FOUNDATIONAL - LABS

Fine-tune with retrieval in the loop - models that learn from your documents, not just generic next-token habit.

THESIS

CRAFT (Contextual Retrieval Augmented Fine-Tuning) pairs retrieval with fine-tuning so models learn how to use private context - not only stuff chunks into a prompt at inference time. Guardrails and SecureDataBridge wrap the workflow so enterprise corpora can participate without leaking into training sprawl.

THE PROBLEM SPACE

01Prompt-only RAG loses institutional voice the moment the retrieved chunk is weak.

02Blind fine-tuning without retrieval discipline overfits or invents.

03Enterprises need adaptation that stays tied to authoritative documents.

APPROACH

Fine-tuning regimes that treat context documents as first-class training signal.

Dedicated instances per run; Guardrail Engine on prompts; SecureDataBridge between stages.

Enterprise tools and custom datasets in the loop for domain fit.

Feedback and incremental training data for continuous improvement.

ARCHITECTURE SKETCH

How the pieces fit

01

CRAFT TRAINER

Runs fine-tuning epochs with retrieved and contextual document packs.

02

CONTEXT PACK BUILDER

Selects and versions the document sets that teach the model what this environment looks like.

03

GUARDED INFERENCE PATH

Same security spine as Fusion - validate, encrypt, generate, re-validate.

04

EVAL HARNESS

Measures relevance and faithfulness against held-out enterprise questions.

OPEN QUESTIONS

  • How much should stay in weights vs. stay in the retriever for regulated corpora?
  • What unlearning story do we owe customers when documents are withdrawn?

WHY IT MATTERS

Domain models that behave like they work here - because they were trained with here in the loop.

A bridge between VectorStore retrieval and production Agent Cloud skills.

RELATED PROGRAMS

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