Fine-tune foundation models
on your proprietary data.
Managed infrastructure for LoRA and QLoRA training on A100 clusters, automated data pipelines, MLOps dashboards, and production deployment — your custom LLM, production-ready in days.
Your data is your competitive advantage. Your model should reflect that.
General-purpose foundation models know the world. They do not know your clinical documentation templates, your formulary, your regulatory jurisdiction, your internal terminology, or how your organization communicates with patients and customers.
Fine-tuning bridges that gap — and AI Factory manages the entire process, from raw data to a deployed, API-accessible model that knows your organization.
Managed end-to-end. From data to deployed model.
Everything needed to take raw proprietary data and produce a production-grade fine-tuned model.
Managed Fine-Tuning
LoRA and QLoRA fine-tuning on NVIDIA A100 80GB clusters. We manage the infrastructure — you own the model. No GPU procurement, no cluster ops, no MLOps overhead.
Data Pipelines
Automated data ingestion, cleaning, deduplication, and formatting from your proprietary sources. Structured outputs, unstructured documents, conversational data — all supported.
MLOps Dashboard
Real-time training metrics, loss curves, evaluation benchmarks, and checkpoint management — surfaced in a dashboard your team can actually read.
Evaluation & Benchmarking
Task-specific evaluation runs after every training cycle. Compare fine-tuned model performance against your baseline before any deployment decision.
Custom Deployment
Deploy to your own infrastructure, our managed cloud, or an air-gapped environment. OpenAI-compatible REST and streaming APIs included.
Ongoing Retraining
Scheduled retraining pipelines as your data grows. Adapter checkpoints versioned and stored. Your model improves continuously as your organization learns.
Six steps. One fine-tuned model.
From your data to a deployed model your team can query via API.
Data assessment
We review your proprietary data — volume, format, quality. We tell you what is trainable and what needs cleaning.
Pipeline build
Automated ingestion, formatting, and validation. Instruction-response pairs generated from your raw documents where needed.
Base model selection
We recommend the right foundation model for your task, size constraints, and inference budget. Or bring your own.
Fine-tuning run
LoRA or QLoRA training on A100 80GB clusters. Hyperparameters tuned for your dataset and task. Checkpoints saved throughout.
Evaluation
Task-specific evaluation against your baseline. We measure what you actually care about, not just training loss.
Deployment
Your model deployed to your target environment. OpenAI-compatible API. On-premise, private cloud, or our managed infrastructure.
Supported foundation models.
We fine-tune on the models that matter for regulated industries — or yours.
General-purpose foundation. Excellent for instruction-following and domain adaptation.
Compact, fast, and strong at structured output tasks and multi-turn workflows.
Small model, strong reasoning. Cost-effective for high-frequency inference.
Pre-trained on PubMed and clinical guidelines. Strong baseline for clinical language tasks.
Biomedical domain pre-training. Well-suited for drug discovery and clinical NLP tasks.
Already have a foundation model? We fine-tune on your choice of base, your data, your deployment.
What organizations build with AI Factory.
Real fine-tuning use cases across regulated industries.
- Clinical documentation models trained on your note templates
- Coding assistant fine-tuned on your ICD/CPT coding patterns
- Prior authorization reasoning for your specific payers
- Patient communication generation aligned to your organization's voice
- Regulatory document generation and compliance summarization
- Drug-drug interaction reasoning fine-tuned on your formulary
- DSCSA exception handling and investigation drafting
- Clinical trial protocol extraction and summarization
- Internal knowledge base Q&A trained on your documentation
- Customer support models aligned to your product and policies
- Code generation fine-tuned on your internal codebase
- Contract review and clause extraction for your jurisdiction
Flexible engagement models.
From a single fine-tuning run to continuous, managed retraining.
Starter
One fine-tuning run
$3,500
- Up to 7B param model
- LoRA fine-tuning
- Up to 50K training examples
- Eval report included
- Model weights delivered
- Email support
Team
Quarterly cadence
$9,500
- Up to 34B param model
- LoRA or full fine-tune
- Up to 500K training examples
- Automated eval suite
- Hosted inference included
- MLOps dashboard access
- Priority support
Enterprise
Continuous retraining
Custom
- Any model size
- Custom training technique
- Unlimited training data
- Custom benchmarks
- Dedicated ML engineer
- On-prem or VPC deploy
- SLA + dedicated support
Your training data never leaves your control.
For regulated industries, training data is often sensitive — patient records, clinical notes, proprietary research, confidential contracts. AI Factory supports fully on-premise training runs where your data never traverses an external network.
- On-premise training runs available
- VPC-isolated cloud training option
- No training data used to train any other model
- Adapter checkpoints versioned and returned to you
- HIPAA BAA available for healthcare training data
Your custom LLM, production-ready in days.
Tell us about your use case and your data. We will tell you what is achievable, how long it takes, and what it costs.