{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_log10","slug":"log10","name":"Log10","type":"product","url":"https://log10.io","page_url":"https://unfragile.ai/log10","categories":["text-writing"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_log10__cap_0","uri":"capability://customer.support.real.time.llm.output.feedback.collection","name":"real-time llm output feedback collection","description":"Captures user feedback on LLM responses in production environments as they occur, creating a continuous stream of quality signals. Enables teams to identify hallucinations, incorrect answers, and user dissatisfaction immediately rather than through delayed batch analysis.","intents":["I want to know immediately when my chatbot gives wrong answers","I need to collect user thumbs-up/thumbs-down ratings on AI responses in real-time","I want to track which types of questions my LLM struggles with"],"best_for":["Enterprise customer support teams","Mid-market companies with production chatbots","Teams running high-volume LLM applications"],"limitations":["Requires integration into existing LLM pipeline","Depends on users providing explicit feedback","Not effective for silent failures users don't report"],"requires":["Production LLM deployment","API integration capability","User-facing feedback mechanism"],"input_types":["LLM responses","user feedback signals","conversation context"],"output_types":["feedback logs","quality metrics","signal streams"],"categories":["customer-support","chatbot","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_log10__cap_1","uri":"capability://customer.support.llm.accuracy.measurement.and.scoring","name":"llm accuracy measurement and scoring","description":"Automatically calculates and tracks accuracy metrics specific to customer support and chatbot use cases. Provides quantifiable measurements of model performance against business-relevant quality benchmarks without requiring manual evaluation.","intents":["I need to measure how accurate my chatbot is compared to last week","I want to know which customer support scenarios my LLM handles poorly","I need metrics to justify LLM investments to stakeholders"],"best_for":["Enterprise teams with production chatbots","Customer support operations","Teams needing measurable accuracy improvements"],"limitations":["Metrics are specific to support/chatbot domain","Requires sufficient feedback data to be statistically meaningful","May not capture all relevant quality dimensions"],"requires":["Real-time feedback data","Production LLM deployment","Baseline accuracy targets"],"input_types":["LLM responses","user feedback","conversation logs"],"output_types":["accuracy scores","performance dashboards","quality metrics","trend reports"],"categories":["customer-support","chatbot","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_log10__cap_2","uri":"capability://customer.support.automated.llm.optimization.without.retraining","name":"automated llm optimization without retraining","description":"Improves LLM accuracy and reduces hallucinations through optimization techniques that don't require expensive full model retraining. Uses feedback signals to adjust behavior and improve outputs at inference time or through lightweight fine-tuning.","intents":["I want to improve my chatbot accuracy without spending weeks retraining","I need to reduce hallucinations in my customer support AI quickly","I want continuous improvement without the cost of full model retraining"],"best_for":["Enterprise teams with production chatbots","Companies without ML infrastructure for retraining","Teams needing rapid accuracy improvements"],"limitations":["Optimization improvements are incremental, not transformative","Requires sufficient feedback data to be effective","May not solve fundamental model capability gaps","Significant integration effort required"],"requires":["Production LLM deployment","Real-time feedback signals","API integration capability","Sufficient historical feedback data"],"input_types":["LLM responses","user feedback","conversation context","quality signals"],"output_types":["optimized model behavior","improved responses","reduced hallucinations"],"categories":["customer-support","chatbot","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_log10__cap_3","uri":"capability://customer.support.production.llm.monitoring.and.alerting","name":"production llm monitoring and alerting","description":"Continuously monitors deployed LLM systems for quality degradation, accuracy drops, and emerging failure patterns. Provides alerts when performance falls below thresholds or anomalies are detected.","intents":["I want to be alerted when my chatbot accuracy drops suddenly","I need to detect when my LLM starts hallucinating more frequently","I want early warning before customer satisfaction is impacted"],"best_for":["Enterprise operations teams","Customer support managers","Teams running mission-critical chatbots"],"limitations":["Requires baseline performance data to detect anomalies","Alert thresholds need manual tuning","Reactive rather than preventive"],"requires":["Production LLM deployment","Real-time feedback signals","Historical performance baseline"],"input_types":["LLM responses","user feedback","quality metrics"],"output_types":["alerts","performance dashboards","anomaly reports"],"categories":["customer-support","chatbot","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_log10__cap_4","uri":"capability://customer.support.conversation.logging.and.replay","name":"conversation logging and replay","description":"Records and stores complete conversation histories with LLM outputs, user feedback, and context. Enables teams to replay, analyze, and learn from specific interactions to identify improvement opportunities.","intents":["I want to review conversations where my chatbot failed","I need to analyze patterns in customer support interactions","I want to use past conversations to improve my LLM"],"best_for":["Customer support teams","Chatbot product managers","Teams analyzing LLM failure modes"],"limitations":["Storage and retrieval at scale requires infrastructure","Privacy considerations with customer data","Manual analysis is time-consuming"],"requires":["Production LLM deployment","Data storage infrastructure","Privacy compliance measures"],"input_types":["conversation transcripts","LLM responses","user feedback","metadata"],"output_types":["conversation logs","replay data","analysis reports"],"categories":["customer-support","chatbot","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_log10__cap_5","uri":"capability://customer.support.scalable.high.volume.llm.inference","name":"scalable high-volume llm inference","description":"Handles production deployments of LLMs at scale without performance degradation. Manages infrastructure, load balancing, and optimization to support high-volume customer interactions.","intents":["I need my chatbot to handle thousands of concurrent conversations","I want consistent response times even during traffic spikes","I need infrastructure that scales with my customer base"],"best_for":["Enterprise companies with high-volume chatbots","Customer support operations at scale","Teams without dedicated ML infrastructure"],"limitations":["Paid pricing model may be expensive at very high volumes","Requires integration into existing systems","Performance depends on model size and complexity"],"requires":["Production LLM deployment","API integration capability","Sufficient budget for infrastructure"],"input_types":["user queries","conversation context","LLM requests"],"output_types":["LLM responses","performance metrics","usage logs"],"categories":["customer-support","chatbot","infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_log10__cap_6","uri":"capability://customer.support.customer.support.specific.quality.metrics","name":"customer support-specific quality metrics","description":"Provides pre-built quality metrics and evaluation frameworks tailored to customer support and chatbot use cases. Measures dimensions like answer correctness, tone appropriateness, and customer satisfaction.","intents":["I want metrics that matter for customer support, not generic LLM benchmarks","I need to measure if my chatbot is actually helping customers","I want to compare my support AI against industry standards"],"best_for":["Customer support teams","Chatbot product managers","Enterprise operations teams"],"limitations":["Metrics are domain-specific to support/chatbot","May not apply to other LLM use cases","Requires sufficient data for meaningful measurement"],"requires":["Production chatbot or support AI","Real-time feedback signals","Historical conversation data"],"input_types":["LLM responses","user feedback","conversation context"],"output_types":["quality scores","metric dashboards","benchmark reports"],"categories":["customer-support","chatbot","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_log10__cap_7","uri":"capability://customer.support.hallucination.detection.and.reduction","name":"hallucination detection and reduction","description":"Identifies when LLMs generate false or unsupported information and applies techniques to reduce hallucination rates. Monitors for confidence mismatches and factual inconsistencies in responses.","intents":["I want to catch when my chatbot makes up information","I need to reduce false answers in my customer support AI","I want to know which topics my LLM is unreliable on"],"best_for":["Customer support teams","Chatbot operators","Teams with high accuracy requirements"],"limitations":["Hallucination detection is not 100% accurate","Requires domain knowledge to validate","Some hallucinations are subtle and hard to detect","Reduction techniques may limit response quality"],"requires":["Production LLM deployment","Real-time feedback signals","Domain knowledge or validation data"],"input_types":["LLM responses","user feedback","factual data sources"],"output_types":["hallucination flags","confidence scores","corrected responses"],"categories":["customer-support","chatbot","quality-assurance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_log10__cap_8","uri":"capability://customer.support.feedback.driven.model.improvement.pipeline","name":"feedback-driven model improvement pipeline","description":"Creates an automated workflow that converts user feedback into model improvements. Identifies high-impact feedback patterns and applies optimizations based on aggregate signals.","intents":["I want my chatbot to learn from user feedback automatically","I need to prioritize improvements based on what users care about","I want a continuous improvement cycle without manual intervention"],"best_for":["Enterprise chatbot teams","Customer support operations","Teams with mature feedback collection"],"limitations":["Requires sufficient feedback volume to identify patterns","Automated improvements may introduce new issues","Needs human oversight to prevent degradation","Significant integration effort required"],"requires":["Real-time feedback signals","Production LLM deployment","Historical feedback data","API integration capability"],"input_types":["user feedback","conversation logs","quality metrics"],"output_types":["improvement recommendations","optimized model behavior","impact reports"],"categories":["customer-support","chatbot","optimization"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Production LLM deployment","API integration capability","User-facing feedback mechanism","Real-time feedback data","Baseline accuracy targets","Real-time feedback signals","Sufficient historical feedback data","Historical performance baseline","Data storage infrastructure","Privacy compliance measures"],"failure_modes":["Requires integration into existing LLM pipeline","Depends on users providing explicit feedback","Not effective for silent failures users don't report","Metrics are specific to support/chatbot domain","Requires sufficient feedback data to be statistically meaningful","May not capture all relevant quality dimensions","Optimization improvements are incremental, not transformative","Requires sufficient feedback data to be effective","May not solve fundamental model capability gaps","Significant integration effort required","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39999999999999997,"quality":0.77,"ecosystem":0.2,"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":"2026-05-24T12:16:31.447Z","last_scraped_at":"2026-04-05T13:23:42.546Z","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=log10","compare_url":"https://unfragile.ai/compare?artifact=log10"}},"signature":"yTRvsRiY1w+0RFFmHm6B8PzvwYVVNM2cDr41uCkQdeEV3ooaj2Je/b5ZSj8pnmPZv0NEOrieFu1FWyC55oZXDA==","signedAt":"2026-06-20T03:39:07.434Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/log10","artifact":"https://unfragile.ai/log10","verify":"https://unfragile.ai/api/v1/verify?slug=log10","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"}}