Adaptive behavioral analytics for production AI
Discover, investigate, and track insights to improve AI products
Platform
Enterprise platform that integrates with your production AI stack
- Open distribution: Deploy our free platform in a scalable Kubernetes cluster in your VPC
- Enterprise controls: Keep data secure with enterprise authentication and authorization using OpenID Connect SSO and role-based access controls
- Flexible integrations: Ingest logs via OTEL traces, SQL, or SDK, bring your own evals, use your current LLM providers, and leverage existing notifications channels
Pipeline
Automated pipeline for scalable data analysis on production AI logs
- Enrich production AI logs with metrics, statistics, attributes, evals, and LLM as judge metrics to capture the status of AI product behavior
- Analyze enriched production logs to uncover behavioral signals with unsupervised clustering, topic modeling, anomaly detection, and data drift assessment
- Publish signals from analysis, report on them via dashboard or notification channels, and select which signals to track in a shared dashboard
Workflow
Intuitive workflow to discover and act on behavioral signals
- Discover behavioral signals that correspond to daily shifts, clusters, or outliers in production AI logs
- Investigate these signals with contextual evidence from relevant production data to quickly triage and take action
- Track signals to codify preferences and optimize future analysis on behaviors you care about most
Be confident in AI product behavior
Continuously analyze your product to understand behavior, and evolve your product based on this understanding
Discover
Discover signals on AI product behavior – the interplay and correlations between users, context, tools, models, and metrics – that provide guidance on how to improve, fix, optimize, or evolve your product over time.
Investigate
Investigate and triage these behavioral signals with evidence in the form of specific logs that are driving the signal and visualizations of temporal or comparative analysis of these signals to prioritize and take action on these insights.
Track
Track these signals as new segments, metrics, or filters to guide future analysis of production AI logs and customize the dashboard the team views to make daily decisions on the AI product.
Improve
Improve your AI product by leveraging insights from these signals to fix issues, identify new segments of users to target, or boost model performance with reinforcement learning, fine tuning, or context engineering.
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