Governance built in.
Evaluation before every release and request-level audit trails, ready for the EU AI Act and ISO 42001.
Corti Models
Train and serve your own frontier LLM. Own it outright.
Train a leading open model on your data and serve it on sovereign European infrastructure. You keep the model, the IP and the roadmap.
Your data is your differentiation. Send it to a closed frontier lab and you lose control over where it is processed, how it is used and what it costs next year.
Settle data sovereignty before any AI ships, and bring your product into the agentic era without handing over the one thing competitors can't copy.
Take control of your AI roadmap
| Dependent | In control |
|---|---|
| Generic models every competitor can use | A model specialized to your business |
| Proprietary data sent to a closed lab | Proprietary data stays yours |
| Price and uptime set by someone else | Predictable cost on infrastructure you can audit |
| Stuck in provider review | Compliant by design |
Faster, leaner inference on hardware based in Europe.
- 180ms to first token on corti-s1
- Zero prompt retention
- Never trained on your data
Models
- corti-s1
- corti-s1-mini
- corti-s1-embedding
- your-model
client = OpenAI(
base_url="https://ai.eu.corti.app/v1",
api_key="<your-api-key>")
r = client.chat.completions.create(
model="corti-s1", messages=msgs)Train on your data. Keep the IP.
Tune open models on your proprietary data with Corti's training pipeline and research team. The model is yours, served on sovereign infrastructure.
- Pre- and post-training on your proprietary data
- Evaluation against your own benchmarks
- Gains from each new base model carried into your model
your-model-v3In production
Base model
GLM 5.2
Owner
You
Hosted
EU, Corti operated
Next refresh
On new data or base model
Your benchmark
Your model goes live without leaving Europe.
Every trained checkpoint deploys straight onto Corti's sovereign infrastructure, with nothing to export or migrate. It ships behind a stable model ID, passes the same eval suites as Corti's own models, and keeps improving as your data and the base models change.
Deploy from checkpoint
Promote a trained checkpoint to a stable model ID on EU infrastructure.
Gate every release
Each version passes your benchmarks and Corti's eval suites before it serves traffic.
Serve efficiently
Latency-tuned serving on hardware Corti operates, with zero retention.
Retrain in a loop
New data or a stronger base model produces a new version. Your integration stays the same.
A frontier AI lab for critical industries. Our researchers train, evaluate and maintain your models with you.
Diagnose
Set the goal and success criteria.
Build
Train on your proprietary data.
Evaluate
Test against your benchmarks.
Deploy
Serve in Europe or on-premises.
Maintain
Keep it current as data changes.
Your data and models stay under your control.
Sovereign infrastructure.
Inference runs on hardware Corti operates in Europe. Deploy in our cloud or on-premises.
Ten years of AI for regulated industries
We began in healthcare, where a wrong answer is a liability. Every call to Corti Models holds to the same standard.
On some tasks the strongest closed models still lead, and we will tell you where. For most production volume, such as coding, extraction, classification and routing, open models have closed the gap at a fraction of the cost. Most teams move one workload first, measure it against their own evals, and keep a closed model only where it still wins.
Yes. The weights produced from training on your data belong to you, and you can export them and run them elsewhere, within the license of the base model they were built on. Most teams keep them on Corti so the model stays served, evaluated and updated on sovereign infrastructure.
An EU region changes where your data sits. Corti operates the hardware itself, in Europe, so no US cloud sits in the request path.
No. Prompts and completions are never stored, logged or used for training.
Yes. Corti Models runs in Corti's sovereign cloud or on-premises, on the same platform. Raise on-premises early, since it changes the deployment timeline.
Sovereignty is about who operates the infrastructure. Open weights are a static artefact, and no data reaches the original developer.