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:

1. Paying premium observability prices for noisy error triage
2. “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.

• 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:

1. Ingest: app logs arrive via API
2. Fingerprint: normalize + hash error signatures
3. Deduplicate: group repeated errors by fingerprint window
4. Analyze once: one LLM call per unique fingerprint
5. 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

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)
}

Early access is open — join the waitlist at Logsense