Fine-tuned models that know your domain
Foundry is the training pipeline that transforms general-purpose language models into domain specialists — without catastrophic forgetting, without drift, without shortcuts.
Seven-phase pipeline
From corpus assembly to production deployment, Foundry manages the complete model lifecycle. Domain knowledge is gathered, validated against vertical-specific schemas, benchmarked, and fine-tuned using KL-regularized training that preserves general reasoning.
Models are aligned with domain constitutions — goal principles, failure modes, and judgment boundaries — then verified against held-out tasks before graduating to production with continuous drift monitoring.
Performance
Domain constitutions
Principles, not just parameters.
Every domain model is aligned with a constitution that defines what to optimize for, what to never do, and how to handle ambiguity.
Goal Principles
What the model should optimize for. Accuracy, compliance, speed, cost — weighted per domain. These define the north star for every decision the model makes.
Failure Principles
What the model must never do. Hard boundaries that cannot be overridden by optimization pressure. Safety rails that hold even under adversarial conditions.
Judgment Principles
How the model handles ambiguity. When to escalate, when to act, when to ask for more context. The nuanced middle ground between automation and human oversight.
Zero drift
KL-regularized fine-tuning.
Standard fine-tuning trades general intelligence for domain knowledge. Our approach constrains training so models gain domain mastery while preserving their reasoning foundation.
Catastrophic forgetting
The model overfits to domain patterns. General reasoning degrades. Edge cases become blind spots.
Overfits to training distribution
Loses ability to handle novel inputs
Blind spots in unseen scenarios
Preserved reasoning
Domain accuracy improves while general capabilities stay intact. The model handles novel situations using both knowledge types.
Learns domain patterns without overfitting
KL constraint maintains base capabilities
Reasons through novel situations compositionally
Triggers re-training when performance degrades
Build your domain model.
From corpus to production in weeks. Constitutional alignment guaranteed.