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Overview#
The Analytics domain provides advanced multi-AI analysis capabilities, client-side analytics execution, usage tracking, and performance monitoring for investigation workflows. It serves as the intelligence layer of the platform, enabling pattern detection, network analysis, and AI-powered insights across multiple providers with built-in cost controls and compliance tracking.
Key Features#
- Multi-AI Analysis -- Executes analysis requests across multiple AI providers in parallel, providing diverse perspectives and consensus-based insights for investigation data
- Client-Side Analytics Execution -- Runs custom Python analytics scripts safely in the browser sandbox, enabling advanced data analysis without server-side code execution risks
- Pre-Built Analytics Templates -- A library of ready-to-use analysis templates for common investigation patterns, with support for custom templates and tagging
- Usage Tracking and Cost Monitoring -- Real-time tracking of AI token usage and costs by organization, with configurable daily budget limits and alerts
- Provider Performance Comparison -- Dashboard metrics comparing AI provider response quality, latency, and cost to optimize provider selection
- Investigation Cost Analytics -- Tracks analytics spending per investigation for budget management and resource allocation decisions
- Rate Limiting and Budget Controls -- Multiple mechanisms prevent runaway costs including daily budget limits, concurrent analysis caps, and pre-analysis cost estimation
- Audit Logging -- All analytics operations are logged for compliance tracking with full audit trail capabilities
- Resilient Multi-Provider Architecture -- Individual provider failures do not block the overall analysis; results from available providers are still returned
- Customizable Analysis Templates -- Organizations can create, tag, and manage their own analysis templates alongside the platform's built-in library
Use Cases#
- Investigators submit case data for multi-AI analysis, receiving insights from multiple providers that are compared and synthesized to identify patterns that a single provider might miss.
- Data analysts use the client-side Python execution environment to run custom analytics scripts on investigation data directly in the browser, with full sandboxing for security.
- Organizations monitor their AI usage costs through real-time dashboards, set daily budget limits, and receive alerts when spending approaches configured thresholds.
- Teams leverage pre-built analytics templates for common investigation patterns like network analysis, entity relationship mapping, and timeline reconstruction.
- Administrators compare AI provider performance metrics to make informed decisions about which providers to use for different analysis types.
Integration#
The Analytics domain works with the Investigation domain for case context, the Evidence domain for data input, and the Visualization domain to present AI-powered insights throughout the investigation lifecycle.
Last Reviewed: 2026-02-05