60%
Higher accuracy than Claude Sonnet 4 on clinical note writing. The fine-tuned Gemma 3 27B model also used 10 to 100 times less compute per request.
Build your own intelligence.
Neural Studio turns your data and your team's expertise into AI models your company owns. We handle the whole build, from choosing a base model to running it in production. It all happens inside your cloud, and the weights are yours.
Runs in your AWS or GCP account, or on your own hardware.
General models know a little about everything. Your business runs on knowing a lot about one thing.
That knowledge lives in your data and in the judgment of your best people. Today, most companies send it through someone else's model, one prompt at a time. We help you build it into a model of your own.
Renting a model vs. owning one
Frontier APIs are a good way to start. As your volume grows and the work gets more specialized, the trade-offs change.
| Question | Renting a frontier API | Owning your model |
|---|---|---|
| Who holds the weights | The provider | You do |
| Where your data goes | To their servers, with every request | It stays in your environment |
| Cost as you grow | Rises with every token | A smaller model, on compute you control |
| Quality on your task | Broad, general, and the same for everyone | Tuned to your task and graded by your experts |
| When the model changes | On the provider's release schedule | When you decide to ship a new version |
| What a competitor can copy | The same model and a good prompt | Very little. Your model learned from data only you have. |
What you own when we're done
Every engagement leaves five things with your company. None of them depend on us to keep working.
- The model weights
- A specialized model trained on your data. It lives in your account, and you can run it wherever you like.
- The evaluation suite
- Test cases built from your experts' real decisions. You always know how good the model is, and whether a new version is better.
- The training data
- Clean, versioned datasets drawn from your own systems, ready for the next model you build.
- The deployment
- A fast, low-cost endpoint inside your cloud or on your hardware, wired into your product.
- The improvement loop
- Feedback from production flows back into training, so the model gets better the longer you use it.
From your data to a model in production
We run the full build with your team. You see every experiment, every score, and every decision along the way.
Assess
We map the workflow, the volume, and the data you have. Then we tell you plainly whether a model you own can beat what you use today.
Select a base model
We test open-weight models on your task and pick the smallest one that can do the job well.
Train
We fine-tune on your data, and use reinforcement learning where the task allows it. Training runs on GPUs we source across clouds at the best price available.
Evaluate
Your experts define what a good answer looks like. Every version is scored against that, side by side with the model you use now.
Deploy
We tune the model for speed and cost, then serve it inside your environment behind your own API.
Improve
We watch quality in production and retrain as your data grows. The model keeps getting better the longer it runs.
A platform and a team, working for you
The platform
Neural Studio is the ML workspace we build on. Notebooks, GPU compute, experiment tracking, environments, and deployment all live in one place. Your engineers get full access, so nothing about your model is hidden from you.
The team
Our ML engineers work with yours from the first dataset to production. You don't need to hire a research team to own a model. If you already have one, we help them move faster.
claims-triageNotebooksRunsDeployments
Evaluation loss, last 7 training runs
Selected runOther runs
The results are already public
Small models trained for one task are matching and beating frontier models on that task, for a fraction of the cost. A few published examples:
4 of 8
Tasks where fine-tuned models of 0.6B to 8B parameters beat every frontier model tested. They served a million requests for about $3, against $6,241 for Claude Opus 4.6.
3x
Gartner expects companies to use small, task-specific models at least three times more than general-purpose LLMs by 2027.
These are third-party results, shown for context. Your results depend on your task, which is why every engagement starts with an assessment on your own data.
Built for companies whose expertise is the product
- Legal
- Contract review, clause extraction, matter intake
- Healthcare
- Clinical notes, medical coding, prior authorization
- Insurance
- Claims triage, underwriting intake, policy comparison
- Financial services
- Document extraction, KYC review, research summaries
- Customer experience
- Ticket routing, reply drafting, quality scoring
- Wealth and accounting
- Client reporting, filing review, reconciliation
Your data stays under your control
Deployed in your environment
Training and serving run in your AWS or GCP account, or on your own hardware. We can also run a dedicated, isolated environment for you.
Isolated by design
Every workspace gets its own filesystem, credentials, and access scope. No storage or credentials are shared between customers.
Encrypted before storage
Data is encrypted on the client side before it reaches object storage.
No lock-in
Your model starts from open weights and ends up in your account. If you ever leave, it goes with you.
Start with one workflow.
Pick the task that costs you the most in API spend, or the one you can't send to a third party. We'll assess it with you and show you what a model you own could do.