System telemetry
Observe requests, responses, retrieval, tools, latency, cost and failure paths.
Solution / 07
AI operations
The monitoring, evaluation and lifecycle capabilities required to operate AI systems as dependable institutional services.
The proposition
Production AI requires continuous evidence about quality, cost, risk, drift, usage and outcomes—not periodic model checks alone.
01
Earlier detection of quality and risk issues
02
Controlled cost and provider performance
03
Repeatable release and change management
What we engineer
A complete operational capability—not an isolated model.
Every engagement is shaped around the institution, its constraints and the outcomes the system must produce.
Continuous evaluation
Run quality, safety and task-specific evaluation against representative datasets.
Lifecycle management
Control versions, releases, rollback, provider changes and deprecation.
Operational governance
Connect incidents, exceptions and material changes to accountable owners.
System flow
How intelligence moves through the system.
This visual grammar is consistent across Praxnetics: context becomes intelligence, intelligence is governed, and action remains observable.
- STEP 01Production events
- STEP 02Telemetry
- STEP 03Evaluation
- STEP 04Risk and cost analysis
- STEP 05Intervention
- STEP 06Improvement
Applications
Where this becomes operational.
01
AI platform operations
02
Model gateway management
03
Agent monitoring
04
Knowledge quality operations
05
Evaluation pipelines
06
AI incident management
Governance by design
Control is part of the architecture.
The system is designed so that trust can be demonstrated through evidence, permissions, evaluation and accountable intervention.
Release gates
Evaluation thresholds
Cost budgets
Drift detection
Incident response
Change evidence
Begin with the operational challenge