logcat.ai provides an AI-powered investigation engine for operating system and device software engineering. The platform autonomously diagnoses root causes across diverse log formats, including Android bugreports, kernel dmesg, and CAN bus traces, with every finding cited to source signals. It offers three investigation scopes—Quick, Deep Research, and Delta—to handle single questions, in-depth analysis of one capture, or cross-artifact correlation across builds and devices.
Funding
Funding not disclosed
Founders
Product
Problem
Device and OS-level engineering relies on manual log correlation across kernel, HAL, framework, and modem layers, making root cause analysis slow and error-prone. Single-layer tools cannot see cross-subsystem failures, and each bug fix can take three to six weeks of triage, patching, and regression testing, consuming 40–50% of device engineers' time.
Solution
logcat.ai delivers an AI engineering layer for the OS stack that autonomously investigates failures across any signal a device emits, from Android OS to Yocto Linux. The platform's Delta engine parses native formats like bugreports, logcat, dmesg, and CAN traces, then correlates timestamps across subsystems to trace causal chains. It routes tasks to frontier models with million-token context windows for whole-bugreport reasoning, while smaller models handle parsing and classification. Every finding includes line-level citations, failed hypotheses are surfaced transparently, and human approval gates ensure no AI-proposed change is applied without engineer review.
Target Audience
Primary customers are device engineering teams at OEMs, automotive suppliers, and embedded systems companies who need to diagnose complex OS-level bugs across Android, Linux, and real-time operating systems, as well as engineering managers overseeing certification cycles and release validation.
Features
- Three investigation scopes: Quick for single cited answers, Deep Research for end-to-end multi-step analysis of one capture, and Delta for correlating across multiple logs, builds, or devices
- Native parsers for bugreports, logcat, dmesg, journalctl, CAN traces, kernel oops, U-Boot output, and modem signaling, with format-aware structure rather than text-based handling
- Cross-layer correlation engine that aligns timestamps across kernel, HAL, framework, modem, and bus layers to trace cause-effect chains
- Task-routed model selection using long-context frontier models for investigation and smaller faster models for parsing and classification
- Mandatory human-approval gates where the model never commits; engineers hold the merge button for any AI-proposed remediation
- Data security with AES-256 encryption at rest, TLS 1.3 in transit, 90-day default retention, and contractual guarantees that customer logs are never used to train AI models