Oumi
Build and deploy custom AI models from a prompt in hours
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Model distillation
Train a compact fraud detection model using a stronger model’s responses as guidance.
Model evaluation
Evaluate a model on a customer support dataset to uncover common weaknesses and recurring failure patterns in its responses.
Train custom model
Fine-tune a model to automatically classify customer support questions by topic and urgency.
Improve custom model
Analyze specific areas where your model is underperforming and generate targeted datasets for retraining.
Dataset synthesis
Generate 500 question-answer pairs from a product documentation PDF for use in training or evaluation.
Dataset augmentation & expansion
Expand your dataset by generating new samples that match the style and format of your existing examples.
Why custom models
Frontier models (e.g., GPT, Claude, Gemini, Qwen, DeepSeek) are built to be general-purpose. However, this power comes with tradeoffs:- They are often not accurate enough on your specific task
- They are slow and expensive at scale
- You are building on a commodity, versus developing your competitive advantage
- Quality can change without warning and impact your product
- Terms of use may change, impacting model availability for your use case
- Deployment options are constrained, limiting privacy/security control
Read more in the “The Case for Specialized Intelligence”.
How custom models are typically developed
Developing a high-quality custom model is typically an iterative loop:- 1. Evaluate - Start by benchmarking existing models to establish baseline performance. This requires a reliable test set and robust evaluation methodology.
- 2. Create Training Set - Analyze where the baseline model fails, then build or curate training data that targets those gaps.
- 3. Train - Train a new model using the improved training set and with careful selection of and training strategy.
How Oumi does it
Oumi follows the same fundamental development loop but automates all the steps while still giving you full flexibility and control.| Development Stage | Traditional Custom Model Development | Oumi |
|---|---|---|
| Evaluation | Teams manually create datasets for testing and build out the entire evaluation process for measuring performance. | Automatically synthesizes comprehensive test sets and generates LLM-based evaluation judges from a simple natural language task description. |
| Training Set Creation | Engineers manually inspect numerous failure cases to identify where models fail, then manually curate training data to improve quality. | Automatically analyzes model failures and surfaces the failure modes. Then automatically generates targeted training data designed to address failure modes. |
| Training | Effective model training requires carefully selecting the right model family, size, and hyperparameters. | Automatically suggests the correct model family, size, and hyperparameters based on tradeoffs (e.g., quality vs. efficiency) and task type. |
| Iteration Speed | Iteration cycles can take weeks or months due to manual data creation, experimentation, and infrastructure management. | Automates all steps in the model development loop, dramatically accelerating iteration cycles. |
| Deployment | Deployment pipelines must typically be built and managed separately from training workflows. | Provides integrated tooling to easily deploy and run trained models in production environments. |
- Transparent - you can see exactly what actions will be taken
- Reproducible - all actions are recorded as reusable recipes
- Flexible - you can review and modify recipes before they are executed
Who Oumi is designed for
AI has automated many workflows, but building high-quality machine learning models has remained a painfully manual effort. As the platform that automates AI development itself, Oumi is best for teams that want:- Higher quality on critical tasks
- Lower inference cost at scale
- Lower latency for latency-sensitive applications
- Building models to deploy on devices
- Controllable deployment for better privacy and security
- Full transparency into AI model development to ensure auditability in regulated industries and beyond
- Full control and ownership over AI models when they are critical for business success
- Enabling AI researchers and domain experts with limited AI expertise to innovate and build a competitive edge in AI without months of effort