Interpret AI provides a platform that ingests multimodal execution traces—text, video, audio, images, and DOM states—to automatically flag failures, annotate data, and deliver structured root‑cause analysis at scale. Its Data Engine discovers rare out‑of‑distribution anomalies, groups them via ontology discovery, and enables semantic search and visualization of clustered failure modes, allowing AI product teams to debug and curate datasets in minutes instead of months.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents, autonomous robots, and multimodal models often fail silently during deployment, producing hard-to‑interpret errors that require weeks of manual debugging and data labeling. Existing tools miss rare out‑of‑distribution events and cannot automatically translate raw trajectory data into actionable insights, leading to unreliable products and delayed releases.
Solution
Interpret AI offers a platform that ingests multimodal execution traces—text, video, DOM states, audio, and images—and automatically detects failures, annotates data, and performs root‑cause analysis at scale. Its Data Engine surfaces rare anomaly patterns, groups them via ontology discovery, and provides structured insights that developers can act on instantly. The system also enriches raw data with deep multimodal annotations, enabling rapid curation of training and evaluation datasets. By turning opaque agent behavior into searchable, clustered knowledge, the platform accelerates debugging cycles from months to minutes and supports continuous improvement of AI agents for real‑world deployment.
Target Audience
Primary customers are AI product teams building autonomous agents, robotics, autonomous vehicles, and multimodal AI systems that require reliable, scalable debugging and data annotation infrastructure.
Features
- Multimodal trajectory ingestion (text, video, audio, images, DOM) with automated failure flagging
- Automated root‑cause analysis that clusters failures and generates structured diagnostic reports
- Auto‑annotation engine for deep enrichment of multimodal data, including conversational audio and visual scene understanding
- Anomaly discovery that identifies out‑of‑distribution events and groups them using ontology‑based categorization
- Semantic search across annotated traces to quickly locate similar failure cases and relevant data
- Visualization tools for clustering and exploring failure modes, supporting dataset curation and targeted evaluation set creation