{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"quotient-ai","slug":"quotient-ai","name":"Quotient AI","type":"platform","url":"https://www.quotientai.co","page_url":"https://unfragile.ai/quotient-ai","categories":["testing-quality","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"quotient-ai__cap_0","uri":"capability://automation.workflow.structured.test.case.builder.with.natural.language.to.test.conversion","name":"structured test case builder with natural language to test conversion","description":"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.","intents":["I need to create a suite of test cases for my LLM application without writing code","I want to define expected behaviors and edge cases for my model before deployment","I need to version control my test cases alongside my model changes","I want to parameterize tests so I can run the same test logic with different inputs"],"best_for":["QA teams evaluating LLM outputs without ML expertise","product managers defining acceptance criteria for AI features","teams building CI/CD pipelines for LLM applications"],"limitations":["Natural language parsing may struggle with ambiguous or highly domain-specific test descriptions","No built-in support for probabilistic assertions or statistical significance testing","Test case complexity is limited by the structured schema — very complex conditional logic requires custom scoring rubrics"],"requires":["Web browser with modern JavaScript support","Account on Quotient AI platform","Access to at least one LLM provider (OpenAI, Anthropic, etc.) for test execution"],"input_types":["natural language test descriptions","structured JSON test specifications","CSV/JSON data files for parameterized tests"],"output_types":["normalized test case objects","test suite definitions","parameterized test matrices"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_1","uri":"capability://tool.use.integration.multi.model.evaluation.runner.with.provider.abstraction","name":"multi-model evaluation runner with provider abstraction","description":"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.","intents":["I want to compare how different models perform on the same test cases","I need to run my test suite against multiple model versions simultaneously","I want to test my prompts against both proprietary and open-source models","I need to handle API rate limits and failures gracefully during large test runs"],"best_for":["teams evaluating model selection decisions","researchers comparing LLM performance across providers","organizations with multi-model deployment strategies"],"limitations":["Provider abstraction adds ~50-150ms latency per request due to normalization overhead","Rate limiting is enforced per-provider but not globally across providers, requiring manual coordination for high-volume runs","Streaming responses are collected in memory before evaluation, limiting support for extremely long-form outputs (>100k tokens)"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Network connectivity to provider APIs","Sufficient API quota/credits for test execution"],"input_types":["test case specifications","model configuration objects (model ID, temperature, max_tokens, etc.)","provider credentials"],"output_types":["structured model responses","execution logs with timing metadata","error reports with retry information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_10","uri":"capability://safety.moderation.team.collaboration.and.permissions.management","name":"team collaboration and permissions management","description":"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.","intents":["I want to control who can modify test cases and run evaluations on my team","I need to audit who made changes to test suites and when","I want to share evaluation results with stakeholders without giving them edit access","I need to integrate with my company's identity provider for authentication"],"best_for":["enterprise teams with multiple stakeholders","organizations with strict access control requirements","teams requiring audit trails for compliance"],"limitations":["RBAC is limited to predefined roles — no support for custom role definitions","Audit logs are immutable but may grow large for high-activity teams","SSO integration requires enterprise plan"],"requires":["Quotient AI platform account","Team members with user accounts","Optional: SSO provider configuration (SAML, OIDC)"],"input_types":["user role assignments","permission specifications"],"output_types":["access control decisions","audit logs","user activity reports"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_11","uri":"capability://safety.moderation.collaborative.evaluation.workflow.with.approval.gates.and.audit.trails","name":"collaborative evaluation workflow with approval gates and audit trails","description":"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.","intents":["I want to require code review-style approval for test cases before they're used in evaluations","I need to track who created and modified test cases for compliance and accountability","I want to prevent unapproved evaluations from running to ensure quality standards"],"best_for":["Organizations with compliance or governance requirements","Teams with multiple contributors to test suites","Regulated industries (healthcare, finance) requiring audit trails"],"limitations":["Approval workflow configuration options unknown — may be limited to simple approve/reject without conditional logic","Audit trail retention and export capabilities unknown","No built-in role-based access control (RBAC) details — approval requirements may not be configurable by role","Approval gate enforcement scope unknown — may not apply to all evaluation types or configurations"],"requires":["Quotient AI platform account with multi-user access","Multiple team members with platform access","Approval workflow configuration (if not using defaults)"],"input_types":["test case changes and new test cases","evaluation configuration changes","approval requests"],"output_types":["approval status for test cases and configurations","audit logs with user attribution and timestamps","approval notifications"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_2","uri":"capability://safety.moderation.custom.scoring.rubric.engine.with.llm.based.evaluation","name":"custom scoring rubric engine with llm-based evaluation","description":"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.","intents":["I need to evaluate subjective qualities like tone, helpfulness, or creativity in model outputs","I want to define domain-specific scoring criteria that aren't covered by standard metrics","I need to ensure consistent evaluation across different test runs and team members","I want to combine multiple scoring dimensions into a composite quality score"],"best_for":["teams evaluating generative tasks (content creation, summarization, translation)","organizations with domain-specific quality standards","product teams iterating on prompt engineering"],"limitations":["LLM-based scoring introduces non-determinism — same output may receive different scores across runs due to evaluator model variance","Scoring latency is 2-5x higher than deterministic metrics because each evaluation requires an LLM call","Rubric quality is dependent on clarity of natural language descriptions — ambiguous rubrics lead to inconsistent scoring","No built-in inter-rater reliability metrics to validate rubric consistency across multiple evaluator instances"],"requires":["API key for evaluator LLM (OpenAI, Anthropic, etc.)","Test outputs to evaluate","Well-defined rubric criteria"],"input_types":["natural language rubric descriptions","structured JSON rubric specifications","model outputs to evaluate","reference/ground truth data (optional)"],"output_types":["numerical scores per rubric dimension","composite quality scores","evaluation explanations/justifications","score distributions across test runs"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_3","uri":"capability://data.processing.analysis.automated.test.generation.from.production.logs","name":"automated test generation from production logs","description":"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.","intents":["I want to create test cases based on actual user interactions rather than hypothetical scenarios","I need to identify edge cases and failure modes from production data","I want to ensure my test suite covers the most common user queries","I need to detect regressions in production behavior after model updates"],"best_for":["teams with mature production LLM applications generating substantial logs","organizations wanting to shift from synthetic to production-grounded testing","teams needing to validate model updates against real usage patterns"],"limitations":["Log analysis requires structured logging with input/output pairs — unstructured logs require preprocessing","Clustering and deduplication may miss rare but critical edge cases if filtering thresholds are too aggressive","Generated tests inherit biases from production data — if production logs are skewed toward certain user segments, test coverage will be similarly skewed","Privacy concerns: production logs may contain sensitive user data that must be redacted before test case generation"],"requires":["Access to production logs with input/output pairs","Logs in structured format (JSON, CSV) or custom parser configuration","Sufficient log volume (minimum ~100 interactions) for meaningful clustering"],"input_types":["production logs (JSON, CSV, or custom format)","log schema configuration","filtering and deduplication parameters"],"output_types":["generated test case specifications","test case metadata (frequency, user segment, timestamp)","clustering analysis and coverage reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_4","uri":"capability://data.processing.analysis.regression.detection.and.quality.trend.tracking","name":"regression detection and quality trend tracking","description":"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.","intents":["I want to know if a model update degraded performance on my test suite","I need to track quality metrics over time to identify trends","I want to prevent deploying models that fail regression tests","I need to understand which test cases are most sensitive to model changes"],"best_for":["teams with continuous model deployment pipelines","organizations requiring quality gates before production release","teams iterating rapidly on prompts and model configurations"],"limitations":["Statistical significance testing requires sufficient test volume (minimum ~30 test cases) to be reliable","Regression detection is sensitive to baseline selection — choosing the wrong baseline can produce false positives/negatives","No built-in support for multi-dimensional regression analysis — comparing across multiple metrics simultaneously requires manual interpretation","Trend analysis assumes test stability — if test cases change between runs, historical comparisons become invalid"],"requires":["Historical test run data (baseline)","Current test run results","Configured regression thresholds"],"input_types":["test results from multiple runs","baseline version specification","regression threshold configuration"],"output_types":["regression reports with effect sizes","quality trend visualizations","pass/fail signals for CI/CD integration","per-test-case sensitivity analysis"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_5","uri":"capability://data.processing.analysis.test.result.visualization.and.comparison.dashboard","name":"test result visualization and comparison dashboard","description":"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.","intents":["I want to see at a glance how my models are performing across all test cases","I need to compare two model versions side-by-side to understand differences","I want to drill down into failing tests to understand why they failed","I need to generate reports for stakeholders showing test coverage and quality metrics"],"best_for":["product managers and non-technical stakeholders reviewing model performance","teams conducting model selection evaluations","organizations requiring audit trails and compliance reporting"],"limitations":["Dashboard performance degrades with very large test suites (>10,000 tests) due to client-side rendering","Filtering and grouping are limited to predefined dimensions — custom analysis requires exporting raw data","PDF export quality is limited for complex visualizations with many data points"],"requires":["Web browser with modern JavaScript support","Test results stored in Quotient AI platform"],"input_types":["test results from multiple runs","model metadata","test case metadata"],"output_types":["interactive HTML dashboards","static visualizations (PNG, SVG)","structured data exports (JSON, CSV)","PDF reports"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_6","uri":"capability://automation.workflow.test.case.versioning.and.change.tracking","name":"test case versioning and change tracking","description":"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.","intents":["I want to understand how test case changes affected my evaluation results","I need to maintain multiple versions of test suites for different model variants","I want to collaborate with teammates on test case definitions without conflicts","I need to audit which tests were used for each model evaluation"],"best_for":["teams collaborating on test suite development","organizations with strict audit and compliance requirements","teams running A/B tests on evaluation methodologies"],"limitations":["Merge conflict resolution is manual for complex test suite changes","No built-in diff visualization for test case changes — requires manual comparison","Version history storage grows linearly with test suite size and change frequency"],"requires":["Test cases defined in Quotient AI platform","User accounts for team members"],"input_types":["test case modifications","branch/merge operations"],"output_types":["version history with timestamps and authors","change diffs","audit logs linking tests to evaluation runs"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_7","uri":"capability://automation.workflow.batch.evaluation.scheduling.and.execution","name":"batch evaluation scheduling and execution","description":"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).","intents":["I want to run my full test suite against multiple models overnight without manual intervention","I need to automatically re-evaluate my models whenever I add new test cases","I want to track progress of long-running evaluations and get notified when complete","I need to schedule regular quality checks on my production models"],"best_for":["teams with large test suites (>1000 tests) requiring hours to evaluate","organizations running continuous model evaluation pipelines","teams needing scheduled quality assurance checks"],"limitations":["Scheduling is limited to fixed intervals — no support for complex cron expressions or conditional triggers beyond model updates","Progress tracking is approximate for very large batches due to aggregation overhead","Job cancellation may leave partial results in inconsistent state"],"requires":["Quotient AI platform account","API keys for LLM providers","Network connectivity for job execution"],"input_types":["test suite specifications","model configurations","schedule definitions","trigger conditions"],"output_types":["job execution logs","progress updates","completion notifications","aggregated results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_8","uri":"capability://tool.use.integration.evaluation.result.export.and.integration.with.external.tools","name":"evaluation result export and integration with external tools","description":"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.","intents":["I want to export my test results to analyze them in my data warehouse","I need to integrate Quotient AI results into my existing monitoring dashboards","I want to trigger downstream actions (alerts, deployments) when evaluations complete","I need to archive test results for compliance and audit purposes"],"best_for":["organizations with existing data infrastructure (data warehouses, BI tools)","teams integrating LLM evaluation into broader ML pipelines","organizations with strict data governance requirements"],"limitations":["API rate limiting may require pagination for very large result sets (>100k records)","Webhook delivery is not guaranteed — requires client-side retry logic for reliability","Export formats have different precision/fidelity — JSON preserves full metadata, CSV may truncate long text fields"],"requires":["API key for Quotient AI platform","Network connectivity to external systems","Credentials for destination systems (data warehouse, BI tool, etc.)"],"input_types":["evaluation results","export format specification","destination configuration"],"output_types":["JSON/CSV/Parquet files","REST API responses","webhook payloads"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__cap_9","uri":"capability://code.generation.editing.prompt.engineering.and.configuration.management","name":"prompt engineering and configuration management","description":"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.","intents":["I want to test multiple prompt variations against my test suite to find the best one","I need to manage different system prompts for different use cases","I want to understand how temperature and other hyperparameters affect model outputs","I need to version my prompts alongside my test cases for reproducibility"],"best_for":["teams iterating on prompt engineering","organizations running A/B tests on prompt variations","teams needing reproducible evaluation across prompt versions"],"limitations":["Templating is limited to simple variable substitution — no support for conditional logic or complex transformations","A/B testing comparison is limited to two configurations at a time","No built-in prompt optimization algorithms — requires manual iteration"],"requires":["Quotient AI platform account","Test suite defined in platform"],"input_types":["prompt text with optional template variables","model configuration parameters","test suite specification"],"output_types":["prompt variations with metadata","A/B test comparison results","configuration impact analysis"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"quotient-ai__headline","uri":"capability://testing.quality.ai.model.testing.and.evaluation.platform","name":"ai model testing and evaluation platform","description":"Quotient AI is a comprehensive platform designed for testing and evaluating AI models, allowing teams to create structured test cases, run evaluations, and track quality regressions effectively.","intents":["best AI model testing platform","AI evaluation tools for quality assurance","how to test AI models","automated testing for AI systems","evaluation framework for machine learning models"],"best_for":["AI development teams","quality assurance professionals"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","Account on Quotient AI platform","Access to at least one LLM provider (OpenAI, Anthropic, etc.) for test execution","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Network connectivity to provider APIs","Sufficient API quota/credits for test execution","Quotient AI platform account","Team members with user accounts","Optional: SSO provider configuration (SAML, OIDC)","Quotient AI platform account with multi-user access"],"failure_modes":["Natural language parsing may struggle with ambiguous or highly domain-specific test descriptions","No built-in support for probabilistic assertions or statistical significance testing","Test case complexity is limited by the structured schema — very complex conditional logic requires custom scoring rubrics","Provider abstraction adds ~50-150ms latency per request due to normalization overhead","Rate limiting is enforced per-provider but not globally across providers, requiring manual coordination for high-volume runs","Streaming responses are collected in memory before evaluation, limiting support for extremely long-form outputs (>100k tokens)","RBAC is limited to predefined roles — no support for custom role definitions","Audit logs are immutable but may grow large for high-activity teams","SSO integration requires enterprise plan","Approval workflow configuration options unknown — may be limited to simple approve/reject without conditional logic","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=quotient-ai","compare_url":"https://unfragile.ai/compare?artifact=quotient-ai"}},"signature":"fbM6C+a1ZuHQWD0gGLG+6rzq13fx2MAAmXPs+Ag3TS6l/jcZSB2f9XcNvibt6nIdH5N3xHU8bX2jEutpeGeMBQ==","signedAt":"2026-06-20T03:04:21.818Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/quotient-ai","artifact":"https://unfragile.ai/quotient-ai","verify":"https://unfragile.ai/api/v1/verify?slug=quotient-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}