(v 1.0 - last updated: Feb 9 2025, 23.00 CET)

IT is becoming more complex, more dynamic, more integrated, more real time with vulnerability for organizations increasing. IT teams tend to work in silos with silo’d tools further increasing the challenge. Additionally, business / application personas are increasingly responsible for IT driving a requirement for better visibility into business performance.

We believe the solution to many of these problems is to automate as much as possible of core scalability problems:

• data harvesting
• data analysis
• reduce human involvement in configuring alerts

We believe this empowers organizations to cover more data, maybe 100 - 1000x more data than would be possible with more manual tools and still get better, earlier actionable alerts with precision. With precision we mean few false positives / negatives - consequently efficiency.

In our perspective traditional monitoring tools are either:

• Too noisy—endless false alerts, making sure you ignore the one that actually matters.
• Too manual—forcing IT teams to sift through logs like they’re panning for gold.
• Too expensive—hello, Datadog, thanks for the invoice 💸💸 and dashboards that look good but still don’t tell me what’s actually wrong.

The result? IT teams are overworked, stuck in reactive mode, and drowning in tools that don’t play nice together.

The Solution:

AIOps - Artificial Intelligence in IT Operations - flips the script. Instead of waiting for something to break, it automates the heavy lifting—detecting issues early, cutting down noise, and giving you real insights instead of just another pile of data.

With AIOps, you get:

✅ Less noise, more signal – Only the alerts that actually matter.

✅ Proactive detection – Early warnings before things go south.

✅ Smarter insights – Cross-system correlations, so you don’t have to play detective.