Frequently Asked Questions

Trakt is an AI-powered predictive maintenance platform built for aviation. It uses multiple AI models working together with physics-based degradation analysis to predict component failures weeks in advance, helping operators move from reactive to proactive maintenance.

Trakt serves commercial airlines, cargo operators, defense organizations, and emergency services fleets. It has been validated across narrowbody and widebody aircraft, military platforms, and ground-based vehicle fleets.

While aviation is the primary focus, Trakt's predictive intelligence has been successfully adapted to other sectors. A metropolitan ambulance corporation used it to improve response times by nearly 80%, and future applications include refineries, manufacturing, mining, logistics, maritime, and rail systems.

Trakt runs six specialized AI models simultaneously: an Early Warning System for anomaly detection, a Failure Prevention model, Component Life Tracking for remaining useful life estimates, Smart Scheduling for maintenance timing, Fleet Optimization, and Parts Forecasting. These models cross-validate each other and automatically select the best prediction for each component.

The failure prevention model achieves ~95% accuracy with a false positive rate below 1%. Component life estimates are accurate to within plus or minus 8%, and the early warning system has a ~97% detection rate. These figures have been validated across multiple live deployments.

Trakt generates multi-horizon forecasts at regular intervals out to 90 days, covering roughly 13 weeks of forward visibility. This gives maintenance teams enough lead time to plan work orders, source parts, and schedule downtime well before a component reaches a critical state.

Yes. Trakt tracks both flight hours and flight cycles (landings) as separate parameters for every aircraft and component. The system calculates a usage intensity profile based on the ratio between the two, so an aircraft operating frequent short-haul rotations will show a very different wear pattern from one flying long-haul routes with far fewer landings per flight hour. This matters especially for cycle-sensitive systems like landing gear, brakes, pressurization, and structural fatigue-limited parts.

Trakt has specialized models for ATA chapters including Air Conditioning (ATA 21), Communications (ATA 23), Electrical Power (ATA 24), Flight Controls (ATA 27), Fuel Systems (ATA 28), Hydraulic Power (ATA 29), Landing Gear (ATA 32), Navigation (ATA 34), APU (ATA 49), and Power Plant (ATA 70-80), among others.

Four physics-based degradation models are applied depending on component condition: a Statistical Life Model for healthy components operating normally, a Linear Wear Model for steady and predictable wear, an Accelerated Degradation Model for components with increasing wear rates, and a Critical Wear Model for parts at very low health levels that need immediate attention. Trakt selects the right model automatically based on real-time component data.

Trakt uses a Multi-AI Intelligence approach where multiple prediction models run in parallel within each of its six tools and cross-validate their results. For example, the Early Warning System blends three independent detection methods, each tuned for different types of irregularity. Failure Prevention combines four classification techniques through a consensus mechanism, and Component Life Tracking fuses five separate statistical and probabilistic methods to estimate remaining useful life. The system uses automatic best-model selection across all six tools, so the strongest prediction always rises to the top.

Trakt collects real-time and historical weather data from the airports your fleet operates into, including temperature, humidity, wind, visibility, and atmospheric pressure. This environmental data is merged into the AI training pipeline so that models learn how conditions such as extreme heat, high humidity, salt air exposure, or frequent icing events affect degradation rates for specific component types. Weather impact analysis is available per component category, and the system adjusts predictions automatically when operating environments change.

The physics-based degradation engine processes temperature, vibration, and pressure readings at a minimum. Each component type has its own normal operating ranges and critical thresholds defined in the system. For example, engine vibration thresholds differ from those used for flight control surfaces or hydraulic systems. These sensor inputs are combined into a single condition indicator score that determines which degradation model is applied and how quickly health is projected to decline.

Yes. Every component receives a full survival analysis that includes a survival probability curve showing the likelihood of continued operation over time, a hazard rate representing the instantaneous risk of failure at any given point, and a remaining useful life distribution showing the range of likely outcomes rather than just a single number. This gives engineering teams a statistical view of component reliability and helps them make better-informed decisions about when to schedule maintenance.

Yes. Beyond the six core AI models, Trakt supports specialized model training for specific engine programmes such as the CFM56 and LEAP families, as well as airframe-specific models for the Boeing 737 and Airbus A320 families. There are also dedicated models for landing gear wear, APU performance degradation, and engine-specific deterioration patterns. These specialized models are trained on your fleet data and layer on top of the general models to improve accuracy for your particular operating profile.

Trakt processes ACARS and ADS-B flight telemetry, sensor data (temperature, vibration, pressure, RPM), MRO maintenance records, pilot reports (PIREPs), weather data, and OEM technical bulletins. Data ingestion runs at a 99.7% success rate.

Your data is processed securely in the cloud. Trakt handles the ingestion, feature engineering, and model training on cloud infrastructure so there are no heavy on-premise compute requirements on your side. All data in transit and at rest is encrypted.

Absolutely. Trakt uses a federated learning approach, which means your proprietary data is never shared with other operators. The system can learn from aggregate, anonymized industry-wide patterns to improve predictions over time, but your maintenance records, sensor readings, and fleet specifics remain entirely private. Participation in the industry learning programme is optional.

Trakt uses TLS 1.3 and AES-256 encryption. It is deployable on AWS GovCloud and is GDPR compliant. For defense clients, the platform has been validated against CBM+ (Condition-Based Maintenance Plus) standards.

Trakt logs every significant action on the platform, categorized by type: authentication events, data operations, prediction runs, report generation, configuration changes, API access, system events, and administrative actions. Each log entry captures the user, timestamp, IP address, session identifier, browser and device details, and a severity level. This comprehensive audit trail supports internal compliance reviews, regulatory inspections, and security investigations.

Once you connect your data sources, Trakt's training pipeline collects your sensor readings, maintenance history, component records, and relevant weather data for your operating airports. When enough data has been accumulated to meet the quality thresholds for a given model, training is triggered automatically in the cloud. There is no manual step required from your side.

Each of Trakt's AI models has minimum data requirements that must be met before training can begin. These vary by model type: some need only 24+ months of sensor history, while others require 36+ months of maintenance records to produce reliable results. As your data flows in, Trakt continuously checks whether the thresholds are met and starts training the moment they are. This ensures predictions are always backed by a sufficient volume of real operational data.

Yes. As more data accumulates from your ongoing operations, Trakt periodically retrains models to capture new patterns, seasonal effects, and changes in your fleet's usage profile. Weather data from your operating airports is also incorporated to account for environmental factors. The result is predictions that become more tailored to your specific fleet over time.

Trakt supports over fifteen distinct trainable model types organized into four categories. Predictive models power the Early Warning System, Failure Prevention, Component Life Tracking, and survival analysis. Optimization models drive Smart Scheduling, Fleet Optimization, and Parts Forecasting. Physics-based models apply established engineering methods for component fatigue, thermal degradation, crack growth, and end-of-life behaviour. Specialized models target specific engine programmes, airframe families, landing gear wear, and APU performance. Your company administrator can see which models are trained and their readiness status at any time.

Training runs asynchronously in the background so it never blocks your day-to-day use of the platform. When a training job is submitted, it is queued and processed by a dedicated worker. You can monitor progress, view completion status, and see results without any interruption to dashboards, alerts, or API access.

Yes. Trakt includes hybrid and physics-informed machine learning models that combine first-principles degradation equations with data-driven AI. For example, a physics model sets the baseline degradation curve for an engine using thermodynamic principles, while the AI layer adjusts that curve based on patterns it has learned from your fleet's actual sensor and maintenance data. This hybrid approach is especially useful when historical data is limited, because the physics model provides a sound starting point that the AI refines over time.

No. Trakt is designed to work alongside your existing systems. It connects to your MRO platform, ingests historical and real-time data, applies AI predictions, and pushes actionable insights back. Your current system of record stays in place.

Trakt supports AMOS, TRAX, SAP (S/4HANA and ECC), Airbus Skywise, Boeing AnalytX, IBM Maximo, IFS Maintenix, Veryon, Ramco, Ultramain, OASES, EmpowerMX, Quantum Control, Oracle, and many others. A custom API connector handles any REST or SFTP-based integration not on the standard list.

Trakt supports fleets from Boeing, Airbus, Bombardier, Embraer and military jets. Case studies include deployments on A320-family and Boeing 737NG aircraft.

Typical deployments go live in 4 to 12 weeks with no infrastructure changes required on your side. Trakt handles the data pipeline setup and model calibration during that period.

Yes. Once an integration is configured, Trakt can run scheduled automatic syncs that pull aircraft records, component data, maintenance history, and work orders from your MRO system on a recurring basis. Each sync is logged with detailed results including records created, updated, skipped, and any errors encountered. If enough new data arrives during a sync to meet a model's training threshold, retraining is triggered automatically in the background.

Yes. Trakt provides a comprehensive REST API (v2.0) for programmatic access to predictions, fleet planning scenarios, asset configurations, maintenance programs, component-level health data, sensor data submission, fleet exports, and integration management. Authentication uses bearer token keys passed via an Authorization header (Bearer <token>) or as a query parameter. Three rate-limit tiers are available: Read Only (100 requests/day, 1,000/month, up to 50 predictions per request), Read/Write (500/day, 5,000/month, up to 100 predictions per request with export capability), and Full Access (2,000/day, 20,000/month, up to 500 predictions per request with access to all endpoints). Tokens are time-limited, support optional IP whitelisting, and can be scoped to a specific company for multi-tenant isolation.

The API is organized around several resource groups. The Configuration endpoint returns all supported regions, currencies, week-end conventions, and maintenance interval types. Planning Scenarios endpoints let you create and retrieve fleet planning scenarios with SLA targets, duration settings, currency, and region-specific configuration. Asset Configurations endpoints return aircraft type profiles (narrowbody, widebody, regional jet, turboprop, business jet, military transport, helicopter) with their associated maintenance programs. The Maintenance Programs endpoint provides task-level definitions with interval type, cost estimate, duration, and required certifications. The Aircraft Extended and Components Extended endpoints deliver full prediction outputs per aircraft and component. The Fleet Export endpoint packages everything into a single response. Full documentation is available at the API Documentation page.

Yes. The API's configuration endpoint exposes six regions: North America (FAA), Europe (EASA), Middle East (GCAA/GACA), Asia Pacific (CAAS/CASA), Africa, and Latin America (ANAC). Each region carries a default currency, week-end convention (for example, Friday in the Middle East versus Sunday elsewhere), timezone, and regulatory authority reference. The API supports fifteen currencies, and every scenario and export response includes the applicable region configuration.

The Components Extended endpoint returns the current health score, predicted remaining useful life (in both hours and days), failure probability, prediction confidence, and a condition indicator. Anomaly detection fields report whether an anomaly has been flagged along with the anomaly score. Survival analysis data includes the hazard rate, survival function, and remaining useful life distribution. Each component also carries a priority classification (critical, high, medium, or low), a recommended action, an action deadline, estimated unit cost with min/max range, lead time in days, part number, serial number, and manufacturer.

Yes. You can create and retrieve Planning Scenarios that define a time window, assign aircraft, set SLA targets (aircraft availability, dispatch reliability, on-time performance), and specify currency and week-end conventions for the region. Asset Configurations provide aircraft type profiles each with engine specifications, typical daily utilization, and a linked set of maintenance programs. Maintenance Programs are defined at the task level with interval type (calendar, flight hours, flight cycles, combined, or condition-based), interval value, expected duration, estimated cost, and required certifications.

API tokens are generated by your company administrator through Trakt's admin panel. Each token is a URL-safe string that is hashed before storage, so the plain-text token is shown only once at creation and cannot be retrieved later. Tokens carry a name, email, organization, access level, and an expiration date (configurable, default 30 days). Administrators can revoke, extend, or adjust the rate limits of any token at any time. Usage is tracked per token with daily and monthly counters that reset automatically. Tokens can also be scoped to a specific company so that API consumers only see data belonging to their organization.

Yes. The API returns cost and lead time estimates for every component type across eleven categories including propulsion, airframe, landing gear, flight controls, avionics, hydraulics, electrical, fuel, environmental control, pneumatic, and cabin systems. Each estimate includes a minimum, maximum, and typical value. These figures are included in the component-level API responses and in the fleet export, which also rolls up an estimated total maintenance cost over the next 30 days for all critical and high-priority components.

Yes. Trakt provides mobile-accessible dashboards so maintenance teams can view component health, predictions, and alerts from any device, anywhere.

Across deployments, Trakt clients have seen maintenance cost reductions of 18 to 22%, a 20 to 34% reduction in unscheduled events, up to 15% improvement in fleet utilization, and up to 40% fewer AOG (aircraft on ground) situations. One cargo operator achieved a shift to 82% scheduled versus 18% unscheduled maintenance.

Clients typically see a return on investment within months.

Yes. Every prediction Trakt generates comes with a plain-language explanation showing which factors contributed most to the result. For example, if a hydraulic pump is flagged, the system will show whether the main driver was rising vibration readings, accumulated flight cycles, time since last overhaul, or a combination of factors. This transparency is important for regulated environments where maintenance teams need to understand and justify decisions.

Yes. Trakt includes a competing failure risks analysis that models multiple potential failure modes for the same component simultaneously. Rather than giving a single pass/fail verdict, the system ranks each failure mode by probability and estimated time to failure, so your maintenance team can address the most likely risk first while being aware of secondary concerns.

Yes. Each user can configure their own email alert preferences by category, including maintenance alerts, anomaly alerts, AOG (aircraft on ground) alerts, and parts alerts. There is also a critical-only mode for users who only want to be notified about the most urgent items. Push notifications are supported as well.

Yes. Trakt includes a predictions chatbot that lets users ask questions in plain language, such as "what are the biggest risks in my fleet right now?" or "which aircraft needs attention first?" The chatbot draws on your live fleet data, trained model predictions, and work order status to provide direct answers without needing to navigate dashboards or run reports manually.

Trakt generates comprehensive reports in both PDF and Excel formats. Reports include fleet overviews, ATA chapter breakdowns, critical component summaries, maintenance schedules, and prediction forecasts. These can be shared with stakeholders, auditors, or regulatory bodies as needed.

Trakt supports bulk import of aircraft, components, maintenance records, sensor data, technicians, and work orders via CSV and Excel files. During onboarding the team will help you map your existing data formats, but once set up you can also import data yourself through the platform at any time.

Trakt is a multi-tenant platform with role-based access control. Each company's data is fully isolated, so users only see their own fleet. Within a company, permissions are scoped by role: company administrators can manage users, configure integrations, and access all data, while standard users see the dashboards and alerts relevant to their function. Platform-level administrators at Trakt have a separate role for system-wide support.

Yes. Trakt includes parts inventory tracking with stock levels, minimum and maximum thresholds, supplier management, and purchase order workflows. The Parts Forecasting AI model ties directly into this, predicting what parts you will need and when based on component health trends across your fleet. The goal is to reduce AOG situations caused by parts shortages while avoiding overstocking.

Yes. Trakt includes a work scheduling engine that takes the AI predictions and generates task assignments matched to available technicians. The system considers each technician's certification level, current workload, and the priority and complexity of each task. Assignments include estimated duration, a confidence score for the match, and a recommended schedule date. You can review and adjust assignments before they are finalized.

Yes. Trakt is configured for six operating regions: North America (FAA), Europe (EASA), Middle East (GCAA/GACA), Asia Pacific (CAAS/CASA), Africa, and Latin America (ANAC). Each region carries a default currency, weekend convention (for example, Friday in the Middle East versus Sunday in the Americas), timezone, and regulatory authority reference. The platform supports fifteen currencies and adjusts scheduling and reporting to match your region's working calendar.

No. Trakt operates as a ground-based decision-support tool. It does not embed in onboard avionics or directly control any aircraft system, so it falls outside the scope of DO-178C airborne software certification. The FAA has recognized condition-based maintenance platforms of this kind through guidance such as FAA Notice No. 8900.634, which treats CBM software as an operational tool requiring integration into the air carrier's maintenance program rather than airborne software certification.

Trakt is a human-in-the-loop system. It provides predictions, risk rankings, and maintenance recommendations, but all final decisions remain with your certificated maintenance personnel. Under 14 CFR Part 43, only authorized persons (A&P mechanics, Part 145 repair stations, or air carriers with appropriate operating certificates) can approve maintenance actions and sign off on return to service. Trakt assists these professionals with prioritization and planning but never replaces their authority or judgment.

Yes. Trakt logs all significant user and system activity, including logins, data imports, prediction runs, report generation, and configuration changes. This audit trail supports compliance reviews and provides full traceability for when and why actions were taken within the platform.

You can schedule a demo through the Trakt website or contact the sales team directly. After an initial consultation, the onboarding team will connect to your data sources, calibrate the AI models for your fleet, and have you live within 4 to 12 weeks.

Trakt typically begins with a scoped paid pilot covering a portion of your fleet so you can validate results before a full rollout. Contact the team at Kquika.com to discuss options.