{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_v-lur","slug":"v-lur","name":"Völur","type":"product","url":"https://volur.no","page_url":"https://unfragile.ai/v-lur","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_v-lur__cap_0","uri":"capability://data.processing.analysis.real.time.production.line.monitoring.with.anomaly.detection","name":"real-time production line monitoring with anomaly detection","description":"Völur ingests sensor data streams from meat processing equipment (temperature, throughput, pressure, line speed) and applies statistical anomaly detection algorithms to identify deviations from optimal operating parameters in real-time. The system likely uses time-series forecasting (ARIMA, Prophet, or neural networks) trained on facility-specific baseline data to distinguish normal variance from equipment degradation or process drift, triggering alerts before quality or safety issues occur.","intents":["detect equipment failures or process drift before they cause downtime or product waste","identify when production lines are operating outside optimal efficiency windows","receive automated alerts when temperature, pressure, or throughput deviate from safe ranges"],"best_for":["meat processing facility operators managing multiple production lines","plant engineers responsible for predictive maintenance and process optimization"],"limitations":["requires facility-specific baseline calibration (typically 2-4 weeks of normal operation data) before anomaly detection becomes reliable","sensor integration is facility-specific; no standardized meat processing equipment APIs exist, requiring custom connectors per equipment manufacturer","latency in anomaly detection depends on sensor polling frequency; real-time detection at sub-second granularity requires industrial IoT infrastructure (not all facilities have this)"],"requires":["industrial IoT sensors on production equipment (temperature, pressure, flow rate sensors)","data ingestion pipeline (MQTT, OPC-UA, or proprietary equipment APIs)","facility-specific historical baseline data (minimum 2-4 weeks of normal operation logs)","network connectivity from production floor to processing backend (on-premise or cloud)"],"input_types":["time-series sensor data (numeric streams)","equipment telemetry (JSON or binary protocol from PLC/SCADA systems)","historical baseline logs"],"output_types":["anomaly alerts (structured events with severity, timestamp, affected equipment)","dashboards with real-time KPI visualization","maintenance recommendations"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_v-lur__cap_1","uri":"capability://planning.reasoning.waste.reduction.optimization.through.processing.parameter.tuning","name":"waste reduction optimization through processing parameter tuning","description":"Völur uses reinforcement learning or Bayesian optimization to iteratively adjust processing parameters (cutting angles, blade speeds, temperature setpoints, conveyor speeds) to minimize trim waste and byproduct loss while maintaining product quality and safety standards. The system models the relationship between parameter combinations and waste output, then recommends or automatically applies adjustments that reduce material loss by 2-5% without violating regulatory constraints (food safety, hygiene, traceability).","intents":["reduce trim waste and byproduct loss by optimizing cutting and processing parameters","find the sweet spot between maximum yield and product quality/safety compliance","automatically adjust line parameters throughout the day as raw material properties vary"],"best_for":["facility managers focused on reducing COGS through waste minimization","sustainability-focused processors seeking to improve material yield and reduce landfill impact"],"limitations":["optimization is facility-specific; models trained on one facility's equipment and raw material sources do not transfer to other facilities without retraining","regulatory constraints (food safety, hygiene standards) must be manually encoded as hard constraints; violations can result in product recalls or facility shutdowns","raw material variability (animal size, fat distribution, muscle quality) requires continuous retraining; seasonal or supplier changes can degrade model accuracy","optimization assumes equipment can be dynamically adjusted; older facilities with manual parameter settings cannot benefit from automated tuning"],"requires":["production data linking parameter settings to waste output (minimum 3-6 months of historical data)","equipment capable of dynamic parameter adjustment (modern PLC-controlled lines)","regulatory documentation defining hard constraints (temperature ranges, hygiene protocols, traceability requirements)","facility-specific waste measurement system (scales, vision systems, or manual logging)"],"input_types":["production parameters (numeric: blade speed, temperature, conveyor speed, cutting angle)","raw material properties (weight, fat content, muscle quality)","waste output measurements (trim weight, byproduct yield)","regulatory constraint definitions (JSON or structured format)"],"output_types":["optimized parameter recommendations (numeric values with confidence intervals)","waste reduction projections (percentage improvement, cost savings)","parameter adjustment schedules (time-series of recommended settings)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_v-lur__cap_2","uri":"capability://data.processing.analysis.energy.consumption.forecasting.and.load.optimization","name":"energy consumption forecasting and load optimization","description":"Völur predicts facility energy consumption patterns (electricity, refrigeration, compressed air) using time-series forecasting models trained on historical consumption data, production schedules, and external factors (ambient temperature, seasonal demand). The system identifies peak consumption windows and recommends load-shifting strategies (scheduling energy-intensive processes during off-peak hours, pre-cooling chillers before peak demand) to reduce energy costs and grid strain, with integration to facility SCADA systems for automated demand response.","intents":["forecast daily and weekly energy consumption to optimize procurement and identify cost-saving opportunities","shift energy-intensive processes (freezing, chilling, cleaning) to off-peak hours to reduce electricity costs","participate in grid demand-response programs to earn revenue from load reduction during peak periods"],"best_for":["facility managers in regions with time-of-use electricity pricing or demand charges","sustainability-focused processors seeking to reduce carbon footprint and energy costs simultaneously"],"limitations":["forecasting accuracy degrades during anomalous events (equipment failures, unexpected production changes, extreme weather); requires manual override capability","load-shifting recommendations assume flexibility in production scheduling; facilities with rigid customer delivery windows have limited optimization potential","integration with facility SCADA/energy management systems is non-standard; requires custom API development per facility","energy cost savings depend on local electricity pricing structure; benefits are minimal in regions with flat-rate pricing or no demand-response programs"],"requires":["12+ months of historical energy consumption data (hourly or sub-hourly granularity)","production schedule data (planned production volumes, line assignments)","facility SCADA system or energy management system with API access","local electricity pricing data (time-of-use rates, demand charges, grid tariffs)","weather data API (ambient temperature, humidity for refrigeration load modeling)"],"input_types":["historical energy consumption (numeric time-series: kWh, kW demand)","production schedules (structured: line ID, product type, volume, duration)","weather data (temperature, humidity)","electricity pricing (time-of-use rates, demand charge structure)"],"output_types":["energy consumption forecasts (time-series with confidence intervals)","load-shifting recommendations (structured: process type, recommended time window, estimated savings)","demand-response participation signals (binary: participate/don't participate in grid event)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_v-lur__cap_3","uri":"capability://data.processing.analysis.regulatory.compliance.tracking.and.traceability.documentation","name":"regulatory compliance tracking and traceability documentation","description":"Völur maintains an audit trail of all production parameters, equipment settings, and quality measurements, automatically mapping them to regulatory requirements (EU food safety regulations, HACCP protocols, animal welfare standards). The system generates compliance reports and traceability documentation on demand, linking product batches to raw material sources, processing conditions, and equipment used, enabling rapid response to recalls or regulatory audits.","intents":["maintain complete traceability from raw material to finished product for regulatory compliance and recall response","automatically generate compliance reports for EU food safety audits and certifications","quickly identify which batches were affected by equipment failures or parameter deviations"],"best_for":["facility compliance officers and quality assurance teams managing regulatory documentation","facilities exporting to EU markets or operating under strict food safety certifications (BRC, FSSC 22000)"],"limitations":["compliance requirements vary by jurisdiction and certification standard; Völur must be configured per facility and regulatory context","traceability accuracy depends on accurate data entry at production time; garbage-in-garbage-out if sensor data or manual logging is incomplete","integration with existing quality management systems (QMS) is non-standard; requires custom data mapping per facility","regulatory requirements change frequently; Völur requires manual updates to compliance rules and report templates"],"requires":["production data with timestamps and equipment identifiers (from SCADA or manual logging)","raw material tracking system (supplier, batch ID, arrival date)","quality measurement data (microbial testing, temperature logs, weight measurements)","regulatory requirement definitions (HACCP protocols, temperature ranges, cleaning schedules)","facility quality management system (QMS) or database for historical records"],"input_types":["production logs (structured: timestamp, equipment ID, parameters, operator)","raw material data (supplier, batch ID, quantity, quality metrics)","quality measurements (numeric: temperature, microbial counts, weight)","regulatory requirement definitions (JSON or structured format)"],"output_types":["traceability reports (PDF or structured: batch ID, raw materials, processing history, equipment used)","compliance audit reports (formatted for regulatory submission)","recall impact assessments (list of affected batches and customers)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_v-lur__cap_4","uri":"capability://planning.reasoning.production.scheduling.optimization.with.constraint.satisfaction","name":"production scheduling optimization with constraint satisfaction","description":"Völur solves the facility production scheduling problem by modeling constraints (equipment availability, cleaning schedules, product changeover times, delivery deadlines, raw material availability) and optimizing the sequence of production runs to minimize changeover losses, equipment idle time, and working capital tied up in inventory. The system uses constraint satisfaction programming (CSP) or mixed-integer linear programming (MILP) to find feasible schedules that balance throughput, waste reduction, and on-time delivery.","intents":["create production schedules that minimize changeover waste and equipment idle time","balance production volume with delivery deadlines and raw material availability","identify bottleneck equipment and recommend capacity investments"],"best_for":["production planners managing multi-product, multi-line facilities with complex constraints","facilities with high changeover costs (e.g., switching between different product types requires equipment cleaning and recalibration)"],"limitations":["scheduling optimization assumes deterministic processing times; real-world variability (equipment failures, quality issues) can invalidate schedules within hours","constraint modeling is facility-specific; each facility has unique equipment, product mix, and operational rules requiring custom constraint definition","optimization is computationally expensive for large facilities (50+ SKUs, 10+ production lines); solving times can exceed 30 minutes, limiting real-time rescheduling","human factors (operator preferences, informal practices) are difficult to model; operators may deviate from recommended schedules"],"requires":["product data (processing time per product, changeover time between products, quality requirements)","equipment data (capacity, availability windows, maintenance schedules, cleaning requirements)","demand forecast or order backlog (product, quantity, delivery deadline)","raw material availability data (inventory levels, delivery schedules)","constraint definitions (cleaning protocols, equipment downtime windows, regulatory requirements)"],"input_types":["product specifications (numeric: processing time, changeover time, quality requirements)","equipment inventory (structured: equipment ID, capacity, availability)","demand data (structured: product ID, quantity, deadline)","raw material availability (numeric: inventory levels, delivery schedule)","constraint definitions (JSON or structured format)"],"output_types":["production schedules (structured: time window, equipment assignment, product, quantity)","changeover plans (structured: source product, target product, cleaning steps, duration)","capacity utilization reports (numeric: equipment utilization %, bottleneck identification)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_v-lur__cap_5","uri":"capability://data.processing.analysis.quality.prediction.and.product.grading.optimization","name":"quality prediction and product grading optimization","description":"Völur predicts product quality attributes (color, texture, fat content, microbial safety) based on raw material properties and processing parameters, enabling early identification of batches at risk of quality issues or downgrade. The system uses supervised learning models (regression, classification) trained on historical quality measurements and processing data to recommend parameter adjustments that improve yield of premium grades and reduce downgrade losses.","intents":["predict product quality before final inspection to enable early intervention","identify which processing parameters most influence premium grade yield","recommend parameter adjustments to maximize high-grade product output"],"best_for":["quality assurance teams seeking to reduce downgrade losses and improve product consistency","facilities with premium product lines where quality consistency is a competitive advantage"],"limitations":["quality prediction accuracy depends on comprehensive quality measurement data; many facilities lack automated quality measurement systems and rely on subjective grading","raw material variability (animal genetics, diet, handling) introduces noise that limits model predictability; models trained on one supplier's animals may not generalize to other suppliers","quality attributes are often correlated with processing parameters in non-linear ways; simple linear models underperform, requiring complex models (neural networks) that are harder to interpret and debug","quality measurement systems are facility-specific and often proprietary; no standardized quality measurement APIs exist"],"requires":["historical quality measurement data (minimum 6-12 months, ideally with automated measurement systems)","processing parameter data linked to quality outcomes","raw material property data (animal weight, fat content, muscle quality)","quality grading standards (premium, standard, downgrade criteria)","quality measurement system (automated or manual with consistent grading)"],"input_types":["raw material properties (numeric: weight, fat content, muscle quality)","processing parameters (numeric: temperature, time, speed, pressure)","quality measurements (numeric: color, texture, microbial counts; categorical: grade)"],"output_types":["quality predictions (numeric: probability of premium grade, predicted quality attributes)","parameter recommendations (numeric: suggested adjustments to maximize premium grade yield)","quality risk alerts (categorical: batch at risk of downgrade, recommended actions)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_v-lur__cap_6","uri":"capability://data.processing.analysis.facility.wide.sustainability.metrics.aggregation.and.reporting","name":"facility-wide sustainability metrics aggregation and reporting","description":"Völur aggregates operational data (energy consumption, water usage, waste output, byproduct recovery) and calculates facility-wide sustainability KPIs (carbon footprint, water efficiency, waste reduction rate, circular economy metrics). The system generates sustainability reports for stakeholder communication (retailers, certifiers, investors) and identifies optimization opportunities to improve sustainability performance.","intents":["calculate facility carbon footprint and track progress toward sustainability targets","generate sustainability reports for retailer requirements and certifications (B Corp, EU Ecolabel)","identify which operational changes (energy efficiency, waste reduction) have the largest sustainability impact"],"best_for":["sustainability officers and facility managers seeking to meet retailer sustainability requirements","facilities pursuing sustainability certifications or ESG reporting"],"limitations":["sustainability metrics require accurate measurement of energy, water, and waste; many facilities lack comprehensive metering and must estimate consumption","carbon footprint calculation depends on electricity grid carbon intensity, which varies by region and time of day; Völur must be configured per facility location","sustainability metrics are often defined differently by different stakeholders (retailers, certifiers, investors); Völur requires custom metric definitions per stakeholder","sustainability improvements often require capital investment (equipment upgrades, process changes) with long payback periods; ROI is difficult to quantify"],"requires":["energy consumption data (electricity, natural gas, water)","waste measurement data (trim waste, byproduct output, landfill disposal)","byproduct recovery data (rendering, animal feed, composting)","facility location data (for grid carbon intensity calculation)","sustainability metric definitions (carbon footprint methodology, water efficiency targets)"],"input_types":["energy consumption (numeric: kWh, cubic meters of water)","waste output (numeric: kg of trim, byproduct, landfill disposal)","byproduct recovery (numeric: kg of rendered products, animal feed, compost)","facility location (geographic: latitude/longitude or region)"],"output_types":["sustainability KPIs (numeric: carbon footprint in kg CO2e, water efficiency in liters per kg product)","sustainability reports (PDF or structured: metrics, trends, improvement opportunities)","benchmarking data (comparison to industry averages or peer facilities)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["industrial IoT sensors on production equipment (temperature, pressure, flow rate sensors)","data ingestion pipeline (MQTT, OPC-UA, or proprietary equipment APIs)","facility-specific historical baseline data (minimum 2-4 weeks of normal operation logs)","network connectivity from production floor to processing backend (on-premise or cloud)","production data linking parameter settings to waste output (minimum 3-6 months of historical data)","equipment capable of dynamic parameter adjustment (modern PLC-controlled lines)","regulatory documentation defining hard constraints (temperature ranges, hygiene protocols, traceability requirements)","facility-specific waste measurement system (scales, vision systems, or manual logging)","12+ months of historical energy consumption data (hourly or sub-hourly granularity)","production schedule data (planned production volumes, line assignments)"],"failure_modes":["requires facility-specific baseline calibration (typically 2-4 weeks of normal operation data) before anomaly detection becomes reliable","sensor integration is facility-specific; no standardized meat processing equipment APIs exist, requiring custom connectors per equipment manufacturer","latency in anomaly detection depends on sensor polling frequency; real-time detection at sub-second granularity requires industrial IoT infrastructure (not all facilities have this)","optimization is facility-specific; models trained on one facility's equipment and raw material sources do not transfer to other facilities without retraining","regulatory constraints (food safety, hygiene standards) must be manually encoded as hard constraints; violations can result in product recalls or facility shutdowns","raw material variability (animal size, fat distribution, muscle quality) requires continuous retraining; seasonal or supplier changes can degrade model accuracy","optimization assumes equipment can be dynamically adjusted; older facilities with manual parameter settings cannot benefit from automated tuning","forecasting accuracy degrades during anomalous events (equipment failures, unexpected production changes, extreme weather); requires manual override capability","load-shifting recommendations assume flexibility in production scheduling; facilities with rigid customer delivery windows have limited optimization potential","integration with facility SCADA/energy management systems is non-standard; requires custom API development per facility","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"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":"2026-05-24T12:16:33.649Z","last_scraped_at":"2026-04-05T13:23:42.559Z","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=v-lur","compare_url":"https://unfragile.ai/compare?artifact=v-lur"}},"signature":"lh11uQf52+8xA1ct+UPd/eR4k6yD64ZAcUK7cpDJQbFYzY0B/od9wtfT6QS6KNlpPwARuAU+Ycnizhy4FddYCQ==","signedAt":"2026-06-21T15:53:15.033Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/v-lur","artifact":"https://unfragile.ai/v-lur","verify":"https://unfragile.ai/api/v1/verify?slug=v-lur","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"}}