{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_opinioai","slug":"opinioai","name":"OpinioAI","type":"product","url":"https://opinio.ai","page_url":"https://unfragile.ai/opinioai","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_opinioai__cap_0","uri":"capability://data.processing.analysis.automated.survey.response.analysis.and.coding","name":"automated survey response analysis and coding","description":"Processes open-ended survey responses using NLP-based text classification to automatically extract themes, sentiment, and behavioral patterns without manual coding. The system likely employs transformer-based language models to parse qualitative feedback, cluster similar responses, and assign semantic tags or categories, reducing the manual effort of traditional thematic analysis from hours to minutes.","intents":["I need to analyze 500+ survey responses without hiring a research analyst","I want to identify recurring themes in customer feedback automatically","I need sentiment and intent extraction from open-ended survey text at scale"],"best_for":["lean product teams without dedicated research staff","startups conducting rapid customer discovery cycles","marketers analyzing feedback from product surveys or NPS campaigns"],"limitations":["AI-driven analysis may misinterpret nuanced sentiment, sarcasm, or cultural context in qualitative feedback","No visibility into model confidence scores or uncertainty quantification — unclear when results are unreliable","Likely struggles with domain-specific jargon or industry terminology not well-represented in training data","No human-in-the-loop validation workflow documented — difficult to correct systematic misclassifications"],"requires":["Survey data in text format (CSV, JSON, or direct upload)","Active OpinioAI account (freemium tier available)","Minimum 10-20 responses for meaningful pattern detection"],"input_types":["text (survey responses)","CSV/JSON (structured survey exports)","plain text (copy-paste feedback)"],"output_types":["structured JSON (themes, sentiment scores, categories)","text summaries (key insights, trend reports)","visualizations (sentiment distribution, theme frequency)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_opinioai__cap_1","uri":"capability://data.processing.analysis.customer.behavior.pattern.inference.from.survey.data","name":"customer behavior pattern inference from survey data","description":"Extracts behavioral insights and customer intent patterns from survey responses by mapping text to behavioral categories (e.g., churn risk, feature requests, pain points, loyalty signals). The system likely uses intent classification models and behavioral taxonomies to infer actionable customer segments and predict next-best actions without requiring explicit behavioral tracking data.","intents":["I want to identify which customers are at risk of churning based on survey feedback","I need to segment customers by behavior type (early adopters, skeptics, power users) from their responses","I want to extract feature requests and prioritize them by frequency and sentiment"],"best_for":["product managers prioritizing roadmap based on customer feedback","customer success teams identifying at-risk accounts for intervention","growth teams segmenting users for targeted retention campaigns"],"limitations":["Behavioral inference is probabilistic — no ground truth validation against actual customer actions (churn, feature adoption)","Requires sufficient survey context to infer behavior; sparse or vague responses yield low-confidence predictions","No temporal tracking — cannot detect behavior changes over time without multiple survey rounds","Lacks integration with actual customer data (usage logs, billing, support tickets) needed to validate inferences"],"requires":["Survey responses with behavioral context (e.g., product usage, satisfaction, intent questions)","Minimum 50+ responses per segment for reliable pattern detection","OpinioAI account with behavior analysis feature enabled"],"input_types":["text (survey responses with behavioral context)","structured metadata (customer segment, product usage level, tenure)"],"output_types":["behavioral segments (JSON with segment labels and confidence scores)","risk scores (churn probability, feature adoption likelihood)","actionable recommendations (text summaries of suggested interventions)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_opinioai__cap_2","uri":"capability://text.generation.language.instant.insight.summarization.and.report.generation","name":"instant insight summarization and report generation","description":"Generates executive summaries, trend reports, and insight dashboards from survey analysis results using abstractive summarization and templated report generation. The system likely uses prompt-based summarization to distill key findings, highlight outliers, and present actionable recommendations in natural language, enabling non-technical stakeholders to consume insights without diving into raw data.","intents":["I need a one-page executive summary of customer feedback for a board meeting","I want to share survey insights with my team without requiring them to use the OpinioAI interface","I need to track how customer sentiment has changed across multiple survey rounds"],"best_for":["product managers and executives needing quick insight summaries","teams sharing customer research findings across departments","stakeholders without direct access to OpinioAI who need digestible reports"],"limitations":["Abstractive summarization may omit important edge cases or minority viewpoints in favor of majority themes","Report templates are likely generic — no customization for industry-specific metrics or KPIs","No version control or audit trail for reports — difficult to track how insights evolved or were used in decisions","Summaries are static snapshots; no real-time dashboard or drill-down capability to explore underlying data"],"requires":["Completed survey analysis in OpinioAI","Minimum 20+ responses for meaningful summary generation","Export capability (PDF, email, or dashboard link)"],"input_types":["structured analysis results (themes, sentiment, segments from prior analysis)"],"output_types":["text (executive summaries, insight narratives)","PDF reports (formatted with charts and key findings)","email-friendly summaries (plain text or HTML)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_opinioai__cap_3","uri":"capability://data.processing.analysis.multi.survey.comparative.analysis.and.trend.tracking","name":"multi-survey comparative analysis and trend tracking","description":"Compares insights across multiple survey rounds or cohorts to identify sentiment trends, emerging themes, and behavioral shifts over time. The system likely maintains a historical index of survey analyses and uses differential analysis to highlight what changed between surveys, enabling teams to measure the impact of product changes or marketing campaigns on customer perception.","intents":["I want to see if customer sentiment improved after we launched a new feature","I need to compare feedback from different customer segments (free vs paid, new vs loyal)","I want to track how specific pain points have evolved across quarterly surveys"],"best_for":["product teams measuring feature impact through customer feedback","marketing teams validating campaign messaging resonance across cohorts","customer success teams tracking satisfaction trends over time"],"limitations":["Comparative analysis requires consistent survey structure across rounds — changes to questions or response formats break trend continuity","No statistical significance testing documented — unclear if observed changes are meaningful or noise","Temporal resolution is limited to survey frequency; cannot detect real-time sentiment shifts between surveys","No causal inference — cannot definitively link sentiment changes to specific product or marketing actions"],"requires":["Multiple survey datasets (minimum 2 rounds for comparison)","Consistent survey questions or metadata tags across rounds","OpinioAI account with historical data retention"],"input_types":["text (survey responses from multiple time periods or cohorts)","structured metadata (survey round, cohort, timestamp)"],"output_types":["trend visualizations (sentiment over time, theme emergence/decline)","comparative reports (what changed between surveys, new themes, resolved issues)","delta analysis (JSON with theme deltas, sentiment shifts, new segments)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_opinioai__cap_4","uri":"capability://automation.workflow.freemium.tier.survey.analysis.with.usage.quotas","name":"freemium-tier survey analysis with usage quotas","description":"Provides free access to core survey analysis capabilities (response coding, sentiment extraction, basic reporting) with usage limits (e.g., responses per month, surveys per quarter) to enable low-friction customer research adoption. The system likely implements quota enforcement at the API/UI level and offers transparent upgrade paths to paid tiers for higher volume or advanced features.","intents":["I want to try customer research tools without committing budget","I need to analyze a small batch of survey responses (under 100) without paying","I want to evaluate OpinioAI before recommending it to my organization"],"best_for":["solo founders and bootstrapped startups with minimal research budgets","teams piloting customer research workflows before scaling","individuals learning survey analysis techniques"],"limitations":["Free tier quotas are not publicly documented — unclear limits on responses, surveys, or features per month","Upgrade pricing and feature tiers are not transparent — difficult to forecast costs at scale","Free tier likely lacks advanced features (comparative analysis, custom taxonomies, API access) available only in paid plans","No SLA or support for free tier — production use cases may lack reliability guarantees"],"requires":["OpinioAI account (free registration)","Survey data within free tier quotas","No credit card required for free tier signup"],"input_types":["text (survey responses)","CSV/JSON (structured survey exports)"],"output_types":["analysis results (themes, sentiment, summaries)","basic reports (text summaries, simple visualizations)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_opinioai__cap_5","uri":"capability://data.processing.analysis.sentiment.and.emotion.classification.from.survey.text","name":"sentiment and emotion classification from survey text","description":"Classifies survey responses into sentiment categories (positive, negative, neutral) and detects emotional undertones (frustration, delight, confusion) using fine-tuned NLP models. The system likely employs multi-label classification to capture mixed sentiments (e.g., positive about feature, negative about pricing) and emotion detection models trained on customer feedback datasets.","intents":["I want to quantify how many customers are satisfied vs dissatisfied with our product","I need to identify frustrated customers for immediate support outreach","I want to measure emotional resonance of marketing messaging in customer feedback"],"best_for":["customer success teams prioritizing support interventions by sentiment","product teams measuring satisfaction metrics from qualitative feedback","marketing teams validating emotional impact of campaigns"],"limitations":["Sentiment classification struggles with sarcasm, irony, and context-dependent language common in customer feedback","No confidence scores or uncertainty quantification — unclear when sentiment predictions are unreliable","Emotion detection is likely coarse-grained (frustration, delight) rather than fine-grained (disappointment, excitement, skepticism)","No domain adaptation — models trained on general text may misclassify industry-specific or product-specific sentiment expressions"],"requires":["Survey responses in text format","Minimum 10-20 responses for meaningful sentiment distribution","OpinioAI account with sentiment analysis enabled"],"input_types":["text (survey responses, open-ended feedback)"],"output_types":["sentiment scores (positive/negative/neutral with confidence)","emotion labels (frustration, delight, confusion, etc.)","sentiment distribution (JSON with percentages, visualizations)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_opinioai__cap_6","uri":"capability://data.processing.analysis.theme.extraction.and.topic.clustering.from.qualitative.feedback","name":"theme extraction and topic clustering from qualitative feedback","description":"Automatically identifies recurring themes, topics, and topics from survey responses using unsupervised clustering and topic modeling techniques. The system likely employs LDA (Latent Dirichlet Allocation) or neural topic models to discover latent themes without predefined categories, then labels themes with human-readable names using LLM-based summarization.","intents":["I want to discover what customers are talking about without predefined categories","I need to identify the top 5-10 themes in customer feedback automatically","I want to see how many responses mention each theme or topic"],"best_for":["product teams exploring customer feedback without predefined hypotheses","research teams discovering emerging issues or feature requests","teams analyzing unstructured feedback at scale (100+ responses)"],"limitations":["Unsupervised clustering produces variable quality themes — some may be redundant, overlapping, or nonsensical without human review","Theme naming is automated (LLM-based) and may be inaccurate or misleading for domain-specific topics","No control over theme granularity — system may produce overly broad themes (e.g., 'product feedback') or overly specific ones (e.g., 'button color')","Requires sufficient data volume (50+ responses) for meaningful clustering; sparse datasets produce unreliable themes"],"requires":["Survey responses in text format (minimum 50+ for reliable clustering)","OpinioAI account with theme extraction enabled","Tolerance for automated theme naming requiring manual review"],"input_types":["text (survey responses, open-ended feedback)"],"output_types":["theme list (JSON with theme labels, descriptions, response counts)","theme distribution (visualizations showing theme frequency)","theme-response mapping (which responses belong to each theme)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_opinioai__cap_7","uri":"capability://tool.use.integration.manual.data.export.and.crm.integration.limitations","name":"manual data export and crm integration limitations","description":"Requires manual export of survey data from OpinioAI and import into external tools (CRM, analytics platforms, spreadsheets) due to lack of native API integrations or CRM connectors. The system likely supports CSV/JSON export but lacks bidirectional sync, webhooks, or pre-built connectors for Salesforce, HubSpot, or other CRM platforms.","intents":["I need to export survey analysis results to share with my team in Salesforce","I want to sync customer sentiment scores back to our CRM for account management","I need to combine OpinioAI insights with usage data from our analytics platform"],"best_for":["teams with simple, one-time export workflows","organizations with custom data pipelines that can handle manual exports","teams not requiring real-time CRM synchronization"],"limitations":["No native CRM integrations (Salesforce, HubSpot, Pipedrive) — requires manual export and import workflows","No API access documented — cannot programmatically retrieve analysis results or automate exports","No webhooks or event-based triggers — cannot push insights to external systems in real-time","Manual export workflows are error-prone and don't scale beyond small teams or infrequent analyses","No bidirectional sync — changes in CRM or external systems don't update OpinioAI analyses"],"requires":["Manual export capability (CSV, JSON, or email)","External tools for data import (CRM, spreadsheet, analytics platform)","Manual data mapping and transformation between OpinioAI and external systems"],"input_types":["structured analysis results (themes, sentiment, segments)"],"output_types":["CSV (for spreadsheet import)","JSON (for custom integrations)","email (for sharing reports)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Survey data in text format (CSV, JSON, or direct upload)","Active OpinioAI account (freemium tier available)","Minimum 10-20 responses for meaningful pattern detection","Survey responses with behavioral context (e.g., product usage, satisfaction, intent questions)","Minimum 50+ responses per segment for reliable pattern detection","OpinioAI account with behavior analysis feature enabled","Completed survey analysis in OpinioAI","Minimum 20+ responses for meaningful summary generation","Export capability (PDF, email, or dashboard link)","Multiple survey datasets (minimum 2 rounds for comparison)"],"failure_modes":["AI-driven analysis may misinterpret nuanced sentiment, sarcasm, or cultural context in qualitative feedback","No visibility into model confidence scores or uncertainty quantification — unclear when results are unreliable","Likely struggles with domain-specific jargon or industry terminology not well-represented in training data","No human-in-the-loop validation workflow documented — difficult to correct systematic misclassifications","Behavioral inference is probabilistic — no ground truth validation against actual customer actions (churn, feature adoption)","Requires sufficient survey context to infer behavior; sparse or vague responses yield low-confidence predictions","No temporal tracking — cannot detect behavior changes over time without multiple survey rounds","Lacks integration with actual customer data (usage logs, billing, support tickets) needed to validate inferences","Abstractive summarization may omit important edge cases or minority viewpoints in favor of majority themes","Report templates are likely generic — no customization for industry-specific metrics or KPIs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3333333333333333,"quality":0.6900000000000001,"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":"2026-05-24T12:16:31.859Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=opinioai","compare_url":"https://unfragile.ai/compare?artifact=opinioai"}},"signature":"aafroFt72+pOD6U96/zdt1n1nNx0iQzq4FiDok3KWYqOdq93LZC8A1nLaBicAaVPWtgDCLSxXy7E4NFT+awGAg==","signedAt":"2026-06-21T04:23:10.123Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/opinioai","artifact":"https://unfragile.ai/opinioai","verify":"https://unfragile.ai/api/v1/verify?slug=opinioai","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"}}