Services

End-to-end model training

We work with open-weight models you can inspect, host, and keep. Each engagement defines its deliverables and evaluation criteria before handoff.

Dataset engineering, training, and deployment documentation under one statement of work.

Dataset engineering

We turn raw exports, documents, and images into training-ready datasets, with documented quality and privacy controls.

Ingestion from S3, GCS, SharePoint, or secure transfer
Deduplication, filtering, and stratified splits
Captioning pipelines for language and image data
PII detection and redaction workflows
Dataset cards with source and lineage records

LLM fine-tuning and alignment

Domain assistants and copilots built on open instruct models, with evaluation harnesses in place before anything reaches production.

Supervised fine-tuning (SFT) on your corpus, with LoRA, QLoRA, or full fine-tuning
DPO preference tuning for tone and policy adherence
Long-context fine-tuning and retrieval-aware (RAG) tuning
Export to safetensors, GGUF, or vLLM-ready bundles
Regression suites on held-out prompts

Image and video model training

Brand-consistent LoRAs and style packs on Stable Diffusion, FLUX, and related open models, trained only on data you own or are licensed to use.

Style, product, and character LoRA training
DreamBooth identity training with documented consent
Paired data preparation for ControlNet and IP-Adapter workflows
Side-by-side comparison grids and scored review rubrics
Multi-LoRA libraries for creative teams

Evaluation and safety

We measure accuracy, refusal behavior, toxicity, and brand fit before launch, for both text and image models.

Custom benchmark suites for each use case
Red-team prompts and image stress tests
Human evaluation protocols where automated metrics fall short
NSFW and policy filters for image pipelines
Scorecards written for non-technical stakeholders

Deployment handoff

We document how to run your model in your own environment. You are not locked to our infrastructure.

Modal serverless GPU endpoints
AWS SageMaker and EC2 inference patterns
VPC-isolated training and artifact storage
Private Hugging Face model and dataset repositories
Runbooks for your MLOps team

Training techniques

Supervised fine-tuning (SFT)Full fine-tuningLoRA and DoRAQLoRALyCORIS (diffusion)DPO preference tuningDreamBooth (with consent)Knowledge distillationAdapter merging and stacking

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