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When prompting a frontier model is too slow, too expensive or too generic, we train a smaller one that wins on your task. Fine-tuning, distillation and task-specific classifiers, measured against a baseline before anything ships.

Get my agent now What we build ↓
Week 1
Prototype on your data
Day 30
Production pilot
100%
Yours: code & weights
Ch. 01The problem

Classifying tickets, extracting fields, routing documents or scoring leads doesn't need a frontier model on every call. A small model trained on your examples is faster, cheaper, predictable, and can run offline on your own hardware.

Ch. 02What we build

  • 01

    Task classifiers

    Tiering, routing, intent and risk scoring. We run a local classifier in our own prospecting, and the data never leaves the machine.

  • 02

    Extraction models

    Structured fields out of invoices, contracts, forms and emails, with confidence scores and a human queue for the doubtful ones.

  • 03

    Distillation

    Teach a small open model to imitate a frontier model on your task, then cut cost per call dramatically.

  • 04

    Domain tone & format

    Fine-tunes that write in your house style, follow your schema and stop needing three paragraphs of prompt.

Ch. 03What you get

  1. 01Dataset build: collection, labeling guidelines, cleaning, splits
  2. 02Baseline vs. fine-tune evals on held-out data
  3. 03Training runs (LoRA / QLoRA / full) with reproducible configs
  4. 04Serving: managed endpoint, your cloud GPU, or on-prem
  5. 05Model card, weights and retraining pipeline handed over
Stack
  • PyTorch
  • Hugging Face
  • Unsloth
  • LoRA / QLoRA
  • vLLM
  • Ollama
  • Llama
  • Qwen
  • Mistral
  • OpenAI fine-tuning

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Ch. 04FAQ

Do you train models from scratch?

Almost never, because it rarely pays off. We fine-tune or distill strong open or commercial base models, which gets you most of the gain at a small fraction of the cost.

How much data do we need?

For classifiers and extraction, often a few hundred to a few thousand good examples. We help you build and label the dataset, and we tell you upfront if the data isn't there.

Who owns the trained model?

You do. Weights, datasets, training configs and eval results are delivered to your storage.

Can it run without internet?

Yes. Small models can run on your servers or a single GPU, fully offline, which is useful for LGPD/GDPR-sensitive data.

Other capabilities
Ch. 05Now

Tell us the pain. We answer in one business day.

  • ✓Reply in 1 business day
  • ✓Fixed price before we start
  • ✓NDA on request