AI Model Training & Specialization

Train, fine-tune, and deploy domain-specific AI models. From data preparation and curation to production-ready deployment — we specialize models to understand your business, your terminology, and your workflows.

Services

What we train

Full-spectrum model training and specialization — from lightweight fine-tuning to training from scratch.

LLM Fine-Tuning

Specialize open-weight LLMs (Llama, Mistral, Qwen, Phi, Gemma) for your domain. LoRA, QLoRA, and full fine-tuning with curated instruction datasets. Your terminology, your tone, your knowledge.

Embedding Models

Train custom embedding models optimized for your data domain. Improve retrieval accuracy for semantic search, RAG, and document similarity. Domain-specific vector representations.

Classification Models

Build custom classifiers for document routing, intent detection, sentiment analysis, content moderation, and multi-label categorization. Trained on your labeled data.

RLHF & Alignment

Reinforcement Learning from Human Feedback to align model outputs with your quality standards, brand voice, and safety requirements. DPO, PPO, and constitutional AI methods.

Data Curation

Prepare high-quality training datasets from your raw data. Cleaning, deduplication, format normalization, instruction generation, and quality scoring. Garbage in, garbage out — we prevent it.

Model Evaluation

Rigorous evaluation with domain-specific benchmarks, human evaluation panels, A/B testing, and automated metrics. Know exactly how your model performs before deployment.

Training Pipeline

From raw data to model in production

A structured, repeatible pipeline for training specialized AI models.

Fase 1

Data Collection

Collect domain data from documents, databases, APIs, and logs.

Fase 2

Data Curation

Cleaning, deduplication, normalization, and formatting of training data.

Fase 3

Base Model Selection

Choose the open-weight model best suited to your use case.

Fase 4

Fine-Tuning

LoRA, QLoRA, or full fine-tuning on curated datasets.

Fase 5

Evaluation

Benchmarks, human evaluation, A/B testing, error analysis.

Fase 6

Deployment

Optimization (quantization, distillation) and production deployment.

Training Pipeline — Live
[Fase 1] Dati: 42,000 curated instruction pairs from domain documents
[Fase 2] Dedup: 98.7% unique → 41,454 training samples
[Fase 3] Base: Llama 3.1 8B (selected for latency + quality balance)
[Fase 4] Train: QLoRA (r=16, alpha=32) × 3 epochs on A100
[Fase 5] Eval: Domain F1: 0.94 | BLEU: +23% vs base | Latency: 42ms
[Fase 6] Deploy: GGUF Q4_K_M → 4.2GB → vLLM on private GPU

✓ Model deployed. Zero external dependencies. 100% private.
Applications

What specialized models can do

Domain-specific models outperform general AI on defined, scoped tasks.

Legal Contract Analysis

Identify clauses, extract obligations, flag risks, and classify contract types with models trained on legal corpora.

Medical NLP

Extract diagnoses, medications, procedures, and clinical entities from unstructured medical text and clinical notes.

Sentiment & Financial Analysis

Analyze earnings calls, analyst reports, and news with models optimized for financial language, terminology, and sentiment.

Multilingual Customer Support

Fine-tune models on your support history to handle customer inquiries in multiple languages with your brand voice and resolution patterns.

Code Generation & Review

Train code models on your internal codebase. Generate code in your style, follow your patterns, and review to your standards.

Document Classification

Automatically categorize incoming documents — invoices, contracts, applications, reports — into your taxonomy with high precision.

Domain-Specific Search

Custom embedding models that understand your industry vocabulary. Better retrieval for technical, scientific, or niche searches.

Content Moderation

Train classifiers optimized for your content policies. Detect harmful, misleading, or off-topic content with precision tuned to your platform's specific needs.

Technology Stack

How we train

Production-grade training infrastructure and tooling.

Training Frameworks

PyTorch Hugging Face DeepSpeed Axolotl

Fine-Tuning Methods

LoRA QLoRA Full Fine-Tune RLHF / DPO

Base Models

Llama 3 Mistral Qwen 2.5 Phi 4 Gemma 2

Serving & Deployment

vLLM TGI GGUF / llama.cpp ONNX

Evaluation

lm-eval-harness MT-Bench Custom Benchmarks

Infrastructure

NVIDIA A100 / H100 CUDA Weights & Biases MLflow

Ready to specialize AI for your domain?

Tell us about your data, your use case, and your quality requirements. We will design a training pipeline that delivers results.

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