Language models trained for your domain, on weights you own.
Frontier models know the internet. They do not know your contracts, your codes, your dialect or your rules. We fine-tune and post-train open-weight models on your data, including reinforcement learning for reasoning and tool use, and distil them into smaller models that run on your hardware.
Zingaro AI trains custom large language models: supervised fine-tuning, preference and reinforcement post-training (DPO, GRPO and RLHF-style methods), reasoning fine-tunes, domain adaptation, distillation into small language models, and evaluation on the client’s own tasks.
You get
- Model weights you own and can host
- The training and evaluation datasets
- An evaluation report on your own tasks
- A serving setup on your infrastructure or ours
Built with
- SFT and LoRA
- DPO and GRPO
- Reasoning fine-tunes
- Distillation and SLMs
- Open-weight bases
Custom LLM training and fine-tuning, end to end.
Supervised fine-tuning
Domain data, your formats and your tone, on open weights from small to frontier-class.
Post-training with RL
Preference optimisation and reinforcement learning with verifiable rewards for reasoning, tool use and format discipline.
Reasoning models
Models that think before they answer, tuned on your problems, with the cost kept under control.
Small language models and distillation
Teacher-to-student distillation so a small model does one job as well as a large one, at a fraction of the cost.
Continued pre-training
For domains and languages the base model barely saw: legal, medical, Gulf Arabic.
Evaluation
Task suites from your data, judged by people and by models, reported before and after.
From one painful job to a system in production.
Models and agents handle the volume. People handle the edges. You always know which did what.
- 01
Discover.
One or two weeks with your team. The jobs listed, sized and ranked. A number on the first one.
- 02
Build the pilot.
Fixed scope, fixed fee, 4 to 6 weeks. Real output on your real data, measured against the number.
- 03
Ship it.
Into production, inside your boundary if the rules require, with evals gating every release.
- 04
Run it, or hand it over.
We operate it with people on the queue and a weekly report, or your team takes it with the runbooks.
Your data does not have to leave the building.
On your servers
Air-gapped where required.
We install on machines you own, inside your network. Where the rules demand it, the system runs with no outbound connection and updates are carried in by hand. Your team keeps the keys.
Data stays inside your network
Your private cloud
We deploy into your account.
We deploy into your own cloud account, in your region, under your access controls. The data stays in your account. We get the access you grant, and nothing more.
Data stays inside your account
Ours
Managed, fastest to start.
We run it on infrastructure we operate. The right choice when the data is allowed to leave and you want the pilot running this month.
Managed by us, on our terms
In banking, insurance and healthcare, the rules decide where the data sits. We built for that first.
How each option worksWhat comes back, and how we measure it.
Deliverables
- Model weights you own and can host
- The training and evaluation datasets
- An evaluation report on your own tasks
- A serving setup on your infrastructure or ours
Measured by
- Accuracy on your task suite
- Cost per request against the baseline
- Latency at production load
- Regressions caught before release
Real figures come from your pilot. We do not publish invented ones.
What people ask about language models.
Fine-tune or prompt a frontier model?
We tell you after looking at the task. Prompting wins for breadth, fine-tuning wins for consistency, cost, privacy and latency on a narrow job. Often the answer is both, routed.
How much data do we need?
Hundreds of good examples for fine-tuning, more for a new language or domain. We can also generate synthetic data and have people check it.
Whose model is it?
Yours. Weights, datasets and evaluation sets are deliverables.
Bring us the language models job you keep postponing.
Twenty minutes is enough to say whether we can take it.
A pilot starts within 5 working days of agreed scope · Nothing upfront · No seat licences