Applicare ingests every log line, links it to the trace, deploy, and entity that produced it, and lets you ask plain-English questions. ArcIn answers. IntelliTrace explains the cause. IntelliTune drafts the fix. No SPL. No KQL. No war rooms.
Log management is how engineering teams collect, search, and reason about the messages applications and infrastructure emit. Done well, it answers the question every incident starts with: “What just happened, and why?” Done poorly, it produces a dashboard nobody trusts and a bill nobody understands. Applicare treats logs as one signal in a causal graph — linked to the trace that emitted them, the deploy that introduced them, and the IntelliTrace pattern that explains them.
A log line on its own is a fact, not a diagnosis. The number of users impacted, the deploy that broke it, the host it fired from, the upstream service that triggered it — those are the questions that resolve the incident. Applicare answers them automatically.
Ingest the whole story. The pipeline accepts enterprise log volume without dropping lines or forcing pre-aggregation. The line you need to debug is always there.
Linked, not loose. Every log line carries the trace ID, service, host, deploy, and commit it came from. The grep-and-stare investigation is gone — one click goes from log to cause.
Accessible to every engineer. ArcIn understands plain-English questions — new SREs and seasoned platform engineers query the same way. SPL and KQL fluency stop being a hiring filter.
Error rate on checkout-svc spikes +340% in 60 seconds. IntelliSense flags the regression against the per-entity baseline. No threshold rule, no engineer awake at 2 AM.
The on-call engineer asks ArcIn in plain English: “errors in checkout-svc last hour.” ArcIn returns: 4,240 errors · NullPointerException in OrderRepository line 142 · first seen 47 min ago. No SPL. No KQL.
Every error log carries its trace ID. One click pivots to IntelliTrace, which surfaces the matching span and the responsible deploy: deploy #6205 · commit 4a7f9d2 · 47 minutes ago.
ArcIn explains: “A null-check was removed from OrderRepository.findById() in commit 4a7f9d2. The same regression caused incident INC-3140 last month.”
IntelliTune drafts the fix — restore the null-check — and opens PR #4831. The deploy is auto-rolled back behind your policy gates while the PR awaits review. Zero pages fired. Zero customers churned.
NullPointerException at line 142 a hundred times — see it once, with the count and the trend.| Legacy log platform | Self-hosted ELK | Applicare | |
|---|---|---|---|
| Query interface | SPL / KQL fluency | Lucene / KQL | ArcIn plain English |
| Trace correlation | Manual trace ID search | Manual, if instrumented | Automatic, every log |
| Anomaly detection | Threshold rules to maintain | Bring your own ML | IntelliSense behavioral baselines |
| Pattern recognition | Manual fingerprinting | Manual | Auto-grouped by similarity |
| Root cause | Engineer’s investigation | Engineer’s investigation | IntelliTrace causal inference |
| Remediation | Page someone | Page someone | IntelliTune drafts the fix |
| Storage | Vendor-locked | You operate it | Managed — or export to your warehouse |
No. Applicare accepts logs from whatever you already emit — structured JSON, plain text, syslog, custom formats. Bring your existing logging library and pipeline as-is.
Yes. Fluent Bit, Fluentd, Vector, Filebeat, OTLP Logs, syslog, and custom HTTP intakes are all supported. Point them at Applicare and data flows immediately — no proprietary agent, no parallel pipeline.
Default rules redact credit card numbers, passwords, API tokens, and common PII patterns at the pipeline — before logs hit storage. You can extend redaction per field, per source, or per environment.
No. ArcIn understands plain-English questions. If you prefer a query language, a visual query builder and a SQL-like interface are available — but neither is required to investigate an incident.
Yes. Raw logs export to S3, Snowflake, BigQuery, and Databricks. ArcIn queries data natively where you store it — long-term analytics and retention live on your data platform, on your schema.
Retention is configurable per source and per environment. Hot, warm, and cold tiers with tail-based filters let you keep the logs you query and archive the rest — predictable cost without losing the line you need.