Expert models
Smaller models trained for one domain. For work inside that domain, they beat general-purpose models.
Made in Europe
for Europe
We are building in Europe for Europe, without any US subprocessor. None of your personal data is subject to the US Cloud Act, and no foreign president can shut down our models.
Why sovereignty mattersPricing
Lower cost
Smaller models need less compute, so we can offer them for less than comparable models.
Per-turn billing
Instead of billing per token we count model turns, which makes cost easier to calculate.
Usage models
Cloud
Use our models through the API, hosted on European infrastructure only.
On premise
For customers in security-critical areas such as defense, we also deploy on your own hardware.
Compliance
No training
We don’t use your prompts or outputs to train our models.
Data security
GDPR is supported by design, and we are working on our ISO certifications.
Legal
EU AI Act
Our models are built with the requirements of the EU AI Act in mind from the start.
Training data
Our training data is sourced in line with EU copyright and text-and-data-mining rules.
One line
to switch.
Our API is compatible with the OpenAI Responses API. If you already use it, testing our models takes a single line change.
See all modelsimport OpenAI from "openai";const client = new OpenAI({ baseURL: "https://platform.plhldr.ai/v1", apiKey: process.env.PLHLDR_API_KEY,});const response = await client.responses.create({ model: "tbd-0-gp", input: "Write a one-sentence bedtime story about a unicorn.",});console.log(response.output_text);
More efficiency instead of bigger models
Instead of trying to compete with frontier models in terms of size, we are working on building more efficient models.
How it worksRetrieval models
Knowledge sits in a retrieval layer rather than in the weights, so it can be updated without retraining.
Distributed MoE
Experts run across several machines, and a request only activates the ones it needs.
Constant context size
The context window stays the same size however long a conversation gets, so context rot and the “lost in the middle” problem don’t occur.
The founders
Denise Harrison
Psychologist (M.Sc.), specialised in learning and memory. For the last few years she has worked with European companies on how they operate. She runs Placeholder AI’s business: operations, strategy and go-to-market.
Nicklas Bocksberger
Previously Head of Data & AI at a European consultancy, where he watched the economics break once pilots hit production volume. He designs and builds the memory and reasoning architecture.
Try our model on your own use case.
Your data stays in Europe the whole time.