Generative & Foundation Model Engineering
Adapt model behavior to a specific task, with evidence that the adaptation helps.
Who commissions this
A defined technical problem.
Organizations whose domain, data, quality requirements, or operating constraints exceed a basic hosted-model integration.
Engineering scope
What the work covers.
- Compare prompting and retrieval baselines before committing to model adaptation.
- Assess dataset rights, quality, provenance, coverage, and held-out splits.
- Evaluate fine-tuning or parameter-efficient adaptation against quality and cost targets.
- Engineer routing, structured outputs, tools, and regression checks around model failure modes.
Representative deliverables
Work your team can inspect and use.
- Adaptation decision brief and baseline comparisons
- Versioned dataset and training/evaluation specifications
- Model-system prototype with documented failure modes
- Deployment and maintenance requirements
A measurable outcome, scoped together.
A task-specific model system with a reproducible comparison to simpler alternatives. Training is commissioned only when the evidence warrants it.
Typical engagements begin at $50,000. Before a proposal, we agree the business objective, data access, system boundaries, deliverables, acceptance criteria, and operating owner. Timelines depend on scope and access.
Related technical evidence
Production AI architecture and inference economics · How to verify an AI performance claim
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