Meaningful inference volume
The workload consumes meaningful AI inference over time.
Recurring activity gives enough signal for measured savings to matter commercially.
Where Forge Fits
Qualify the workload, not just the company.
The strongest current fit is standard API-addressable AI inference where the workload can be identified, baseline cost measured, acceptable outcomes defined and an approved provider path used.
Fit is assessed at workload level. A large enterprise can have some workflows that are strong candidates, others that require custom review and others that should remain outside standard Forge scope.
Five characteristics of an attractive Forge workload
Volume, integration control, accepted outcome definition, baseline measurability and recurring economic relevance all matter before a workload should be considered attractive.
Controllable integration path
The workload is accessible through an approved integration path.
Forge needs a bounded way to identify and govern eligible activity.
Definable accepted result
The business can define what acceptable output means.
Required outcome quality must be explicit enough to govern optimization.
Credible baseline
The no-Forge cost or provider usage can be measured.
A real baseline is necessary for defensible shared-savings evidence.
Recurring commercial relevance
There is enough activity for verified savings to matter.
Forge is not positioned around one-off trivial usage where evidence has little economic significance.
Strong current fit examples
Examples are indicators, not automatic qualification. Support, document and enterprise-automation workflows can be strong candidates when they are API-addressable, measurable and compatible with the required security envelope.
Support & service
Classification, summarization and structured assistance.
Strongest where traffic is API-addressable and quality can be measured.
Document workflows
Extraction, review and structured document analysis.
Requires appropriate security and data-handling qualification.
Enterprise automation
Recurring text and structured inference embedded in business processes.
Best fit when the workload has stable identifiers and an approved provider path.
Important boundary
Not every workload is standard-fit today
Large-scale video generation, large-media creative pipelines, real-time streaming voice, PHI-bearing healthcare inference, highly restricted data, closed embedded SaaS AI, on-device inference, training and fine-tuning, safety-critical autonomous control and unmeasurable subjective outcomes require separate evaluation or fall outside the current standard scope.
Next step
See where Forge could fit your AI environment.
Start with a high-level qualification conversation. No sensitive or confidential information is required.