governance Guide ↗Machine learning operations landscape: platforms and tools ↗
Trend is reinforced by arXiv paper on 'hidden evidence-use forgetting' in MLLMs, plus regulatory tailwinds (EU AI Act, ISO 42001, NIST AI RMF) making grounding-level audit a multi-year structural requirement for regulated document AI.Hidden Forgetting in Continual Multimodal Learning: When Accuracy Survives but Grounding Fails ↗Enterprise AI Compliance Evidence Management: Always Audit-Ready ↗
Building requires custom attribution/grounding-tracing pipelines over multimodal models, OCR + region mapping, baseline checkpoint comparison, and audit-grade reporting — non-trivial but achievable with existing CV and LLM tooling; not a weekend project.Hidden Forgetting in Continual Multimodal Learning ↗