{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_qualifire","slug":"qualifire","name":"Qualifire","type":"product","url":"https://qualifire.ai","page_url":"https://unfragile.ai/qualifire","categories":["testing-quality"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_qualifire__cap_0","uri":"capability://safety.moderation.real.time.chatbot.output.quality.monitoring","name":"real-time chatbot output quality monitoring","description":"Continuously analyzes chatbot responses in production using configurable quality metrics (hallucination detection, tone consistency, brand alignment, factual accuracy) with sub-second latency evaluation. Implements streaming evaluation pipelines that intercept responses before user delivery, enabling immediate detection of quality degradation without batch processing delays or post-hoc analysis.","intents":["I need to catch when my chatbot starts giving off-brand or hallucinated responses before users see them","I want real-time alerts when chatbot quality drops below acceptable thresholds across my fleet of instances","I need to measure and track quality metrics across multiple chatbot deployments simultaneously"],"best_for":["Medium to large enterprises running 3+ production chatbot instances","Teams managing customer-facing AI assistants where brand reputation is critical","Organizations with SLAs requiring <5 minute detection of quality issues"],"limitations":["Monitoring latency adds 50-200ms per response evaluation depending on metric complexity","Quality metrics are chatbot-specific; cannot monitor image generation, code generation, or other AI modalities","Requires integration at response interception point; incompatible with fully black-box third-party chatbot APIs","No offline evaluation mode; all monitoring requires active cloud connectivity to Qualifire service"],"requires":["Production chatbot deployment with accessible response pipeline","API credentials for Qualifire service","Baseline quality metrics defined and calibrated for your specific use case","Network connectivity with <200ms latency to Qualifire infrastructure"],"input_types":["chatbot responses (text)","user queries (text)","conversation context (structured JSON)","quality metric definitions (JSON schema)"],"output_types":["quality scores (numeric 0-100)","violation alerts (structured JSON)","quality trend reports (time-series data)","metric breakdowns (categorical analysis)"],"categories":["safety-moderation","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qualifire__cap_1","uri":"capability://automation.workflow.prompt.deployment.and.a.b.testing.orchestration","name":"prompt deployment and a/b testing orchestration","description":"Automates the deployment of prompt variations across chatbot instances with built-in traffic splitting, version control, and rollback capabilities. Manages prompt versioning as immutable artifacts with metadata tracking, enables canary deployments (e.g., 10% traffic to new prompt, 90% to baseline), and provides automated rollback triggers based on quality metric thresholds without manual intervention.","intents":["I want to test a new prompt on 20% of my chatbot traffic without manually redeploying","I need to roll back a prompt change automatically if quality metrics drop below a threshold","I want to track which prompt version is running on each chatbot instance and when it was deployed"],"best_for":["Teams iterating rapidly on prompt engineering with multiple production instances","Organizations running continuous A/B tests on chatbot behavior","Enterprises needing audit trails and version control for prompt changes"],"limitations":["Deployment granularity is per-chatbot-instance; cannot split traffic at the conversation level within a single instance","Rollback decisions are based on pre-configured metric thresholds only; no manual override during automatic rollback","No built-in prompt optimization suggestions; requires external prompt engineering or LLM-based optimization tools","Canary deployment windows are fixed-duration; no adaptive traffic shifting based on statistical significance"],"requires":["Qualifire monitoring integration already active on target chatbot instances","Quality metrics baseline established for rollback threshold configuration","API access to chatbot deployment infrastructure or Qualifire's chatbot connector","Prompt versioning schema defined (e.g., semantic versioning or timestamp-based)"],"input_types":["prompt text (string)","traffic split percentages (numeric 0-100)","rollback trigger thresholds (metric name + numeric threshold)","deployment schedule (ISO 8601 timestamps)"],"output_types":["deployment status (enum: pending, active, rolled_back)","traffic allocation per version (numeric percentages)","deployment audit log (structured JSON with timestamps)","rollback event records (with trigger reason and metrics snapshot)"],"categories":["automation-workflow","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qualifire__cap_2","uri":"capability://data.processing.analysis.multi.instance.chatbot.fleet.quality.aggregation","name":"multi-instance chatbot fleet quality aggregation","description":"Aggregates quality metrics across multiple chatbot instances into unified dashboards and reports, enabling cross-instance trend analysis, comparative performance ranking, and fleet-wide anomaly detection. Implements hierarchical metric aggregation (per-instance → per-model → fleet-wide) with configurable rollup functions (mean, percentile, max) and time-series correlation analysis to identify systemic issues affecting multiple instances simultaneously.","intents":["I need a single dashboard showing quality metrics across all 15 of my production chatbots","I want to identify which chatbot instances are underperforming compared to the fleet average","I need to detect when a quality issue affects multiple instances simultaneously (e.g., shared model degradation)"],"best_for":["Enterprises managing 5+ chatbot instances across different teams or products","Organizations with centralized AI quality assurance teams","Teams needing fleet-wide SLA reporting and compliance tracking"],"limitations":["Aggregation is time-bucketed (typically 1-minute or 5-minute intervals); sub-minute anomalies may be smoothed out","Correlation analysis assumes metric independence; cannot detect complex multi-metric failure patterns","No automatic root cause analysis; anomaly detection only flags deviations, requires manual investigation","Cross-instance comparison assumes homogeneous chatbot configurations; heterogeneous setups require manual metric normalization"],"requires":["Minimum 2 chatbot instances connected to Qualifire monitoring","Consistent quality metric definitions across all instances","Qualifire dashboard or API access for viewing aggregated metrics","Time synchronization across all chatbot instances (NTP or equivalent)"],"input_types":["per-instance quality metrics (numeric time-series)","instance metadata (JSON: model, version, region, team)","aggregation configuration (JSON: rollup functions, time buckets)","anomaly detection thresholds (numeric: standard deviations or percentile bounds)"],"output_types":["fleet-wide quality dashboards (HTML/JSON visualization)","comparative performance rankings (instance → metric → percentile)","anomaly alerts (instance + metric + deviation magnitude)","trend reports (time-series CSV/JSON with fleet averages)"],"categories":["data-processing-analysis","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qualifire__cap_3","uri":"capability://safety.moderation.quality.metric.configuration.and.customization","name":"quality metric configuration and customization","description":"Provides a framework for defining custom quality metrics tailored to specific chatbot use cases (e.g., customer support vs. sales assistant) using composable metric definitions. Supports metric templates (hallucination, tone consistency, factual accuracy, brand alignment) with configurable thresholds, weighting schemes, and custom evaluation logic via LLM-based or rule-based evaluators. Enables teams to define domain-specific metrics without code changes.","intents":["I need to define custom quality metrics specific to my customer support chatbot (e.g., 'response helpfulness', 'escalation appropriateness')","I want to weight different quality metrics differently (e.g., hallucination is 50% of score, tone is 30%)","I need to adjust quality thresholds per chatbot instance based on different SLAs"],"best_for":["Teams with domain-specific chatbot use cases requiring custom quality definitions","Organizations with mature AI quality practices and defined evaluation criteria","Enterprises needing per-instance or per-team metric customization"],"limitations":["Custom metric evaluation adds latency proportional to evaluator complexity; LLM-based evaluators add 100-500ms per response","No built-in metric validation; misconfigured metrics may produce misleading quality scores","Metric composition is additive only; no support for conditional or branching metric logic","Custom evaluators require external LLM API calls; no offline evaluation capability"],"requires":["Access to Qualifire metric configuration interface (UI or API)","Clear definition of quality criteria for your chatbot use case","For LLM-based evaluators: API credentials for evaluation LLM (OpenAI, Anthropic, etc.)","Understanding of metric weighting and threshold semantics"],"input_types":["metric definitions (JSON schema with name, type, evaluator, threshold)","metric weights (numeric 0-1)","evaluator configuration (LLM prompt, rule-based logic, or template name)","threshold values (numeric or percentile-based)"],"output_types":["metric configuration (JSON artifact)","metric validation report (list of configuration errors)","metric evaluation results (per-response scores)","metric performance statistics (distribution, percentiles)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qualifire__cap_4","uri":"capability://automation.workflow.quality.alert.and.notification.routing","name":"quality alert and notification routing","description":"Routes quality violation alerts to appropriate teams via configurable notification channels (Slack, email, PagerDuty, webhooks) with alert severity levels, deduplication, and escalation policies. Implements alert grouping (e.g., 'suppress duplicate hallucination alerts from same instance within 5 minutes') and escalation rules (e.g., 'if quality stays below threshold for 10 minutes, escalate to on-call engineer'). Enables teams to define alert routing rules based on metric type, instance, or severity.","intents":["I want Slack notifications when my chatbot quality drops below critical thresholds","I need to escalate to PagerDuty if a quality issue persists for more than 10 minutes","I want to suppress duplicate alerts for the same issue within a 5-minute window"],"best_for":["Teams with on-call rotations or dedicated AI quality engineers","Organizations needing rapid response to production quality issues","Enterprises with multiple teams managing different chatbot instances"],"limitations":["Alert deduplication is time-window based; complex deduplication logic (e.g., 'same root cause') requires manual configuration","Escalation policies are fixed-rule based; no adaptive escalation based on response time or resolution history","Notification delivery is best-effort; no guaranteed delivery or retry logic for failed notifications","Alert routing rules are static; no dynamic routing based on team availability or on-call schedules"],"requires":["Qualifire monitoring integration active on target instances","API credentials for notification channels (Slack webhook, PagerDuty API key, etc.)","Alert routing rules defined (metric type → notification channel → severity level)","Escalation policy configuration (time thresholds, escalation targets)"],"input_types":["quality violation events (metric name, value, threshold, instance)","alert routing rules (JSON: condition → notification channel)","escalation policies (JSON: time threshold → escalation target)","notification channel credentials (API keys, webhooks)"],"output_types":["alert notifications (Slack messages, emails, PagerDuty incidents)","alert history log (structured JSON with timestamps and delivery status)","escalation event records (with escalation reason and target)","alert statistics (count, severity distribution, response time)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qualifire__cap_5","uri":"capability://data.processing.analysis.prompt.performance.analytics.and.comparison","name":"prompt performance analytics and comparison","description":"Analyzes performance metrics for different prompt versions deployed across chatbot instances, enabling comparative analysis of prompt effectiveness. Tracks metrics like response quality, user satisfaction (if available), latency, and cost per version, with statistical significance testing to determine if performance differences are meaningful. Provides visualizations comparing prompt versions side-by-side with confidence intervals and effect sizes.","intents":["I want to see if my new prompt version actually performs better than the baseline across quality metrics","I need to compare response latency and cost between two prompt versions to decide which to keep","I want statistical confidence that a prompt change improved quality, not just random variation"],"best_for":["Teams running continuous A/B tests on prompt variations","Organizations with data-driven prompt engineering practices","Enterprises needing quantitative justification for prompt changes"],"limitations":["Statistical significance testing requires minimum sample sizes (typically 100+ responses per version); early-stage tests may be inconclusive","Comparison assumes stable baseline metrics; external factors (e.g., user behavior changes) can confound results","No built-in cost tracking; cost comparison requires integration with LLM billing APIs","Latency analysis is aggregate only; no per-response latency breakdown or percentile analysis"],"requires":["Minimum 2 prompt versions deployed simultaneously with traffic splitting","Quality metrics collected for both versions over sufficient time period (typically 24+ hours)","Statistical significance threshold configured (e.g., p-value < 0.05)","Optional: user satisfaction data or conversion metrics for business impact analysis"],"input_types":["prompt version identifiers (string)","quality metrics per version (numeric time-series)","traffic allocation per version (numeric percentages)","optional: user satisfaction scores, conversion metrics (numeric)"],"output_types":["comparative performance report (HTML/JSON with tables and charts)","statistical significance test results (p-value, confidence interval, effect size)","prompt version rankings (by metric)","recommendation (which version to promote based on metrics)"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qualifire__cap_6","uri":"capability://data.processing.analysis.quality.metric.baseline.and.drift.detection","name":"quality metric baseline and drift detection","description":"Establishes baseline quality metrics for each chatbot instance and detects when actual metrics drift significantly from baseline, indicating potential degradation. Uses statistical methods (z-score, moving average, exponential smoothing) to identify gradual drift or sudden shifts in quality. Enables teams to define acceptable drift thresholds and receive alerts when metrics deviate beyond acceptable bounds.","intents":["I want to know when my chatbot quality gradually degrades over time, not just when it drops below a threshold","I need to detect sudden quality shifts (e.g., from a model update) separately from gradual drift","I want to establish a baseline for a new chatbot and track deviations from that baseline"],"best_for":["Teams monitoring long-running chatbot instances for gradual quality degradation","Organizations needing early warning of quality issues before they become critical","Enterprises with mature monitoring practices and defined baseline metrics"],"limitations":["Baseline establishment requires historical data (typically 7-30 days); new instances lack baselines","Drift detection assumes metric stationarity; non-stationary metrics (e.g., seasonal patterns) produce false positives","Statistical methods are univariate; cannot detect correlated drift across multiple metrics","Drift thresholds are static; no adaptive thresholds based on metric volatility or time-of-day patterns"],"requires":["Historical quality metric data (minimum 7 days, preferably 30+ days)","Baseline metric values computed from historical data","Drift detection method selected (z-score, moving average, exponential smoothing)","Acceptable drift threshold configured (numeric or percentile-based)"],"input_types":["historical quality metrics (numeric time-series)","baseline metric values (numeric)","drift detection method (enum: z-score, moving-average, exponential-smoothing)","acceptable drift threshold (numeric or percentile)"],"output_types":["baseline metrics report (per-instance baseline values)","drift detection alerts (metric name, current value, baseline, deviation magnitude)","drift trend visualization (time-series with baseline and confidence bands)","drift statistics (rate of change, volatility, anomaly score)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Production chatbot deployment with accessible response pipeline","API credentials for Qualifire service","Baseline quality metrics defined and calibrated for your specific use case","Network connectivity with <200ms latency to Qualifire infrastructure","Qualifire monitoring integration already active on target chatbot instances","Quality metrics baseline established for rollback threshold configuration","API access to chatbot deployment infrastructure or Qualifire's chatbot connector","Prompt versioning schema defined (e.g., semantic versioning or timestamp-based)","Minimum 2 chatbot instances connected to Qualifire monitoring","Consistent quality metric definitions across all instances"],"failure_modes":["Monitoring latency adds 50-200ms per response evaluation depending on metric complexity","Quality metrics are chatbot-specific; cannot monitor image generation, code generation, or other AI modalities","Requires integration at response interception point; incompatible with fully black-box third-party chatbot APIs","No offline evaluation mode; all monitoring requires active cloud connectivity to Qualifire service","Deployment granularity is per-chatbot-instance; cannot split traffic at the conversation level within a single instance","Rollback decisions are based on pre-configured metric thresholds only; no manual override during automatic rollback","No built-in prompt optimization suggestions; requires external prompt engineering or LLM-based optimization tools","Canary deployment windows are fixed-duration; no adaptive traffic shifting based on statistical significance","Aggregation is time-bucketed (typically 1-minute or 5-minute intervals); sub-minute anomalies may be smoothed out","Correlation analysis assumes metric independence; cannot detect complex multi-metric failure patterns","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.438Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=qualifire","compare_url":"https://unfragile.ai/compare?artifact=qualifire"}},"signature":"QiIGGMtsvhjOfsFWJRdP1r8uI0QD6FhJrbW8CkZg3WeFlVub44ZsHJgoQ5gA2+J2OFEF4NvK8AjQDqPV7ZB2Cg==","signedAt":"2026-06-21T02:23:26.963Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qualifire","artifact":"https://unfragile.ai/qualifire","verify":"https://unfragile.ai/api/v1/verify?slug=qualifire","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"}}