The same pipeline. Pointed at your problem.

We fine-tune speech, language, embedding, and multimodal models on your data — and give you weights you can host yourself. The output is a model, not a vendor lock-in.

Start a project Talk to the team

When to call us

You probably need a custom model if…

General models miss your jargon

Your industry has acronyms, drug names, part numbers, or local names that get garbled by general LLMs and ASR systems. A small fine-tune fixes 80% of those errors.

Your data can't go to the cloud

Healthcare, finance, government, regulated research. We train on your hardware (or in an enclave) and hand back weights you keep.

You speak a language they don't

Tigrigna, Amharic, Afaan Oromoo, Somali, Afar, Sidamo — and the long tail beyond. We've built the data pipelines for under-resourced languages.

Cost-per-call is killing you

Frontier APIs at scale are expensive. A small fine-tuned model running on your own GPU is often 10–100× cheaper per request, and faster to first token.

How an engagement runs

Five steps. No surprises.

Week 0

Scoping & eval design

We sit with you and your data, define the tasks the model has to do, and write the eval suite first. No "ship it and pray" — the success bar is set before training.

Week 1–2

Data curation

Cleaning, deduping, alignment, confidence scoring, gold-set review. We turn whatever you have — transcripts, PDFs, support tickets, audio — into a training manifest you can audit.

Week 2–4

Training & iteration

Pick the right base model (Llama, Qwen, Whisper, Piper, your in-house checkpoint), train, evaluate, iterate. You see eval numbers and qualitative samples weekly.

Week 4–5

Deployment

Hand-off to your hardware, our managed inference, or both. We package the model in the runtime that fits — vLLM, llama.cpp, Triton, whatever you're already running.

Ongoing

Maintenance & refresh

Quarterly retrains as your data and tasks evolve. We watch the eval suite for regression and re-tune on the deltas instead of starting from scratch every time.

Always

You own the weights

The output of an engagement is a model checkpoint, the eval suite, and the data pipeline that produced them. If you fire us tomorrow, the model still works.

What we tune

Beyond LLMs.

Language

Llama, Qwen, Mistral, Gemma. Continued pretraining and instruction tuning. Domain adaptation. Long-context fine-tuning.

Speech recognition

Whisper-class encoder-decoder, parakeet, conformer-CTC. Domain audio, dialect, accent, and vocabulary adaptation.

Speech synthesis

Piper, VibeVoice, XTTS-class voice models. Multi-speaker, multi-language, and consented voice cloning.

Embeddings

Domain-tuned embedding models that beat off-the-shelf cosine similarity for your retrieval task.

Start a project

Tell us what you're building.

We'll respond within two business days with either a yes-and-here's-how, a no-and-here's-why, or a follow-up question. We don't waste each other's time.