Resources

What Production AI Looks Like Inside an Air-Gapped Semiconductor Fab

In an air-gapped fab the network boundary sets the deployment architecture before the model is chosen: local compute, portable models, controlled releases, and a feedback loop that runs on site.

Cut Service Parts Forecast Errors by 50%: What Changes With an AI Layer

Forecast accuracy plateaus at 50-70% because the demand signal is incomplete, not because the model is wrong. An AI operating layer expands what the forecasting model sees.

The Four Structural Gaps in Modern SPM Systems

Four architectural gaps common across all major SPM platforms limit forecast accuracy, planning coverage, and execution speed. These are structural, not operational.

Why Your Service Parts Management (SPM) Needs an AI Operating Layer

SPM systems optimize supply response but miss demand signals outside their inputs. An AI operating layer makes the full picture visible and actionable.

5 Ways AI Improves First-Time Fix Rates in Field Service

AI addresses five root causes of low FTFR simultaneously: diagnostics, parts prediction, knowledge management, scheduling, and proactive maintenance.

5 Shifts We've Seen Deploying Enterprise AI

Five key shifts from deploying nearly 100 enterprise AI workflow solutions and the GTM changes required to win in 2026.

AI Isn't a Software Upgrade, and the Market Knows It

Software stocks lost nearly $1 trillion in value despite strong quarters. AI represents a paradigm shift, not an incremental software improvement.

Start with the Workflow, Not the Agent

Function-scoped AI improves local efficiency but workflow-native AI changes cost-to-serve. The P&L impact lives in the workflow itself.

Outcome-Based Agents: Taking Agentic AI to the Finish Line

How outcome-based agents are transforming AI from task completion to business results.

Small Models, Big Impact: Specialized AI for Precision Outcomes

Why specialized small models outperform general-purpose LLMs in aftermarket service.

How to Evaluate AI for Service Operations: The Metrics That Actually Matter

Real deployment data: forecast error 17% to under 3%, FTFR 75% to 88%. How to evaluate AI claims in your service environment.

Keep Your Data Safe: The Embedded LLM Approach

How embedded LLMs protect sensitive data while delivering AI capabilities.

AI in Home Appliance Customer Service Resolution Centers

Transforming appliance support with AI-powered resolution.

AI Warranty Claims Automation: 90% Faster Processing with Built-In Fraud Detection

AI auto-codes 75-85% of warranty claims in under 1 minute, cuts processing time by 90%, and catches fraud that costs OEMs 3-15% of warranty spend.

Navigating the Service Skills Knowledge Gap with AI

How AI bridges the knowledge gap as experienced technicians retire.

Slash Service Costs with the Issue Resolution Curve

Understanding and optimizing the issue resolution curve.

Unlock Insights with Equipment 360 View

Complete equipment visibility for better decision making.

Best AI for First-Time Fix Rates: What Actually Moves the Number

AI parts prediction lifted FTFR from 75-80% to 88% and eliminated 30,000 truck rolls. Here is what differentiates the AI that works from the AI that does not.