Advanced analysis and forecasting of events on your infrastructure

Where it will be useful

you have a log of failures or tickets going back at least six months, but no confidence that events are analysed systematically.

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How it works

Three steps: the log file -> analysis by statistical models and AI -> a link with the results.

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What the module finds

More than 20 methods that find and group your main problems, their direct and indirect sources and links, and build forecasts.

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Security

No access to your infrastructure. Isolated processing, automatic masking.

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Audit levels

The basic run is free, at the start. After that it depends on the analysis result, your stage in monitoring and automation, and what you want.

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Run the analysis

Send an incident export - take a free basic analysis, made with the key statistical models.

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It will be useful if

Networks and sites

You are a provider, a hosting company, a data centre or a traffic exchange point

Our project grew out of 20+ years of hands-on practice plus the core ITIL practices. You get a new, useful result and a second opinion.

IT operations

You run network outsourcing or an MSP, an IT department with a service desk, and the like

Even the first run of our engine shows the main problem groups behind 80% of downtime, and the ways to fix them.

Outside IT

You look after power grids and utilities, water supply, transport, production and so on

You keep a record of failures (time, object, event). That is enough for our project to be useful to you.

How it works

1

Export your logs

Any format works - whatever you already export from monitoring.

CSVJSONTXTXLSX
2

Send the file

Through the form on this page with your email, or straight to the Telegram bot. No signup, no install, no access to your infrastructure.

3

Get a link with the results

From 10 minutes to 48 hours, depending on the data: findings, charts, an action plan.

Sample report

A real audit run on real data

Not a mockup. It shows what your own report would look like.

What our statistical module looks for

Eight core methods out of 20+ in the OpsLab engine. The full list also covers correlations, trend diagnostics, anomaly detection and a per-service breakdown.

Priority
Priority (Pareto ranking)
Which 20% of incidents cause 80% of the downtime, so you fix the few that matter. The Gini coefficient (a measure of inequality: 0 - all incidents equally bad, 1 - one incident caused everything) shows how concentrated the problem is.
3 of 90 incidents = 41% of all downtime. Gini = 0.67.
Root cause
Root causes (clustering)
Groups incidents by behaviour - time of day, duration, frequency - instead of by ticket category. A long list of "different" errors often turns out to be a small number of root causes.
90 incidents -> 2 clusters: "night, short" and "morning, long". Two causes, two teams.
Cycles
Cycles (periodicity)
Finds hidden repeat cycles in uneven time series - a method from astrophysics applied to incident logs. It also shows in which hours and days failures cluster. It reports FAP (false alarm probability - the chance the cycle is random noise).
Period = 23.1 h, FAP = 4x10⁻⁸ -> almost certainly a scheduler, not chance.
Verification
Before / after a change
Tests whether a deploy or a config change really moved the incident rate, and by how much. Cliff's delta (effect size - how big the shift is, from 0 to 1) turns "it feels worse" into a number.
Cliff's delta = +0.445, p < 0.001 -> the rate grew, confidence above 99.9%.
Forecast
Forecast and tail risk
Estimates how many failures to expect in the coming weeks, and the probability of a rare but very long outage - the one that breaks an SLA. The answer is always a range, never a falsely precise single number.
7 days - about 67 incidents (52-84). Chance of an outage longer than 6 hours - 7%.
Event links
Event links
Finds which events appear together and in what order, so an early alert can sit on the leading signal instead of the crash itself. Lift shows how many times more often a pair occurs than by chance.
database_timeout -> app_crash in 89% of cases, lift = 4.2.
Reliability
MTBF and MTTR without an agent
Mean time between failures (MTBF) and mean time to repair (MTTR) straight from your export - no agent, no access to your servers. Mean, median and P95 (the value only 5% of cases are worse than) together show the bad day, not only the typical one.
MTBF: 2.0 h mean, 0 h median, 24 h P95 - rare long pauses pull the mean up.
Benchmark
Industry norms
Puts your metrics next to the thresholds of your industry. The thresholds are data, not a hard-coded list. Where a threshold is unknown, the engine says so instead of inventing a norm.
P95 response 340 ms against a 200 ms telecom threshold; jitter 12 ms - within the 30 ms norm.
What comes next
Popular fixes after the analysis
Which problems we find most often and what to do about them, in ITIL / ITSM terms.

Run the analysis

CSV, JSON, TXT, XLSX - up to 20 MB.

📂
Drop your incident history file here
CSV · JSON · TXT · XLSX · up to 20 MB

Data can't leave your perimeter? Anonymize the file yourself first (free, offline script)

or
✈️
Telegram bot Send the file right in the chat - no email needed
→
✓ File received. The report will arrive at {email} from {from} in a few minutes. If you do not see it, check your spam folder.
Something went wrong. Please try again or send the file to the Telegram bot.

Security

✓

No access to your infrastructure

No agents, no VPN, no server access. Only the file you decided to send.

✓

Isolated processing

Every file is analysed in its own session. Client data never mixes.

✓

Deleted after 30 days, or sooner on request

Your file and the report page are visible only to you, on the site, for up to 30 days.

✓

No training on your data

Your data is never used to train AI models.

✓

Automatic masking

IP addresses, host names and emails are masked automatically before analysis.

✓

Anonymize before you send it

Run the offline script: it replaces service, host and people names with codes.

Download the script · How to run it

What it costs

The first analysis is free, with no strings attached. After that the price depends on how big your log is and how deep the work goes - we send an invoice by email.

First analysis €0
free, no strings attached
  • Your own data, analysed on a short program
  • A report page at its own link - findings with charts
  • Key numbers: MTBF / MTTR, repeat failures, priorities
  • A list of what your data was missing
Paid analysis from €250
one-off audit or monthly
  • Every method of the engine, not a short program
  • Charts stand next to the finding they prove
  • An action plan and a check against your industry thresholds
  • What to add to your data so the next analysis goes deeper
  • Monthly: the same document plus the change since last month
Ask for an analysis →

The price depends on the size of your log and the depth of the work. Write to us - we name the figure and send an invoice.

Want a conversation, not a document? Your findings read by an operations engineer →