0data leaves your environment
Your data never becomes someone else's training set
Training and inference happen inside infrastructure you control — on-premise, your cloud account, or an air-gapped box. No prompt logging by a third party.
Small language models, built in depth
Small models.Deep in yourown domain.
We build small language models for companies — trained on your data, tuned to your vocabulary, small enough to run on your own hardware. Scroll to see exactly what happens between the words you type and the words a model predicts.
- 0.5–7B
- parameter range
- 100%
- data stays yours
- ~40ms
- typical response
Typical deployment configuration; varies by model size and hardware.
Inside one forward pass01 / 06
Tokenization
A model never sees words. Your sentence is cut into tokens — whole words, fragments, punctuation — using a vocabulary learned from your domain.
tokenize
"The patient reports a mild fever and"
Thepatientreportsamildfeverand
7 tokens
Why small
Bigger is a benchmark. Smaller is a business case.
20–60×cheaper per million tokens
A frontier model is overkill for one job
You don't need a model that writes sonnets to classify a claim or draft a discharge note. A well-tuned 3B model can outperform a much larger general-purpose model on a narrowly defined domain task, at a fraction of the bill.
40msmedian latency, on-device
Fast enough to sit inside the workflow
Small models fit on a laptop, a handset, or a shop-floor terminal. No round trip, no outage window, no rate limit at month end.
1domain, learned properly
Non-linear gains from narrow scope
Accuracy does not climb smoothly with parameters. It jumps when the vocabulary, the data and the evaluation all match the work being done. That jump is what we engineer.
Built for narrow, high-value domainsThe right model for
The right model for
the work.
Insurance
Claims classification
3B domain model
Manufacturing
Maintenance + shop-floor terminology
Local model
Healthcare
Clinical documentation
Private deployment
Finance
Policy and compliance language
Controlled model
The path to production
The methodEight weeks from
Eight weeks from
domain to weights.
A fixed, staged engagement. You own the weights, the data pipeline, and the evaluation suite at the end of it.
- 01week 1
Scope the domain
We sit with the people doing the work, define the one task worth automating, and write the evaluation before writing any training code.
- 02weeks 2–4
Curate the data
Extraction, de-duplication, PII handling, a domain vocabulary, and a held-out set your own experts label. This is where most of the accuracy comes from.
- 03weeks 5–8
Train and evaluate
Continued pre-training and fine-tuning at the smallest size that clears your bar. Every run is scored against the evaluation from step one.
- 04ongoing
Ship and watch
Quantised weights deployed where you need them, with drift monitoring and a retraining loop fed by real usage.
Explore an SLMTell us what your
Tell us what your
data already knows.
Describe your use case and we’ll reply with a straight answer on whether a small model is the right tool — and what it would take.