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Phaidra Prism

Resolve infrastructure issues early before they impact operations

Phaidra Prism gives operators the speed and institutional knowledge to stop critical incidents before they impact SLAs.
Problem

Traditional tools surface alarms but investigation workflows remain manual

Operators spend valuable time manually correlating telemetry, alarms and documentation across disconnected systems.
Solution

Prism builds a living operational model of your AI factory

Prism creates a facility specific knowledge graph that models how infrastructure systems, dependencies, telemetry, and operational conditions interact across your environment. The result is intelligence grounded in how your facility actually behaves.
How it works

Built around real-time operational context

1

Ingest live telemetry, operational data and design documentation

2

Cleaned & normalized into ontology

3

Build factory-specific knowledge graph

4

Analytics & conditions evaluated

5

Triaged insights surfaced to operator

Core capabilities

Instead of reacting to alarm floods, operators proactively address issues via actionable intelligence

Alarm prioritization

Reduce alarm fatigue by identifying the events with the greatest operational impact and surfacing them in the appropriate context.
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AI-assisted investigation

Investigate system behavior in natural language to troubleshoot issues and perform root cause analysis in minutes, not days.

Report generation

Generate end of shift summaries instantly with timelines of operational events, investigations and system activity.

Continuous operational analytics

Always-on analytics to detect abnormal behavior, drift, and instability before issues escalate into operational incidents.
Case study

Measured in production environments

Prism identified early signs of chiller degradation that led operators to uncover more than 50 malfunctioning fans across a customer facility.

By surfacing the operational impact before it became a larger reliability issue, the team avoided significant energy losses, reduced cooling risk, and eliminated the need for costly infrastructure retrofits.

50 +
malfunctioning fans identified
$ 700 K+
avoided retrofit costs
$ 2 K+/day
potential energy savings identified
25 %
reduction in chiller plant inefficiency
Trust

Intelligence grounded in deep
operational expertise

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Built by infrastructure experts

Prism was developed by engineers and operators with experience across data centers, control systems, industrial infrastructure, HVAC systems and machine learning.

Informed by production environments

Many of Prism’s analytics and investigative workflows are based on real operational patterns, troubleshooting methods and failure conditions observed in production environments.

Designed for real operations

This gives operators insights that reflect how infrastructure systems actually behave in the field, not just what looks statistically abnormal.

See Phaidra Prism in action

Request demo