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Inference Wire

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SaaS / B2B·AI Agents & Automations·16 Weeks

aiDetect

15 Minutes of Manual Work, Handled by AI Agents in 3 Seconds

A B2B platform handling hundreds of daily production events had outgrown its manual process for detection, ticketing, and stakeholder outreach. We deployed AI agents that monitor every data stream and handle the full incident lifecycle from first detection to notification automatically.

3 seconds

Avg Response Time

$500K

Annual Savings

20+/week

Analyst Hours Saved

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Responsive UI mockup of the AiDetect live monitor dashboard on laptop and mobile screens, an AIOps and system monitoring case study by Hexaa.
“I tell people it's like we added three really reliable people to the team who never take a day off. ”
MR

Marcus Reeves

VP of Production, aiDetect

Before

Manual anomaly scanning, 8-minute response delays, Siloed stakeholder alerts

After

AI-driven anomaly detection, Sub-3-second response, Unified multi-channel alerts

Narrative

The full story.

The Challenge

The production support team handled hundreds of events daily, and each one needed about 15 to 20 minutes to detect, log, and notify the right people. As event volume kept growing, alerts were going out across email, chat, and phone separately, so the right person only got the message about half the time.

What We Built

We built a set of AI agents that work together. One watches incoming data and flags anomalies the moment they appear. Another classifies severity and routes the incident, while a third sends coordinated alerts across email, chat, phone, and messaging simultaneously. The agents also generate enriched tickets in the client's incident management platform and assign ownership on their own. The whole pipeline from detection to ticket to notification wraps up in under 3 seconds.

Results

Incident detection and ticketing now runs automatically for the vast majority of events. Response time went from 15 minutes to under 3 seconds. The right people get notified within seconds instead of finding out late or not at all. The client estimates the platform saves them around $500K a year in avoided SLA penalties and repeat incident costs.

Story gallery

Project snapshots.

Technical proof

How it was built.

Implementation details and the stack behind the delivery.

Implementation highlights

Key engineering decisions from this project.

Cut event to response time from 15 minutes to under 3 seconds
Deployed 6 interconnected microservices powering AI agents that handle detection, routing, ticketing, and communication
Reached full notification coverage across 4 communication channels

Stack snapshot

Tools and platforms used in the delivery.

PyTorchCohere APIPostgreSQL ReactApache Kafka

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