Telemetry Intelligence

Observability Tells You What. logcat.ai Tells You Why.

Your dashboards show metrics. Your alerts fire. But nobody explains why battery drain spiked after the last OTA, why reboots cluster on one SKU, or why connectivity fails in the same sequence across firmware versions. That's what logcat.ai does.

The Telemetry Gap

You collect everything. You understand almost nothing.

Your telemetry infrastructure works. App events, crash reports, device health metrics, connectivity logs — all flowing into storage. The pipeline investment was significant. It's running.

But what happens next? Dashboards show you trends. Alerts tell you something is wrong. Search helps when you already know what to look for. None of them answer the question that actually matters: why is this happening?

The result: 90%+ of the most direct signal about how your product behaves in the real world sits in cold storage. The crashes get counted. The root causes stay hidden.

Why Building In-House Fails

Three paths companies try — all leading to the same dead end

Path 1: Scripts & Notebooks

Someone writes parsers, builds Grafana dashboards, maintains Jupyter notebooks. Works for one version, one format. Breaks every release. Becomes a second product nobody signed up to maintain.

Path 2: Naive AI

Dump telemetry into an LLM. Fails because logs aren't English prose — they're massive, structurally complex, semantically domain-specific. Context limits, hallucinations, absurd inference costs.

Path 3: Internal Platform

The "do it right" version. Multi-quarter, multi-engineer investment competing with core product roadmap. Solves one domain, breaks on the next. Constant upkeep, never complete.

All three paths lead to sunk cost that never fully delivers.

From Observability to Understanding

The missing piece isn't more data — it's intelligence that explains the data you already have

What You Have: Observability

-Dashboards show trends and anomalies
-Alerts fire when thresholds are crossed
-Search helps when you know what to look for
-A human must ask every question and correlate every event

What You Need: Telemetry Intelligence

+Autonomous investigation that traces root causes across layers
+Causal reasoning that explains why metrics changed
+Regression detection across firmware versions and device populations
+Reports with evidence and recommendations, not just data
+Scales with your fleet, not your headcount

logcat.ai: The Intelligence Layer

Sits on top of your existing telemetry infrastructure — not a rip-and-replace

Deep Research

The autonomous investigation engine. Traces root causes across kernel, framework, and application layers. Ask why — get answers with evidence.

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Delta

The comparison engine. Cross-version, cross-device, cross-layer comparison. Find what changed between the build that worked and the build that didn't.

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Your Infrastructure

Works on top of your existing telemetry infrastructure — Elasticsearch, S3, Datadog exports, Kafka pipelines, MDM-collected diagnostics, and internal storage systems. No rip-and-replace. No second telemetry stack to maintain.

Fits into your existing telemetry workflow

logcat.ai is designed to work on top of the telemetry systems teams already use.

Connect existing sources such as:

-Elasticsearch / ELK
-S3 or cloud object storage
-Datadog and observability exports
-Kafka or internal streaming pipelines
-MDM-collected diagnostics, bugreports, and field logs

Use logcat.ai in two modes:

+Continuous telemetry intelligence for production systems and fleets
-Ad-hoc investigation for one-off bugreports or diagnostic bundles

What This Looks Like in Practice

A device manufacturer sees reboot rates spike 3x after an OTA update

1
Dashboards show reboot rates rising after firmware v3.2.1 across one device population.
2
The team still does not know which subsystem changed, whether the issue is isolated to a SKU or rollout cohort, or what sequence of failures is causing the reboot.
3
logcat.ai ingests the logs and telemetry already flowing through the organization’s stack — such as Elasticsearch, S3, Datadog, Kafka, or MDM-collected diagnostics — without requiring a new pipeline or manual upload workflow.
4
Deep Research analyzes affected devices and traces the causal chain automatically: system_server crash → thermal service instability → new thermal thresholds in v3.2.1 → aggressive CPU throttling → watchdog-triggered reboot.
5
Delta compares telemetry across v3.2.0 and v3.2.1, across affected and unaffected device populations, and isolates the changed thermal policy as the most likely regression source.
✓
Result: root cause identified in minutes using the telemetry the team already collects, without manual correlation across dashboards, logs, and release artifacts.

One Intelligence Layer, Every Device Category

logcat.ai understands the full stack across every vertical you ship

Turn Your Telemetry Into Explanations

Run logcat.ai on the telemetry you already collect. Surface the root causes your dashboards, alerts, and search workflows cannot explain.

Works with existing telemetry infrastructure — no rip-and-replace required.