Quality stops improving.
Prompting only goes so far on your formats, jargon and workflows. Training on your own data raises the ceiling.
Custom AI models, built for your enterprise.
Trained on your agents' work and run where you choose.
Generic AI doesn't know your domain.
Your data has rules.
HIPAA, GDPR and internal policy limit which outside APIs can see it.
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 a model that keeps improving.
You decide whether to continue after every stage.
Assess
A free audit of where your AI spend goes and which tasks repeat at volume.
Prepare
We turn your examples and request logs into training and test sets. Your team approves what goes in.
Build
A three-to-four-week proof of concept finds the smallest model that meets your bar. Then we train the production model.
Run
We serve it behind one API: Ertas-managed, in your cloud or on-premise.
Improve
Your traffic and feedback train each new version, and it passes your evals before it takes over.
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.
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
Build it with us, or build it yourself.
Runs where your data is allowed to go.
- 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, inside your network.
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.
Common questions.
Do we own the models you train for us?
Models run on Ertas by default, trained on your work and shaped by your team. Ownership and handover of the model are available with a minimum engagement commitment. Models your own team trains on Ertas belong to you.
Which tasks are a good fit?
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.
What happens to requests the model can't handle?
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.
How do we switch over?
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.
Does Ertas need access to our cloud or the internet?
Only if you choose Ertas-managed hosting. In your own cloud or on-premise, the model runs inside your environment.
What's the typical engagement length and cost?
A build starts with the audit and data preparation, then 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.
How does this fit into our long-term AI strategy?
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.