Gen AI
Leading language models, evaluated against real network questions.
Our mission
We built PulsarOps AI to answer the question monitoring can't: WHY
Good monitoring tells you something broke, not what to do about it. the PulsarOps AI event intelligence platform closes that gap for the engineers who run the network.
01
Ingest @ Scale
Data as it arrives
02
Analyze @ Scale
Analysis and evidence
03
Insights @ Scale
Insights and remediation
PulsarOps AIUnified event intelligence
Ingest, analyze, insights. The loop never stops.
- Gen AI
- Data engineering
- ML
- AIOps and SecOps
- US, Canada, UAE
The gap
Monitoring answers what. Nobody was answering why.
The infrastructure we work in are not short of signal. They are short of explanation.
Alerts arrive from every vendor, each true on its own, none agreeing on what happened. The work falls to the most senior person in the room, matching timestamps by hand across consoles.
PulsarOps AI takes in syslogs, application logs, configs, tickets, inventory and topology, correlates them across vendors, and returns a probable root cause with its evidence.
It runs alongside your monitoring and picks up where the alert stops.
Built by MIR Digital Solutions
A decade of data & AI engineering behind every release.
PulsarOps AI comes out of MIR Digital Solutions, a founder-led team rooted in data engineering, ML and enterprise AI.
Visit MIR Digital SolutionsData engineering
Streaming pipelines that keep millions of multi-vendor events a day queryable.
ML
Classification, anomaly detection and correlation trained on real network telemetry.
AIOps and SecOps
Deployments sized for enterprise NOCs and the controls their security teams require.
United States
Enterprise and regulated deployments, on-premise or in your cloud.
Canada
Where the platform started, inside a Tier 1 carrier NOC.
United Arab Emirates
In-country deployment for government and sovereign data centers.
Origin story
It started with an outage nobody could explain fast enough.
It began inside a live network operations team with more log data arriving each day than it could read.
Read the full origin story-
01 The challenge
More log data than any team could read
- A telecom outage in Canada hit payments, internet and mobile data.
- Their senior team could not read the error logs arriving each day.
The gap was not visibility. It was explanation.
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02 What we proposed
A pipeline that ends in an assistant
- An open-source pipeline feeding ML and GenAI.
- Streamed into an assistant engineers could question directly.
Modeled on a day in the life of a senior engineer.
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03 Widening the AI
From reading logs to reasoning over them
- Syslog formats across routers, switches, firewalls and load balancers.
- Anomaly detection and correlation over the raw stream.
The logs already held the answer.
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04 Pilot to production
Rolled forward, then kept going
- After a successful pilot, into that production network.
- Advanced dashboards and early warning insights since.
Scaling to thousands of devices and millions of logs per day.
How we build
Four decisions we do not revisit.
Every release is measured against these.
01
Human-centric, or it does not ship
We modeled a day in the life of the engineers who run the network, then built to that. It augments them, never replaces them.
02
Evidence travels with the answer
Root cause is built in, not a paid add-on. Every finding carries the correlated events and raw logs behind it.
03
Alongside your monitoring, never instead of it
We start at the next question and work back until there is a probable cause worth acting on.
04
Your data never leaves your perimeter
On-premise, private cloud, or a sovereign data center. Regulated operators cannot send telemetry to someone else's tenant, so we never ask them to.
The day in the life we built to
Night shift
22:00 – 06:00
Day shift
06:00 – 14:00
Core router
- first alert 09:14
- upstream link suspected
- next steps attached
detail intact
The shift changes. The incident detail does not.
Who we work with
Complex infrastructure, high uptime, and a team that cannot grow fast enough.
Multi-vendor infrastructure, rising alert volume, and troubleshooting that rests on a handful of senior people.
Where they operate
- Telecommunications
- Financial services
- Retail
- Oil and gas
- Data centers and critical infrastructure
What they are living with
- Multi-vendor networks and siloed operational systems
- High volumes of alerts, incidents and logs
- Inconsistent mean time to resolution
- Engineers correlating logs by hand
What they need from us
- Business-critical uptime
- Efficiency without adding headcount
- Private or on-premise deployment
- Answers an engineer can verify
The business case
What a decade of engineering is worth on an invoice.
Modeled over three years on a real deployment shape.
Download the CTO ROI brief- 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.
What we built
One platform. Three stages.
Data in as it arrives, analysis at scale, insight an engineer can act on.
See the full platform-
01 Ingest @ scale
Data as it arrives
- Syslogs, logs, configs, tickets, topology
-
02 Analyze @ scale
Analysis and evidence
- LLMs, agentic RAG, anomaly detection
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03 Insights @ scale
Insights and remediation
- Early warning trends, root cause, next steps
Meet the team
Meet the team. See the platform.
Get us on a call with your network in front of us.
Common questions
What buyers ask us first.
MIR Digital Solutions. PulsarOps AI is a MIR platform, built by a founder-led team rooted in data engineering, ML and enterprise AI. More at mirdigitalsolutions.com.
No, it runs alongside what you have. Monitoring tells you something broke. PulsarOps AI is where your team finds out why, with the evidence attached.
Nowhere. Deployment is on-premise, sovereign-ready and cloud-agnostic, against only your data sources inside your own perimeter. The security and deployment page has the detail procurement asks for.
The United States, Canada and the United Arab Emirates, serving enterprise NOCs worldwide.
With a demo call and a real incident from your infrastructure. Book one on the demo page or write to sales@pulsarops.ai.
Book a demo
See PulsarOps AI running on your own data.
Thirty minutes with an engineer. Bring a real incident.