Services and capabilities
Each engagement is shaped around the actual users, operating constraints, system ownership, and desired outcome for organizations evaluating prompting, retrieval, fine-tuning, specialized training, or ground-up model development.
Prompt and workflow optimization can combine dataset preparation and governance with a defined response to “The team assumes every problem needs training.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Retrieval-augmented generation systems can combine fine-tuning and adaptation workflows with a defined response to “Training data is noisy or legally unclear.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Fine-tuning existing foundation models can combine evaluation harnesses and red-team testing with a defined response to “Success has no measurable evaluation set.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Training specialized open-source models can combine GPU and inference architecture with a defined response to “A model works in demos but not production.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Ground-up model feasibility and research planning can combine model serving, monitoring, and rollback with a defined response to “Inference hardware and operating cost are unknown.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Local, cloud, VPS, GPU, and hybrid deployment can combine open-source and hosted model comparison with a defined response to “Privacy requirements conflict with the deployment plan.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Technical and operational coverage
What shapes scope
Direct help from Faith Forge Labs
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