Debug Your Entire Fleet, Not One Device at a Time
AI that understands fleet behavior across devices, firmware versions, and time. From crash aggregation to proactive anomaly detection to OTA regression analysis — logcat.ai turns scattered device logs into fleet-wide intelligence.
See It In Action
A real-world fleet debugging scenario: devices rebooting after exactly 7 days of uptime
Field reports show devices in a smart building deployment rebooting after roughly 7 days of uptime. No crash logs retained on device.
A single bugreport is uploaded from an affected device. logcat.ai parses kernel logs, logcat, and memory diagnostics automatically.
AI detects a vendor connectivity service leaking ~2MB/day. At 7 days, the 16MB low-memory threshold triggers the OOM killer.
Deep Research traces the leak to a socket reconnection handler that allocates buffers without releasing them on timeout. Fix: bounded retry with cleanup.
Weeks of pulling individual devices, reproducing the issue, attaching debuggers, and correlating logs across multiple teams.
Pattern detected on first upload. Root cause confirmed with Deep Research in under 5 minutes. Fix verified with Delta comparison.
Fleet Intelligence
Stop debugging one device at a time. Aggregate crash patterns, thermal trends, and connectivity health across your entire deployed fleet to see the signals that individual device logs hide.
Crash Aggregation
Automatically group kernel panics, watchdog resets, and application crashes across your fleet. Identify the top failure modes by frequency, device type, and firmware version.
Thermal Trending
Track thermal zone behavior across deployments. Detect devices running hotter than expected, identify thermal throttling patterns, and correlate with workload or environmental factors.
Connectivity Patterns
Monitor cellular, WiFi, BLE, and LoRa connectivity across your fleet. Identify devices with degraded signal quality, frequent disconnections, or modem firmware issues.
Watchdog Correlation
Correlate watchdog reset events with preceding system state — memory pressure, thermal events, driver failures, and resource exhaustion — to find the actual root cause.
Proactive Detection
Detect gradual degradation patterns — thermal creep, slow memory leaks, connectivity erosion — before they cascade into fleet-wide outages.
Thermal Creep Detection
Identify devices whose operating temperatures are gradually increasing over time — often a sign of degrading thermal paste, blocked ventilation, or firmware-induced power management regressions.
Memory Leak Trend Analysis
Track memory consumption patterns across firmware versions. Detect slow memory leaks that only manifest after days or weeks of uptime, before they cause OOM kills and service restarts.
Connectivity Degradation
Monitor signal quality trends and connection stability metrics over time. Get early warning when devices in specific regions or hardware batches start showing degraded connectivity.
Regression & Validation
Know exactly what changed after every OTA update and hardware revision. Compare firmware versions, validate BSPs, and qualify new hardware with evidence, not guesswork.
Pre/Post Update Comparison
Upload logs from before and after OTA updates. Delta automatically identifies new kernel warnings, changed driver behavior, and performance regressions introduced by the update.
Automatic Regression Flagging
AI identifies statistically significant changes in crash rates, thermal behavior, memory usage, and boot times between firmware versions across your fleet.
Rollback Decision Support
Get clear evidence for rollback decisions. See exactly which issues were introduced, their severity, and how many devices are affected before deciding to proceed or revert.
BSP Validation
Analyze kernel boot logs, driver probe sequences, and device tree bindings to verify BSP configuration. Catch misconfigured clocks, missing regulators, and incorrect pin muxing.
Driver Verification
Verify that all drivers probe successfully, bind to correct devices, and operate without errors. Detect intermittent failures, deferred probes, and resource conflicts.
Peripheral Health Monitoring
Monitor I2C, SPI, UART, and GPIO subsystems for communication errors, timeout patterns, and device disconnections that indicate hardware or signal integrity issues.
Fleet Debugging, Before and After
Without logcat.ai
- Debug one device at a time — no visibility into fleet-wide patterns
- Reproduce issues manually in the lab, often unsuccessfully
- OTA regressions discovered weeks later through customer complaints
- Memory leaks and thermal creep invisible until devices fail in the field
- BSP validation is a manual checklist, not an automated analysis
- Root cause investigations take days or weeks across multiple teams
With logcat.ai
- Fleet-wide crash aggregation surfaces the top failure modes instantly
- Upload a bugreport and get root cause analysis in minutes
- Delta comparison catches OTA regressions before rollout
- Proactive detection flags memory leaks and thermal trends early
- Automated BSP and driver validation against kernel boot logs
- Deep Research traces issues through the full stack autonomously
Part of the logcat.ai Platform
Fleet intelligence is built on the same AI platform that powers bugreport analysis, interactive debugging, and cross-file comparison. Every tool works together.
Autonomous Investigation for Fleet Issues
Deep Research traces kernel panics and fleet-wide failures through the full embedded stack automatically
Fleet-Wide Investigation
Multi-step analysis across kernel, driver, and application layers
Cross-layer correlation complete
Fleet-Wide Comparison with Delta
Upload logs from multiple devices or firmware versions. Delta identifies regressions, hardware-specific failures, and common root causes across your fleet.
Delta Correlation Engine
Multi-file cross-layer analysis
What We Analyze
Upload logs from any embedded Linux device
Linux kernel logs from any embedded board — device tree issues, driver probes, thermal events, memory pressure, and kernel panics
System daemon logs from embedded Linux — service failures, network events, watchdog resets, and application crashes
Application-level logs from embedded Linux — stdout/stderr from containerized services, custom application logging, and Android logcat for AOSP-based devices
Full system diagnostic packages — Android bugreports for AOSP-based devices, custom diagnostic bundles, and combined log archives from any Linux system
Who Uses This
Engineers building and maintaining embedded Linux products at scale
Fleet Operations Engineers
Monitor fleet health, diagnose issues across thousands of deployed devices remotely, and identify fleet-wide regressions before they escalate to outages.
Hardware & BSP Engineers
Validate BSP configurations, debug driver probe failures, and qualify new hardware revisions with systematic kernel log analysis.
Firmware Engineers
Debug kernel panics, memory leaks, and power management issues on custom boards running Yocto, Buildroot, or custom Linux distributions.
QA & Release Engineers
Validate OTA firmware updates, compare pre/post-update logs with Delta, and gate releases on regression-free evidence.
Regulatory & Compliance
Generate structured analysis reports for regulatory submissions. Maintain complete audit trails from raw telemetry to root cause findings.
Connectivity & Protocol Analysis
Debug communication stacks across all major IoT protocols
Cellular (LTE/5G/NB-IoT)
Analyze modem AT command sequences, registration failures, signal quality degradation, and data session drops across cellular-connected IoT devices.
WiFi
Debug association failures, roaming issues, DHCP problems, and throughput degradation. Correlate WiFi events with application-layer connectivity issues.
BLE (Bluetooth Low Energy)
Analyze BLE connection events, GATT service discovery, pairing failures, and advertisement patterns. Debug devices that lose BLE connections under specific conditions.
LoRa / Zigbee / Thread
Debug mesh networking issues, gateway communication failures, and protocol-specific timing problems in low-power wide-area network deployments.
Edge AI & Inference Debugging
Debug ML workloads running on edge devices
NPU Crash Analysis
Analyze crashes and errors from neural processing units. Correlate NPU firmware errors with kernel events to identify driver bugs, memory allocation failures, and hardware faults.
Inference Pipeline Hangs
Debug ML inference pipelines that hang or timeout. Trace the issue through the full stack — from application framework to runtime to hardware accelerator.
Thermal Issues Under ML Workload
Monitor thermal behavior during sustained inference workloads. Identify when thermal throttling degrades model performance and correlate with cooling system effectiveness.
Built for Regulated Industries
Compliance-ready analysis for medical, industrial, and safety-critical IoT deployments
FDA 21 CFR Part 11 Ready
Structured analysis outputs suitable for regulatory submissions. Complete traceability from raw logs to root cause findings with timestamped investigation steps.
IEC 62304 Software Lifecycle
Support software maintenance processes with systematic defect analysis. Document investigation methodology and root cause evidence for audit requirements.
Complete Audit Trails
Every investigation step is logged with timestamps, queries, and findings. Export complete analysis history for regulatory audits and quality management systems.
Frequently Asked Questions
Common questions about IoT fleet intelligence and embedded analysis
logcat.ai scales to fleets of any size. Upload logs from individual devices or bulk-upload from thousands of units. The platform aggregates findings across your entire fleet to surface common failure patterns and regressions.
Upload logs collected before and after an OTA update. Delta compares them automatically, identifying new kernel warnings, changed driver behavior, performance regressions, and crash patterns introduced by the firmware update. Works with any number of devices.
Proactive detection identifies gradual degradation patterns — thermal creep, slow memory leaks, connectivity quality degradation, and storage wear — before they cause failures. It works by analyzing trends across multiple log uploads over time.
Yes. logcat.ai provides structured analysis outputs with complete investigation audit trails. Every step is timestamped and documented, making it suitable for FDA 21 CFR Part 11, IEC 62304, and other regulatory frameworks. Enterprise customers can deploy on-premises for additional data control.
Upload kernel boot logs and runtime logs from your target hardware. logcat.ai analyzes driver probe sequences, device tree bindings, peripheral communication patterns, and power management behavior to identify configuration issues, hardware faults, and compatibility problems.
logcat.ai supports kernel logs (dmesg) and system logs (syslog/journald) from any Linux distribution — including Yocto, Buildroot, OpenWrt, Ubuntu Core, Debian, Alpine, and custom distributions. For Android-based IoT devices, we support full bugreport analysis.
Yes. We analyze cellular (LTE/5G/NB-IoT), WiFi, BLE, LoRa, and Zigbee connectivity from kernel and application logs. The platform correlates connectivity events with system state to identify root causes of connection drops, signal degradation, and protocol failures.
Yes. logcat.ai can analyze NPU driver errors, inference pipeline timeouts, and thermal behavior under ML workloads. We correlate hardware accelerator events with kernel and application logs to trace issues through the full inference stack.
Fleet Intelligence Starts Here
See how logcat.ai transforms fleet telemetry into actionable intelligence. From a single device bugreport to fleet-wide proactive detection — in minutes, not weeks.