From rented AI to AI you own
Stop renting AI for work you do a million times a day.
NeoSmith builds a private AI model for your specific workflow, trained on the work your AI already does. It costs a fraction of what the big AI providers charge to run, and you own it outright. You do not need an ML team.
No code rewrite: your engineers change one setting. If the new model does not beat your current AI on your own test, it does not ship.
Your app
Hybrid Safety Net
Your model
Routine requests
Big AI model
Hard cases
What we commit to
Your test, your bar
A new model goes live only if it beats your current AI on a test you approve before we start.
Nothing for a failed run
No setup fee and no charge for idle time. If training does not beat your current AI, you do not pay for it.
Yours to keep
Download your model in standard formats at any time and run it anywhere.
The rented AI trap
General-purpose AI is priced for every possible task. You pay for that on every request.
Most companies run their AI on a few big models and pay per request. That works for a pilot. At scale, the bill, the delays and the dependence on one vendor all grow with usage.
| Topic | Renting a big AI model | Owning a NeoSmith model |
|---|---|---|
| Cost per request | Premium price on every request, including simple ones | A fraction of the price for routine work. Premium rates only for the hard cases |
| AI agents | One task can trigger dozens of requests, each billed at full price | Each request costs a fraction, so multi-step agents stay affordable |
| Speed | Large models respond more slowly, and an agent waits on every step | Smaller models respond faster |
| Vendor changes | The provider updates or retires models on its own schedule, and results can change overnight | Your model changes only when you choose to retrain it |
| Ownership | Your know-how ends up inside someone else's product | You keep the model and can run it anywhere |
| Data | Every request goes to a third-party service | Runs in our cloud, with a regional partner, or inside your own systems |
How it works
Three steps. No ML team required.
We run the whole process. Your team approves the passing bar and watches the results.
Connect
Your engineers point your existing AI workflow at NeoSmith with one configuration change. Nothing changes for your users: requests keep going to the AI you use today while we learn from them.
Your part One configuration change
Train and prove
We train a dedicated model on the work your AI already does. Before it handles any real request, it is tested against your current AI on a test you approve in advance. If it does not win, it does not ship.
Your part Approve the test and the passing score
Switch and own
The new model starts on a small share of requests and takes more only while it keeps winning. Hard requests still go to a big AI model. The model is yours to download at any time.
Your part Watch the dashboard
Have your own data and ML team? Try the self-serve console (early access) →
The Hybrid Safety Net
Your model first. A big model when it matters.
Every request is checked as it arrives. Routine requests go to your own model. Unusual or complex ones go to a leading general-purpose model, so quality holds on the hard cases. Each retraining makes your model better, and it takes on a larger share of the work over time.
Built with the same process
We built our own coding product this way.
NeoSmith Code is a set of small coding models with a big-model safety net, built with the Model Factory. On a widely used public coding benchmark, it scored 92.2%. Claude Fable 5, the strongest single model we tested, scored 89.8%. NeoSmith Code did it at 3.7x lower cost per task. It works inside Claude Code, Cursor, Codex, Cline and other tools developers already use.
NeoSmith Code is a system of models, compared here with single models. The full method and evidence are in the tech report.
What you build stays yours.
Your model
Download it in standard formats at any time and run it anywhere.
Your data
Your data stays yours.
Your tests
The tests and passing scores you set belong to you.
Runs where your compliance team needs it.
NeoSmith managed cloud
We run training and hosting. Your software calls one secure address.
Your private cloud or data center
The whole system runs inside your own boundary. Your data does not leave.
Regional compute partners
Dedicated capacity in your country or region, for data-residency rules.
Every option uses the same industry-standard interface, so you can move between them without rewriting your software.
Built for high-volume, repeatable work.
A dedicated model pays off where the same kind of task runs thousands or millions of times.
Customer support and voice agents
Fast, on-brand answers that use your own tools and policies. Response time matters most when a customer is waiting.
Document extraction and routing
Read, tag and route invoices, forms, tickets and emails at high volume, at a much lower cost per document than a big general model.
Coding assistants
Follows your company's conventions inside the tools your developers already use. Can run inside your boundary, so code stays in-house.
See NeoSmith Code →Multi-step AI agents
Give each step of an agent its own small model, so long workflows stop multiplying your AI bill.
Priced to the job. Pay for results, not hardware.
You pay per training job and per request served. There is no setup fee and no charge for idle time. A training run that fails to beat your current AI costs you nothing. The price depends on the size of the model, the amount of data and where it runs, so we quote each job.
Questions leaders ask us
Will our model make more mistakes than ChatGPT or Claude?
Before launch, it must match or beat your current AI on a test you approve. After launch, hard requests still go to a big model, and your model's share of traffic grows only while it keeps winning.
How much work is this for our engineering team?
One configuration change to connect. Your code stays the same. We handle data preparation, training, testing and rollout. Your team approves the test and reviews the results.
Do we really own the model? What happens if we leave?
Yes. You can download your model in standard formats at any time and run it anywhere.
How do you keep our data secure and compliant?
You choose where everything runs: our cloud, a regional partner, or entirely inside your own systems, so your data does not leave your boundary. Your data and tests stay yours.
How much data do we need?
None up front on the managed path, because we learn from the work your AI already does. On the self-serve console, 10 to 500 examples, and we generate extra examples to fill gaps.
Can we still use the big AI models?
Yes. The Hybrid Safety Net sends hard requests to a big model automatically.
What if the new model is not better?
It does not ship, and you are not charged for that training run.
How is this different from adding our documents to a prompt?
Documents give the AI facts. Training teaches it behaviour: your format, your tone and how to use your tools. Most products use both.
Bring one workflow. We will tell you whether your own model can beat what you use today.
30 minutes with an engineer. Leave with a clear yes or no, and what it would take.