200GB+ VRAM training infrastructure

Custom AI Models & GenAI

Fine-tune, train, and deploy domain-specific AI models. RAG pipelines, custom training, and production MLOps.

What is Custom Models?

Custom AI models are trained or fine-tuned on your specific data to outperform general-purpose models on your domain. Whether it's understanding your industry jargon, classifying your documents, or generating content in your brand voice — custom models deliver accuracy that off-the-shelf models can't match.

RAG (Retrieval-Augmented Generation) pipelines let your AI answer questions from your documents, databases, and knowledge base without expensive full model training. Combined with fine-tuning, you get an AI that truly understands your business.

We handle the full pipeline: data preparation, model selection, training infrastructure, optimization (quantization, distillation), and production deployment with monitoring.

Use Cases

Domain-Specific Fine-Tuning

Models trained on your data that outperform general-purpose AI.

RAG Pipelines

AI that answers questions from your documents, databases, and knowledge base.

Model Optimization

Quantization, distillation, and optimization for faster, cheaper inference.

Custom Classification Models

Train models to classify documents, emails, images, or support tickets specific to your industry and taxonomy.

Domain Language Models

Fine-tuned language models for specialized vocabulary: legal, medical, finance, or technical documentation.

AI-Powered Search & Q&A

Build intelligent search over your knowledge base — employees and customers get instant, accurate answers from your docs.

How It Works

Data Audit

Assess quality, volume, and gaps

Model Selection

Base model and architecture choice

Fine-Tuning

Domain-specific training and optimization

Evaluation

Accuracy benchmarks and bias testing

Deploy & Monitor

Production serving with drift detection

Fine-Tune Pipeline

Tech Stack

PyTorch

Hugging Face

LoRA/QLoRA

RAG

ChromaDB

Pinecone

NVIDIA GPUs

Python

Docker

FastAPI

PROOF POINT

RunHotel — Custom Models in Production

Fine-tuned domain models achieving 95%+ accuracy across healthcare, finance, and hospitality — trained on our RTX 5090 + Threadripper PRO infrastructure with 200GB+ VRAM.

Read full case study →

200GB+

VRAM Available

100 examples

Fine-tune From

95%+

Accuracy

On-prem/Cloud

Deployment

Built for Every Stakeholder

Full model lineage tracking — every training run versioned and reproducible
Evaluation framework with custom benchmarks for your domain
Deployment-ready models: ONNX, TensorRT, or cloud API formats
Bias testing and safety evaluation included in every project

Frequently Asked Questions

Absolutely. We can train on-premise using your infrastructure, in your private cloud (AWS/GCP/Azure), or on our air-gapped training servers. Data never leaves your control. We sign NDAs and comply with GDPR, SOC 2, and industry-specific regulations.

It depends on the task complexity. For fine-tuning a language model on your domain, 500-5,000 high-quality examples often suffice. For classification tasks, 100-1,000 labeled examples per class is typical. We always start with a data audit to assess what you have and identify gaps — sometimes clever data augmentation reduces the collection burden significantly.

A typical fine-tuning project takes 4-8 weeks end-to-end. Week 1-2 covers data preparation and baseline evaluation. Weeks 3-5 focus on iterative training runs with hyperparameter tuning. Final weeks handle evaluation, bias testing, and deployment setup. The actual GPU training time is hours to days — the human effort is in data curation and evaluation.

Absolutely. We regularly improve existing models through additional fine-tuning, distillation (making large models smaller and faster), quantization (optimizing for specific hardware), and RAG pipeline optimization. If your current model is underperforming, we start with a diagnostic to identify whether the issue is data, architecture, or deployment-related.

Fine-tuning permanently alters a model's weights by training on your data — best for domain-specific language, classification, or generation tasks. RAG (Retrieval-Augmented Generation) retrieves relevant documents at query time and feeds them to an unmodified model — best for Q&A over large, frequently updated knowledge bases. Many projects use both: fine-tuning for tone and domain understanding, RAG for factual accuracy from your documents.

Talk to a Custom Models Specialist

Share your data challenge — we'll recommend an approach within 48 hours.

Book a Call

Let's Train Your Custom Model