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Platform

Advanced triage. Advanced troubleshooting. One platform.

One pipeline for every vendor in your infrastructure, and every answer carries the evidence that produced it.

Book a free demo See the capabilities
Network overview
PulsarOps AI network overview dashboard showing device health, interface error trends and correlated events across multi-vendor infrastructure.
  • Multi-vendor streaming ingestion
  • Multi-device correlation
  • Agentic RAG assistant
  • ML log classification
  • Early-warning trends
  • Root cause and next steps
A day in the life

Network engineer: the port-flapping morning.

Four steps on one console, with the correlation already done.

The shift starts and nothing is down. No page overnight, every dashboard on the wall green, and a port flapping since the small hours.

It is not an outage yet. It is a rising count of interface errors on one link, buried in millions of syslogs from every vendor in the infrastructure. Monitoring tells you something broke. It does not tell you why.

  1. 1 Early warning

    Heat map flags the interface errors

    • Degradation surfaces before it becomes an outage

    Nothing has breached a threshold yet

  2. 2 Drill down

    Down to the interface level

    • Errors and discards charted per interface over time

    One screen, no query language

  3. 3 Correlate

    Across interfaces and devices

    • Similar events grouped instead of listed one by one

    Pattern-based, ML-assisted

  4. 4 Act

    The assistant recommends next steps

    • A recommended action attached to the finding

    Human-centric, evidence-backed

Watch it happen on the real screens

Capabilities

Everything triage needs, in one platform.

Six capabilities on one pipeline, which is why an answer carries its evidence.

Raw vendor output One schema

  • Core routerCISCOlink state down
  • Aggregation AJUNIPERinterface flap
  • Load balancerF5pool member down
  • Edge firewallPALO ALTOsession table full
  • Access ANOKIAuplink error rate

Five vendor formats, one aligned schema.

Multi-vendor streaming ingestion

Syslogs, application logs, configs, inventory, tickets and topology, in real time, from every vendor.

Multi-device correlation

Events connected across interfaces and devices, so one incident reads as one incident.

Agentic RAG assistant

Ask in plain language. The retrieved logs, configs and topology come with the answer.

ML log classification

Millions of events sorted by type and frequency, so the shape of the problem shows first.

Early-warning trends

Degradation is visible while it is still a trend, before the ticket opens.

Root cause and next steps

Every finding comes with a recommended action. Root cause is built in, not an add-on.

How it works

One platform. Three stages.

Data arrives, the analysis happens in the same place, and the insight leaves with its evidence.

  1. 01 Ingest @ scale Data as it arrives
  2. 02 Analyze @ scale Analysis and evidence
  3. 03 Insights @ scale Insights and remediation
Evidence

One payload carried through all three stages.

  1. 01 Ingest @ scale

    • Syslogs and application logs
    • Configs and inventory
    • Tickets and topology

    Real-time and historical, multi-vendor

  2. 02 Analyze @ scale

    • Latest LLMs, agentic RAG
    • ML-based classification and anomaly detection
    • Pattern-based correlation

    Millions of events, minutes not hours

  3. 03 Insights @ scale

    • Early warning trends
    • Root cause analysis
    • Recommended next steps

    Human-centric, evidence-backed

The same three stages, on one page.

It names what enters each stage and what does the work.

Download the brief
The three platform stages, ingest, analyze and insights, with the components behind each one
Key technical benefits

What the engineering team actually gets.

Written for the people who will run it.

01

Live troubleshooting almost immediately

Millions of real-time syslogs go in and troubleshooting starts, with no modeling exercise first.

02

Hours of troubleshooting saved

Error logs are parsed into dashboards and the assistant, so the reading stops being a human job.

03

An early warning system

Trends and error classification make degradation visible while there is still time to act.

04

Many engineers at once

Engineers and sysadmins work in it at the same time, which matters on a bridge call.

05

Faster operations reporting

The dashboards speed up the operations reporting your leads write by hand today.

06

Correlate issues within minutes

ML picks out the related error logs.

Incident recovery
50-75%
faster incident recovery
Return on investment
228%
annualized ROI
Payback
3.7 mo
payback period
Cost efficiency
2-4x
more cost-efficient over three years

Three-year estimate: five-person NetOps team, ~125 incidents/yr, triage cut from 8 hours to 1.

See it working

The screens an engineer actually works in.

Key feature 01

AI-powered troubleshooting and diagnostics

  • Collect data in real time through an adaptive ingest pipeline and device library.
  • Find root causes with ML built for network telemetry.
Network overview
Network overview dashboard listing devices, interface error counts and correlated event groups.
Key feature 02

Real-time trends and insight visualization

  • See conditions across devices before issues become outages.
  • Recognize evolving patterns in network logs.

Step two of the port-flapping morning.

Interface analysis
Interface analysis view charting error and discard trends per interface over time.
Key feature 03

An assistant that advises the network engineer

  • Query event logs, configurations and topology.
  • Get recommended next steps in minutes, with the logs behind them.
AI assistant
AI assistant panel answering a network health question with a written summary and supporting log evidence.
Live analysis

Watch a question turn into an answer.

The events get connected and the cause gets named, with the evidence.

linked by topology

Scattered events snap into one related cluster.

root cause Core router
Aggregation A
Aggregation B
Edge firewall
Access A
Access B
Load balancer
Server A
alerting root cause

The fault spreads down. The cause stays upstream.

  1. Core router
  2. Aggregation A
  3. Edge firewall
  4. Load balancer
  5. Access A

fault on hop 3

The fault sits on one hop.

Live analysis
Recorded from the platform. No narration.
Deployment

Secure and self-contained. Only your data sources.

Regulated operators do not get to send network telemetry to someone else's tenant. So we never ask them to.

Read the security detail

On-premise

Runs inside your perimeter. Nothing leaves your network.

Sovereign-ready

In-country deployment for government and regulated industries.

Cloud-agnostic

Your VPC on AWS, Azure or GCP. Your keys, your controls.

Works with what you run

  • Cisco
  • Juniper
  • F5
  • Palo Alto
  • Fortinet
  • Nokia
  • Ericsson
  • Linux
  • Windows
  • MySQL
  • Apache

Multi-platform, one adaptive ingest pipeline.

Comparison

How we compare.

Categories, not vendor names.

PulsarOps AI compared with legacy log analytics, monitoring suites and cloud-only AIOps
Capability PulsarOps AI Legacy log analytics Monitoring suites Cloud-only AIOps
Pricing model Flat Ingest-based Per-device Per-GB
Root cause Built-in Add-on No Partial
On-prem Yes Partial Yes No
Data sovereignty Yes Partial Partial No
AI assistant Yes No No Partial

Scroll the table sideways on a narrow screen.

See the full comparison See who this is built for
Explainer

Take a glimpse of PulsarOps AI.

pulsarops.ai / platform overview

More briefs and case material

Book a demo

See it run on your own data.

Thirty minutes with an engineer. Bring a real port-flapping morning.

Book a free demo sales@pulsarops.ai