VDF AI turns governed enterprise data into domain-specific models that understand your terminology, policies, workflows, and response standards. The fine-tuning program connects data preparation, model training, evaluation, deployment, and stewardship in one private AI lifecycle.
General-purpose LLMs are useful, but they do not naturally know your product codes, approval rules, risk language, document templates, escalation paths, or compliance expectations. Private fine-tuning teaches the model how your organization works while preserving data residency and operational control.
The result is not just a custom model. It is a governed AI asset: prepared from traceable datasets, evaluated against business scenarios, deployed through VDF AI Networks, and monitored with evidence that technical, risk, and compliance teams can review.
RAG retrieves context. Fine-tuning changes the model behavior your users experience on every request.
The VDF AI Cloud platform connects data, agents, model routing, execution evidence, and compliance workflows instead of treating fine-tuning as an isolated training job.
Prepare source assets through connectors, exploratory data analysis, feature discovery, vector indexes, semantic search, and fine-tune dataset exports. This creates a controlled data path from enterprise source to training file.
Register providers, models, tools, and agents, then benchmark candidate models against domain-specific scenarios before they are used in real workflows. Evaluation becomes a promotion gate, not an afterthought.
Deploy validated models into governed networks with routing diagnostics, run records, learning analytics, and Vault-backed proof for execution history. This is where model behavior becomes observable and auditable.
Powered by VDF Data Suite for data operations and integrated with the broader VDF AI platform for governed deployment.
Discover and prepare data from PostgreSQL, MySQL, Microsoft SQL Server, Oracle, JDBC-compatible systems, documents, APIs, and knowledge bases without moving sensitive sources into a public training environment.
Generate reusable training datasets with prompt-completion rows and metadata, then export them as OpenAI chat JSONL, OpenAI completion JSONL, Anthropic messages JSONL, or generic CSV for downstream training workflows.
Run private model training and adaptation on customer-controlled infrastructure. VDF AI helps teams choose the base model, tuning strategy, compute plan, acceptance criteria, and deployment target for the business workflow.
Use exploratory data analysis, feature discovery, asset quality signals, and PII review checkpoints before training begins. Teams see coverage gaps, imbalance, and source-data risks early enough to fix them.
Compare fine-tuned candidates against baselines using domain-specific test scenarios, reference answers, regression history, and quantitative scores before approving a model for production traffic.
Deploy approved models into VDF AI Networks with auditable routing, run history, drift review, retraining triggers, and compliance evidence. Stewardship keeps the model aligned after launch, not only at go-live.
A structured program that connects data readiness, training, evaluation, deployment, and post-launch review.
Connect enterprise sources, inspect available assets, run exploratory analysis, and define success metrics for the exact workflow the model must improve.
Create fine-tuning datasets, review representative examples, choose the base model and tuning strategy, then run private training on customer-controlled infrastructure.
Benchmark candidate models against domain scenarios, baseline models, expected answers, and prior versions before any production promotion decision.
Register the validated model in VDF AI Networks, configure routing policies, keep audit documentation, and review drift, performance, and retraining triggers after launch.
Fine-tuning is most valuable when output quality depends on proprietary examples, structured formats, and repeatable decisions.
Fine-tune models on contracts, claims, KYC records, or policy documents so outputs follow internal classification schemas, redaction rules, and approval language instead of generic summaries.
Build copilots that understand banking product codes, telecom network terminology, manufacturing defect categories, legal clause structures, or internal support policies.
Train small, fast classifiers and specialist models that VDF AI Networks can route to automatically, reserving larger general models for tasks that actually need them.
Use database records, knowledge assets, and operational examples to teach consistent extraction, classification, summarization, and decision-support behavior.
Common questions about private model fine-tuning with VDF AI.
Build a fine-tuning path that keeps data under your control, proves model quality before deployment, and connects the result to governed enterprise AI workflows.