How I Built AI-Powered Log Triage in Go (and Made It 100x Cheaper with Fingerprinting)

Aryan Goyal — Tue, 16 Jun 2026 02:42:28 +0000

I built LogSense to do AI root-cause analysis on production errors without dashboard fatigue or runaway token costs.

Early access is open — join the waitlist at Logsense

I got tired of two things:

Paying premium observability prices for noisy error triage
“AI analysis” tools that still process duplicate stack traces like they’re unique incidents

So I built LogSense.

LogSense = drop in an API key, get AI root-cause analysis on every error. No dashboards. No rules.

The core idea is simple:

Same error 1000 times = 1 LLM call.

That one design choice changed the economics completely.

The Problem With Naive AI Log Analysis

Most pipelines treat every incoming log line as independent work.

If one bug explodes during an outage, you might see thousands of near-identical stack traces.

Naive flow:

ingest logs
call LLM per event (or per tiny batch)
pay repeatedly for the same root cause

That gets expensive fast, and signal quality drops because you’re summarizing noise, not incidents.

The Architecture I Built (Go + Gin + RabbitMQ + K8s)

At a high level:

Ingest: app logs arrive via API
Fingerprint: normalize + hash error signatures
Deduplicate: group repeated errors by fingerprint window
Analyze once: one LLM call per unique fingerprint
Fan out results: attach RCA + remediation hints to all grouped events

This means volume spikes do not linearly increase AI cost.

Fingerprinting: The Cost + Signal Moat

The fingerprinting layer does the heavy lifting.

For each error event, LogSense normalizes unstable fields:

timestamps
UUIDs/request IDs
dynamic numbers/IDs
environment-specific noise

Then it hashes the stable structure (message + stack shape + service context).

So these:

panic: nil pointer at user_id=12345
panic: nil pointer at user_id=67890

collapse into the same canonical signature if they’re the same underlying defect.

Result: one root-cause analysis for one issue, regardless of repetition count.

Why This Matters in Production

During incident windows, repeated errors dominate traffic.

Without dedup, your AI bill scales with chaos.

With dedup, your AI bill scales with unique failures.

That’s the model LogSense is built around:

faster triage
better incident grouping
predictable AI spend

Example Flow (Pseudo-Go)

func process(event LogEvent) {
    normalized := normalize(event)
    fp := fingerprint(normalized)

    if cache.Exists(fp) {
        cache.IncrementCount(fp)
        return
    }

    analysis := llm.Analyze(buildPrompt(normalized))
    cache.Store(fp, analysis)
    publish(analysis)
}