{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_assets-scout","slug":"assets-scout","name":"Assets Scout","type":"product","url":"https://scout.asseter.ai","page_url":"https://unfragile.ai/assets-scout","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_assets-scout__cap_0","uri":"capability://data.processing.analysis.ai.driven.asset.verification.and.validation","name":"ai-driven asset verification and validation","description":"Automatically validates asset data against predefined schemas and business rules using LLM-based reasoning to detect inconsistencies, missing fields, and anomalies in asset records. The system processes asset metadata (serial numbers, condition status, location, ownership) through a verification pipeline that cross-references against historical records and flagged patterns to reduce manual verification overhead by identifying high-risk or suspicious entries for human review.","intents":["Automatically flag assets with incomplete or inconsistent metadata without manual audits","Detect asset status discrepancies (e.g., asset marked 'in use' but not checked out to anyone)","Validate serial numbers and identifiers against known patterns to catch data entry errors","Identify assets at risk of loss, theft, or misclassification based on anomaly detection"],"best_for":["Finance teams managing 100-500 physical assets requiring quarterly verification cycles","IT asset managers tracking hardware inventory across multiple locations","Compliance-heavy organizations (financial services, healthcare) needing audit trails for asset verification"],"limitations":["Verification accuracy depends on quality and completeness of historical asset data — garbage in, garbage out","No built-in support for custom business rules beyond basic schema validation; requires manual configuration per asset type","Batch verification latency scales with dataset size; real-time verification for >10k assets may require pagination"],"requires":["Asset data in structured format (CSV, JSON, or database table)","At least 50-100 historical asset records for pattern learning","Active internet connection for LLM inference (cloud-based)"],"input_types":["structured asset metadata (JSON/CSV)","asset images for visual verification","historical asset records for anomaly baseline"],"output_types":["verification report with flagged records","confidence scores per asset","remediation recommendations"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_1","uri":"capability://data.processing.analysis.real.time.asset.portfolio.health.dashboard","name":"real-time asset portfolio health dashboard","description":"Aggregates asset metadata and verification results into a live dashboard displaying portfolio-level metrics (total asset count, verification status distribution, anomaly rate, location heatmaps) with drill-down capabilities to individual asset records. The dashboard updates asynchronously as new verification runs complete, using WebSocket or polling to push changes to connected clients without requiring page refresh.","intents":["Get a 30-second snapshot of overall asset portfolio health and verification status","Identify which asset categories or locations have the highest anomaly rates","Track verification completion progress during bulk asset audits","Monitor asset depreciation and replacement cycles at a glance"],"best_for":["Asset managers and finance controllers needing executive-level visibility without deep technical knowledge","Operations teams managing multi-location asset deployments requiring real-time status awareness","Compliance officers preparing for audits and needing quick evidence of asset verification coverage"],"limitations":["Dashboard refresh latency depends on verification pipeline speed; large batches (>5k assets) may show stale data for 5-10 minutes","No custom metric definitions — limited to pre-built KPIs (count, status, location); custom calculations require API access","Mobile responsiveness may be limited; optimized for desktop/tablet viewing"],"requires":["Active Assets Scout account with at least one asset record","Modern web browser with WebSocket support (Chrome 16+, Firefox 11+, Safari 6+)","Sufficient asset data volume (>10 records) for meaningful dashboard visualization"],"input_types":["asset metadata from integrated data sources","verification run results","user-defined date ranges for filtering"],"output_types":["interactive HTML dashboard","exportable summary reports (PDF/CSV)","real-time metric updates via WebSocket"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_10","uri":"capability://search.retrieval.asset.search.and.discovery.with.semantic.filtering","name":"asset search and discovery with semantic filtering","description":"Provides full-text and semantic search across asset metadata, enabling users to find assets using natural language queries or structured filters. The search engine indexes asset names, descriptions, tags, and metadata, and uses semantic similarity to surface related assets even if exact keywords don't match. Advanced filtering supports complex queries (e.g., 'laptops purchased in 2023 with >8GB RAM in good condition') without requiring SQL knowledge.","intents":["Find specific assets by name, serial number, or description","Discover assets matching complex criteria (e.g., 'all laptops in warehouse A purchased after 2022')","Identify similar assets for comparison or bulk operations","Search asset history and audit logs for specific changes or events"],"best_for":["Asset coordinators and managers needing quick asset lookup without navigating complex UIs","Users performing ad-hoc asset discovery and analysis","Organizations with large asset portfolios (>1000 assets) where browsing is impractical"],"limitations":["Search performance may degrade with very large portfolios (>100k assets) without proper indexing and pagination","Semantic search accuracy depends on asset metadata quality; sparse descriptions limit semantic matching effectiveness","Advanced filtering syntax may be unintuitive for non-technical users; requires training or documentation","Search results are limited to indexed asset metadata; cannot search unstructured documents or attachments"],"requires":["Asset records with searchable metadata (name, description, tags)","Search index built and maintained (automatic with Assets Scout)","Web browser or API client for search queries"],"input_types":["natural language search queries","structured filter criteria (field, operator, value)","asset identifiers (serial number, asset ID)"],"output_types":["ranked list of matching assets","asset details with highlighted search matches","filter suggestions for refining results"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_2","uri":"capability://text.generation.language.conversational.asset.management.via.chatbot.interface","name":"conversational asset management via chatbot interface","description":"Exposes asset management operations (query, update, verify, report) through a natural language chatbot that parses user intent and translates it into structured API calls. The chatbot maintains conversation context across multiple turns, allowing users to refine queries (e.g., 'show me laptops' → 'filter to 2023 or newer' → 'which ones are in storage?') without re-specifying full parameters each time.","intents":["Query asset inventory using natural language instead of SQL or API syntax","Bulk update asset status or metadata through conversational commands","Generate ad-hoc reports by describing what data you need in plain English","Troubleshoot asset discrepancies by asking contextual questions about specific assets"],"best_for":["Non-technical asset coordinators and administrative staff without API/SQL knowledge","Busy asset managers who prefer quick conversational queries over navigating UI menus","Teams with high staff turnover where training on complex asset management UIs is costly"],"limitations":["Chatbot intent parsing may misinterpret ambiguous queries; complex multi-step operations require explicit confirmation steps, adding latency","No support for bulk operations via chatbot — large-scale updates (>100 records) require API or CSV import","Conversation context is session-scoped; no persistent memory across sessions or users, limiting ability to reference previous queries","Chatbot cannot execute custom business logic or conditional workflows — limited to CRUD operations on asset records"],"requires":["Active Assets Scout account","Web browser or mobile app with chatbot interface enabled","Basic English language proficiency for query phrasing"],"input_types":["natural language text queries","asset identifiers (serial number, asset ID, location name)","date ranges and filter criteria in conversational form"],"output_types":["natural language responses with asset data","formatted tables or lists of matching assets","confirmation messages for update operations"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_3","uri":"capability://data.processing.analysis.automated.asset.categorization.and.tagging","name":"automated asset categorization and tagging","description":"Uses LLM-based classification to automatically assign asset categories, subcategories, and tags based on asset name, description, and metadata patterns. The system learns from user-provided examples and corrections, refining classification accuracy over time through few-shot learning. Categories are mapped to predefined taxonomies (e.g., IT Hardware → Laptop → MacBook Pro) to ensure consistency across the asset portfolio.","intents":["Automatically categorize newly imported assets without manual tagging","Standardize inconsistent asset naming conventions across the portfolio","Suggest relevant tags for asset discovery and filtering","Identify miscategorized assets by comparing predicted vs. actual categories"],"best_for":["Organizations importing assets from multiple legacy systems with inconsistent naming schemes","Asset managers with large portfolios (>500 assets) where manual categorization is prohibitively time-consuming","Teams needing to enforce standardized asset taxonomies across departments"],"limitations":["Classification accuracy depends on asset description quality — sparse or vague descriptions (e.g., 'equipment') may result in low-confidence predictions","Custom taxonomies require manual definition and training examples; no automatic taxonomy inference","Retraining on user corrections is asynchronous and may take hours to reflect in new classifications","No support for hierarchical or multi-label categorization — each asset assigned to single primary category"],"requires":["Asset records with at least name and description fields","Predefined asset taxonomy or willingness to define one","Minimum 20-50 labeled examples for few-shot learning to improve accuracy"],"input_types":["asset name and description text","asset metadata (purchase date, location, condition)","user-provided category corrections for retraining"],"output_types":["predicted asset category and subcategory","confidence score (0-1) for prediction","suggested tags","categorization report with accuracy metrics"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_4","uri":"capability://data.processing.analysis.seamless.data.integration.and.etl.from.multiple.sources","name":"seamless data integration and etl from multiple sources","description":"Provides connectors and import pipelines for ingesting asset data from common sources (CSV/Excel, databases, ERP systems, cloud storage) with automatic schema mapping and deduplication. The ETL pipeline detects and merges duplicate asset records based on configurable matching rules (e.g., matching serial numbers or asset IDs), and performs data normalization (standardizing date formats, unit conversions, location names) before storing in the Assets Scout database.","intents":["Import asset inventory from legacy Excel spreadsheets or CSV exports without manual data entry","Sync asset data from ERP systems (SAP, Oracle) or accounting software (QuickBooks) on a scheduled basis","Consolidate asset records from multiple departments or locations into a single unified portfolio","Detect and merge duplicate asset entries created by data entry errors or system migrations"],"best_for":["Organizations migrating from legacy asset management systems to Assets Scout","Multi-location or multi-department companies with asset data scattered across different systems","Finance teams needing to reconcile asset records from accounting software with physical inventory"],"limitations":["Schema mapping requires manual configuration for non-standard data formats; no automatic schema inference","Deduplication accuracy depends on data quality and matching rule configuration — may miss duplicates with typos or partial matches","Scheduled sync frequency is limited to daily or weekly; real-time bidirectional sync not supported","No support for complex data transformations (e.g., splitting a single field into multiple fields); requires pre-processing"],"requires":["Source data in supported format (CSV, Excel, SQL database, or cloud storage with API access)","API credentials or database connection details for source systems","Schema mapping configuration (field names, data types, transformations)"],"input_types":["CSV/Excel files","SQL database connections","REST API endpoints from ERP/accounting systems","cloud storage files (S3, Google Drive, OneDrive)"],"output_types":["normalized asset records in Assets Scout database","import report with row counts, errors, and deduplication summary","mapping configuration for future imports"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_5","uri":"capability://data.processing.analysis.asset.lifecycle.tracking.and.depreciation.forecasting","name":"asset lifecycle tracking and depreciation forecasting","description":"Tracks asset acquisition date, usage patterns, and maintenance history to automatically calculate depreciation, predict end-of-life, and recommend replacement timing. The system uses historical depreciation curves and asset-specific wear patterns (inferred from maintenance logs and usage frequency) to forecast when assets will reach end-of-service, enabling proactive replacement planning and budget forecasting.","intents":["Automatically calculate asset depreciation for financial reporting and tax purposes","Predict when assets will reach end-of-life based on usage and maintenance patterns","Generate replacement recommendations to avoid unexpected asset failures","Forecast capital expenditure needs for asset replacement cycles"],"best_for":["Finance teams needing accurate asset depreciation for quarterly/annual financial statements","Operations managers planning asset replacement budgets and procurement cycles","Organizations with high asset turnover (e.g., IT hardware, vehicles) requiring predictive maintenance planning"],"limitations":["Depreciation forecasting accuracy depends on historical maintenance and usage data — new assets with limited history may have low-confidence predictions","No support for custom depreciation methods beyond standard straight-line and accelerated depreciation","End-of-life predictions are probabilistic and may not account for unexpected failures or extended asset life due to exceptional maintenance","Requires integration with maintenance tracking system to access usage and repair history"],"requires":["Asset acquisition date and initial cost","Maintenance and repair history (dates, costs, descriptions)","Usage frequency or utilization metrics (optional but improves accuracy)","Depreciation method and useful life parameters per asset type"],"input_types":["asset metadata (acquisition date, cost, condition)","maintenance logs (repair dates, costs, descriptions)","usage metrics (hours in operation, utilization rate)"],"output_types":["depreciation schedule (monthly/quarterly/annual)","end-of-life prediction with confidence interval","replacement recommendation with suggested timing","capital expenditure forecast report"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_6","uri":"capability://data.processing.analysis.multi.location.asset.tracking.and.location.intelligence","name":"multi-location asset tracking and location intelligence","description":"Maintains asset location history and provides location-based analytics (asset distribution by location, location utilization rates, asset movement patterns). The system tracks asset transfers between locations, generates location-specific reports, and can flag assets that are out of expected locations or have unusual movement patterns. Location data is visualized on maps and can be integrated with physical location metadata (e.g., warehouse capacity, climate control).","intents":["Track which assets are in which locations and identify assets in unexpected locations","Analyze asset distribution across multiple warehouses, offices, or facilities","Monitor asset movement patterns to detect theft or unauthorized transfers","Optimize asset allocation based on location-specific demand and utilization"],"best_for":["Organizations with assets distributed across multiple physical locations (warehouses, offices, retail stores)","Logistics and supply chain teams managing asset movement and transfers","Security-conscious organizations needing to track asset movements for theft prevention"],"limitations":["Location tracking accuracy depends on manual location updates or integration with physical tracking systems (RFID, GPS); no built-in hardware tracking","Location history is limited to recorded transfers; cannot reconstruct asset location between recorded updates","Map visualization may be slow with >10k assets; requires pagination or filtering for large portfolios","No real-time location tracking without external hardware integration (e.g., GPS trackers, RFID readers)"],"requires":["Location data (location names, addresses, or geographic coordinates)","Asset transfer records or location update logs","Optional: integration with RFID, GPS, or other physical tracking systems"],"input_types":["asset location updates (manual or automated)","asset transfer records with source and destination locations","location metadata (address, capacity, climate control)"],"output_types":["asset location report with current and historical locations","location-based analytics (distribution, utilization, movement patterns)","map visualization of asset distribution","anomaly alerts for unexpected asset movements"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_7","uri":"capability://data.processing.analysis.asset.compliance.and.audit.trail.generation","name":"asset compliance and audit trail generation","description":"Maintains immutable audit logs of all asset changes (creation, updates, verification, transfers) with timestamps and user attribution. The system generates compliance reports documenting asset verification coverage, change history, and chain-of-custody for regulatory requirements. Audit trails can be exported in formats required by compliance frameworks (SOX, HIPAA, ISO 27001) and support digital signatures for non-repudiation.","intents":["Generate audit trails documenting all asset changes for regulatory compliance","Prove asset verification coverage for financial audits and compliance reviews","Track chain-of-custody for high-value or sensitive assets","Export compliance reports in formats required by auditors and regulators"],"best_for":["Financial services and healthcare organizations subject to regulatory audits (SOX, HIPAA)","Public companies needing to document asset controls for financial statement audits","Organizations managing sensitive or high-value assets requiring chain-of-custody documentation"],"limitations":["Audit logs are immutable but stored in Assets Scout database; no external blockchain or third-party audit log storage for enhanced non-repudiation","Compliance report generation is limited to pre-built templates; custom compliance frameworks require manual configuration","Digital signatures require PKI infrastructure; not all compliance frameworks require or support digital signatures","Audit log retention is subject to Assets Scout's data retention policies; organizations with longer retention requirements may need external archival"],"requires":["Active Assets Scout account with audit logging enabled","User authentication and role-based access control for proper attribution","Optional: PKI infrastructure for digital signatures"],"input_types":["asset change events (creation, updates, transfers, verification)","user identity and role information","compliance framework requirements"],"output_types":["immutable audit logs with timestamps and user attribution","compliance reports in standard formats (PDF, CSV, XML)","digitally signed audit trails (optional)","chain-of-custody documentation"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_8","uri":"capability://data.processing.analysis.asset.condition.assessment.and.maintenance.recommendations","name":"asset condition assessment and maintenance recommendations","description":"Analyzes asset condition data (age, maintenance history, repair costs, utilization) to assess current condition and recommend maintenance actions. The system uses predictive modeling to estimate remaining useful life and suggest preventive maintenance schedules based on asset type and usage patterns. Condition assessments can be informed by user-provided condition ratings or images (for visual condition assessment via OCR/image analysis).","intents":["Assess asset condition based on age, maintenance history, and usage patterns","Generate preventive maintenance recommendations to extend asset life","Prioritize assets for maintenance or replacement based on condition risk","Estimate remaining useful life for budget planning and replacement scheduling"],"best_for":["Operations teams managing assets requiring regular maintenance (vehicles, machinery, HVAC systems)","Facilities managers optimizing maintenance schedules to minimize downtime","Organizations seeking to extend asset life through preventive maintenance"],"limitations":["Condition assessment accuracy depends on quality of maintenance records and repair cost data; incomplete records lead to low-confidence predictions","Maintenance recommendations are probabilistic and may not account for asset-specific factors (e.g., environmental conditions, operator skill)","No integration with maintenance management systems (CMMS) for automated work order generation; recommendations require manual follow-up","Visual condition assessment (via images) is limited to basic condition indicators (rust, damage, cleanliness); cannot assess internal component condition"],"requires":["Asset maintenance history (repair dates, costs, descriptions)","Asset age and acquisition date","Usage metrics or utilization data (optional but improves accuracy)","Optional: asset condition images for visual assessment"],"input_types":["maintenance logs (repair dates, costs, descriptions)","asset age and usage metrics","condition images (for visual assessment)","user-provided condition ratings"],"output_types":["condition assessment score (0-100)","remaining useful life estimate","preventive maintenance recommendations with priority","maintenance schedule","cost-benefit analysis for maintenance vs. replacement"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_assets-scout__cap_9","uri":"capability://data.processing.analysis.asset.cost.analysis.and.total.cost.of.ownership.tco.calculation","name":"asset cost analysis and total cost of ownership (tco) calculation","description":"Aggregates asset acquisition cost, maintenance costs, depreciation, and operational costs to calculate total cost of ownership over asset lifetime. The system compares TCO across asset types or vendors to inform procurement decisions, and identifies high-cost assets for potential replacement or optimization. TCO calculations can be customized to include organization-specific cost factors (e.g., energy consumption, insurance, downtime costs).","intents":["Calculate total cost of ownership for individual assets or asset categories","Compare TCO across vendors or asset types to inform procurement decisions","Identify high-cost assets for potential replacement or optimization","Forecast total asset portfolio costs for budget planning"],"best_for":["Procurement teams evaluating vendor proposals and asset purchase decisions","Finance teams analyzing asset portfolio costs for budget optimization","Organizations seeking to reduce operational costs through asset optimization"],"limitations":["TCO calculations are only as accurate as the cost data provided; incomplete or estimated costs reduce reliability","Custom cost factors require manual configuration; no automatic discovery of organization-specific cost drivers","TCO comparisons across asset types may not account for qualitative factors (e.g., reliability, user satisfaction) that influence true cost","Forecasting future costs (e.g., energy prices, maintenance inflation) requires assumptions that may not hold"],"requires":["Asset acquisition cost","Maintenance and repair costs (historical or estimated)","Depreciation parameters","Optional: operational costs (energy, insurance, downtime)"],"input_types":["asset cost data (acquisition, maintenance, depreciation)","operational cost factors (energy, insurance, downtime)","asset lifetime or useful life parameters"],"output_types":["TCO calculation per asset","TCO comparison across asset types or vendors","cost breakdown by category (acquisition, maintenance, depreciation, operational)","TCO forecast for asset portfolio"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Asset data in structured format (CSV, JSON, or database table)","At least 50-100 historical asset records for pattern learning","Active internet connection for LLM inference (cloud-based)","Active Assets Scout account with at least one asset record","Modern web browser with WebSocket support (Chrome 16+, Firefox 11+, Safari 6+)","Sufficient asset data volume (>10 records) for meaningful dashboard visualization","Asset records with searchable metadata (name, description, tags)","Search index built and maintained (automatic with Assets Scout)","Web browser or API client for search queries","Active Assets Scout account"],"failure_modes":["Verification accuracy depends on quality and completeness of historical asset data — garbage in, garbage out","No built-in support for custom business rules beyond basic schema validation; requires manual configuration per asset type","Batch verification latency scales with dataset size; real-time verification for >10k assets may require pagination","Dashboard refresh latency depends on verification pipeline speed; large batches (>5k assets) may show stale data for 5-10 minutes","No custom metric definitions — limited to pre-built KPIs (count, status, location); custom calculations require API access","Mobile responsiveness may be limited; optimized for desktop/tablet viewing","Search performance may degrade with very large portfolios (>100k assets) without proper indexing and pagination","Semantic search accuracy depends on asset metadata quality; sparse descriptions limit semantic matching effectiveness","Advanced filtering syntax may be unintuitive for non-technical users; requires training or documentation","Search results are limited to indexed asset metadata; cannot search unstructured documents or attachments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.25,"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":"2026-05-24T12:16:29.133Z","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=assets-scout","compare_url":"https://unfragile.ai/compare?artifact=assets-scout"}},"signature":"E243Fq3vhV2LkSf5ax4U/sVkp2JzNMSMOJyFtbL/bAe+eOLgbLf8n0AZdRDyCRq1E1cNRqJCeSUxSWGJ9hrZBA==","signedAt":"2026-06-20T22:42:54.146Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/assets-scout","artifact":"https://unfragile.ai/assets-scout","verify":"https://unfragile.ai/api/v1/verify?slug=assets-scout","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"}}