Custom AI, built for your business.
Trained on your data, run where you choose, owned by you.
99%
lower cost than Claude Opus 5
and 96% lower than GPT-5.6 Luna
+18%
better after training
ROUGE-1, same model before and after
23×
faster than Claude Opus 5
and 6× faster than GPT-5.6 Luna
Measured in our study on a summarization benchmark (QMSum). Read the paper
Generic AI doesn't know your domain.
Off-the-shelf models learn from the public web. Your business runs on documents that never left your network.
- 01
Quality stops improving.
Prompting only goes so far on your formats, jargon and workflows. Training on your own data raises the ceiling.
- 02
Your data has rules.
HIPAA, GDPR and internal policy limit which outside APIs can see it.
- 03
Every API call adds to the bill.
Per-token pricing makes each feature a variable cost. A model trained for the task costs less to run at scale.
From first call to production.
You decide whether to continue after every step.
- 01
Use case audit
Free. We find the spend worth cutting.
- 02
Proof of concept
Three to four weeks to the smallest model that meets your bar.
- 03
Build
Trained, tested against your bar, connected to your systems.
- 04
Run and improve
Runs where you choose, retrained as your tasks change.
It starts with a free audit.
Token waste.
Prompts, reasoning and retries you pay for on every API call.
Repeat work.
Tasks that run at volume in the same shape.
Today's baseline.
Cost, speed and quality, measured on your examples.
Your quality bar.
A test set built from your examples.
You get a written scope. If a task isn't ready, it says so.
30 minutes. Technical. No pitch.
Build it with us, or build it yourself.
Built with us
We scope, train and run the model with your team.
- One team from audit to production
- Retrained as your tasks change
- You own the model
Built by your team
Your ML engineers train models on Ertas, with enterprise support.
- Ertas for your whole team
- Frontier GPUs and a custom credit allocation
- Dedicated support
Runs where your data is allowed to go.
The model is yours in every option.
Ertas-managed
We host, monitor and update the model. Your data stays private.
Your cloud
Your own AWS, Azure or Google Cloud account, under the controls you already have.
On-premise
Your own servers, including air-gapped environments with no internet connection.
Designed for teams working under HIPAA, GDPR and the EU AI Act.
Built for teams that run on frontier APIs.
If a frontier model handles the same kind of request thousands of times a day, a model trained on that work can take much of it over.
AI agent companies
Today
Every agent step is a frontier API call. Long system prompts, tool-call loops and retries push the cost per task up with every new customer.
With Ertas
We train a model on the steps your agent repeats most, such as routing, tool choice and extraction. Hard calls stay on frontier models.
Automation agencies
Today
Client workflows in n8n, Make or Zapier call a frontier model at every step, so margins shrink as client volume grows.
With Ertas
A model trained for each client workflow, behind one endpoint you swap into the existing step.
SaaS with AI features
Today
A feature that worked in the demo gets expensive at scale, as per-token bills and response times grow with every active user.
With Ertas
A model trained on your feature's real requests, returning output in the exact format your product expects.
Enterprise operations
Today
Support triage, document extraction and claims handling make thousands of near-identical frontier API calls a day.
With Ertas
One model per high-volume task, tested against your quality bar before it takes any traffic.
Regulated industries
Today
Clinical notes, contracts and financial records can't go to just any outside API, so AI projects stall at compliance review.
With Ertas
We train on de-identified or in-house data and run the model in your cloud or on your own servers, including air-gapped.
Common questions.
Yes. The trained model is yours, whether it runs Ertas-managed, in your cloud, or on-premise, and you can move it later. Models your own team trains on Ertas belong to you too.
Tasks that repeat at volume in a stable format, where you already spend heavily on a frontier model. Open-ended work, or a workflow that still changes every week, usually isn't ready, and the audit will tell you.
With the adaptive model endpoint, they stay on the frontier model you use today. Your custom model takes the share it handles well, and that share grows as it's retrained.
With the adaptive model endpoint, you point the API call at a new model name. Request format, SDK and logging stay as they are, and nothing changes until you approve it.
Only if you choose Ertas-managed hosting. In your own cloud or on-premise, the model runs inside your environment, and air-gapped installations are supported.
A build starts with a proof of concept of three to four weeks. Pricing depends on the task, data volumes, and deployment environment. We scope this on the call.
Each model is an asset trained on your own work. As your data grows and better base models ship, we retrain it and test it against your bar, so it keeps improving.