Quotient AI

Enables teams to define LLM test cases through a structured interface that captures input prompts, expected outputs, and evaluation criteria. The platform converts natural language test descriptions into machine-readable test specifications, storing them in a normalized schema that supports versioning and parameterization. Tests are organized hierarchically by test suite and can reference shared fixtures and data templates.

Executes test cases against multiple LLM providers (OpenAI, Anthropic, Ollama, etc.) through a unified abstraction layer that normalizes API differences and handles authentication, rate limiting, and retry logic. The platform batches requests, streams responses, and collects structured outputs for downstream evaluation. Supports both synchronous and asynchronous execution with configurable concurrency limits.

Provides role-based access control (RBAC) for test suites, evaluations, and results with granular permissions (view, edit, execute, delete). Supports team workspaces with shared resources and audit logs tracking all user actions. Integrates with SSO providers for enterprise authentication.

Supports multi-user evaluation workflows where test cases and evaluation configurations can be reviewed and approved before execution. Changes to test cases, rubrics, and evaluation settings are tracked with user attribution and timestamps. Approval gates can require sign-off from designated reviewers before test cases are marked as 'approved' or evaluations are executed. Audit trails provide complete visibility into who made what changes and when.

Allows teams to define custom evaluation criteria as rubrics that are executed by LLMs to score test outputs on arbitrary dimensions (correctness, tone, completeness, etc.). Rubrics are expressed in natural language or structured JSON and are applied to model responses using a separate evaluator LLM. The platform supports both deterministic scoring (exact match, regex) and LLM-based scoring with configurable evaluator models and temperature settings.

Analyzes production logs and user interactions to automatically generate test cases that reflect real-world usage patterns. The platform extracts input-output pairs from logs, clusters similar interactions, and creates representative test cases with configurable filtering and deduplication. Generated tests are tagged with metadata (frequency, user segment, timestamp) to prioritize high-impact scenarios.

Tracks test results across time and model versions, detecting regressions (performance drops) and quality trends through statistical analysis. The platform compares current test run results against baseline versions, computes effect sizes, and flags significant changes. Supports configurable regression thresholds and can integrate with CI/CD pipelines to block deployments when regressions are detected.

Provides interactive dashboards for visualizing test results, comparing performance across models and versions, and drilling down into individual test failures. The platform renders score distributions, pass/fail rates, and trend charts with filtering and grouping capabilities. Supports exporting results in multiple formats (JSON, CSV, PDF) for reporting and analysis.

Maintains version history for test cases and test suites, tracking changes to test definitions, expected outputs, and evaluation criteria. The platform supports branching test suites for A/B testing different evaluation approaches and merging changes with conflict resolution. Test case versions are linked to model evaluation runs, enabling traceability between test changes and result changes.

Enables scheduling of large-scale test runs across multiple models and configurations with resource management and progress tracking. The platform queues evaluation jobs, distributes them across worker processes, and provides real-time progress updates. Supports recurring evaluations on schedules (daily, weekly) and conditional triggers (on model updates, on new test cases).

Exports test results in multiple formats (JSON, CSV, Parquet) and provides API endpoints for programmatic access to evaluation data. The platform supports webhooks for notifying external systems of evaluation completion and integrates with common data warehouses and BI tools. Results can be pushed to external systems or pulled via REST API with pagination and filtering.

Allows teams to define and version multiple prompt variations and model configurations (temperature, max_tokens, system prompts, etc.) within the platform. Supports templating with variable substitution and enables A/B testing different prompts against the same test suite. Configurations are stored with metadata and can be compared side-by-side to understand impact of changes.