Finetune small, open-source language models on your Data Platform datasets or Crowd Platform job answers, then have Crowdee's crowd benchmark the result before you trust it in production.
Looking for a delivered verdict instead of a model you host yourself? See our five crowd products.
Need to build and label the dataset first? See the Data Platform.
Your data. Your model. Benchmarked before you trust it.
Point a finetuning run at a Data Platform dataset version or a Crowd Platform job's accepted answers — no separate export or labeling step.
Choose from a curated catalog of small, permissively-licensed language models from the Ministral, Gemma, Qwen, and Granite families.
Parameter-efficient finetuning keeps runs fast and affordable, at a flat, upfront credit cost per run.
Every finetuned model is versioned and scoped to your organization alone — never shared across tenants.
Send a model's outputs to Crowdee's crowd for an independent transparency score before you rely on it.
Completed models are registered and ready to query through Crowdee's infrastructure — no separate deployment step.
The same finetuning workflow Crowdee uses internally, scoped to your organization's own data.
Choose a crowd-labeled Data Platform dataset version, or a Crowd Platform job with accepted answers.
Select an open-source language model sized and licensed for your use case.
Start a LoRA finetuning run at a flat, upfront credit cost — no metered compute billing.
Submit the model's outputs on a validation set through the same crowd-rating flow used for any AI system's output.
Query your finetuned model from your organization's private, versioned model registry.
A curated set of small, LoRA-friendly open-source language models — grouped by family, each suited to different tasks and licenses.
Mistral AI's edge-optimized, agentic models.
Lightweight models under Google's Gemma Terms of Use.
Multilingual models under an Apache 2.0 license.
IBM's enterprise-ready models under an Apache 2.0 license.
We're expanding the base-model catalog over time — talk to us about your specific use case.
Book a DemoNo separate ML infrastructure to build — training data, finetuning, and benchmarking all run on the same stack as your other Crowdee modules.
Every finetuning run, training dataset, and resulting model is scoped to your organization alone.
Know the credit cost of a finetuning run before you start it — no metered compute billing.
Reuse the same AI Result Evaluation flow to get a human transparency score on your model's outputs.
The AI Platform turns labeled data into your own model. If you want a delivered verdict, your own crowd program, or the datasets themselves, these might fit better.
Prefer a managed, delivered verdict over finetuning and hosting your own model? See Crowdee's agency verification platform.
Crowd PlatformNeed to collect labels or accepted answers with your own crowd program first? See the Crowd Platform.
Data PlatformNeed to build, clean, and label the dataset before finetuning on it? See the Data Platform.
We'll walk through building a training set, finetuning a model, and benchmarking it with the crowd on a real tenant.
