Supervised fine-tuningSecurity and privacy
Turn acquisition and asset data into faster investment decisions.
Train AI on your work and put it to production.
Unified fine-tuning, reinforcement learning and production inference, deployable on DataObserve Cloud or your own GPU cluster.
Your data
You decide what your AI learns and what stays private.
Your intelligence
Knowledge and capabilities that grow with you and belong to you.
Your hardware
A vision of powerful personal AI running on machines you own.
// Leap today withDataObserve
Intelligence you own.
Develop models & agents
// Training
Build agents that get better at your work.
Train language models and agents on your own work. Fine-tuning, reinforcement learning and inference run in one system, on DataObserve Cloud or your GPU cluster.
- Supervised fine-tuningTeach your domain using your data, examples, and expert demonstrations.
- Reinforcement learningTrain agents to reason, use tools, and complete multi-step tasks with your own environments and rewards.
Run your applications
// Production inference
Put the intelligence you build to use.
Turn a trained checkpoint into a model deployment. Bring your own model’s capabilities into customer-facing products, internal tools, and agents.
- From checkpoint to deploymentSave inference weights and publish them under a model name your applications can use.
- Connect through a familiar APIStream responses through Dataobserve’s Python client or an OpenAI-compatible endpoint.
// Control the learning
From experimentto deployment
Your models are a strategic asset. You pick where they run, and the data, weights and compute stay yours.
Open model → your training run
Distributed training from Python
Choose an open model, configure its adapters, and start learning from your data. Dataobserve handles the compute behind each training step.
See your labin action
See what’s happening across your cluster without losing sight of the experiment. The Console brings your team’s training runs, checkpoints, and deployments into one place.
// The platform
Everything a model lab needs.One console.
Training, inference, datasets and evaluations share one API, so a checkpoint never has to leave the system that made it.
Training
SFT and RL from Python.
Inference
Serve checkpoints behind one API.
Datasets
Expert-written, versioned, ready to train.
Evaluations
Graded sets and red-team suites.
Console
Every run, checkpoint and deployment.
Integrations
SDK, API and your own storage.
// Own the stack
Frontier-grade models.Under your control.
Your models are a strategic asset. You pick where they run, and the data, weights and compute stay yours.
- Managed infrastructure
DataObserve Cloud
The complete AI lab, ready to use. Focus on your models while Dataobserve operates the infrastructure.
- Managed training, sampling, and inference
- Download your trained weights and take them with you
- Usage-based, per-token pricing
- On-prem · Your infrastructure
DataObserve Cluster
Turn the GPUs you already own into a frontier AI lab. Deploy the same Dataobserve product in your environment.
- Training and inference on your existing GPU cluster
- Your data and model weights in your environment
- The same API, workflows, and Console
// Security + integrations
Connect your data with confidence.Built for ML engineers.
Integrate with your stack
Call your model through Dataobserve’s Python client or an OpenAI-compatible endpoint. Bring data from your own storage.
Built with security in mind
GDPR compliant and SOC 2 Type II certified, with HIPAA-enabled workflows for health data.
Developer-friendly API + SDK
Train, sample, save and deploy from a few lines of Python. Documented, versioned, scriptable.
Built for scale
One shared GPU fleet for training and inference, or the same product on your own cluster.
// Data + AI Excellence
Train and Deploy Industry‑Leading Generative and Traditional AI.
Every model below is open-weight, optimized for training and inference, and tested for correctness. Tags show parameter count; MoE models list total and active parameters. For on-prem model and hardware requirements
// Generative AI
Fine-tuning data written by the people who do the work.Supervised examples, preference pairs and red-team prompts from domain experts, sent straight into your DataObserve training run.
- Chain-of-thought + in-context learningReasoning written out step by step, for models that get the answer right and the reasoning wrong.
- Data augmentationExpert-reviewed perturbations, for accuracy that drops when inputs change shape.
- DialogTurn-by-turn conversations, for assistants that lose the thread after turn three.
- Full lengthLong-form writing, summaries and reviews that have to pass a subject expert.
- Supervised fine-tuning
- RLHF + DPO
- Safety, evals + red teaming
- 77 sub-tasks in 7 families
- Code, speech, vision + video
- 12 industries
// Traditional AI
Real-world data, recorded for the model you’re training.Speech, video, sensor, image and document data, captured with consent and labelled by people who know the domain.
- Data collectionFour modality groups, from egocentric video to contact-center calls, recorded to your spec.
- Data annotationAnnotators trained on your guidelines, with a second reviewer on every file.
- Training-ready deliveryVersioned, documented datasets loaded straight into your DataObserve project.
- Egocentric + sensor
- Speech + audio
- Image + video
- Text + documents
- Consent-managed capture
// Next steps
Tell us what you’re training.We’ll build a proof of concept.
- Review your current model, data and evals
- Pick the open-weight base model that fits
- Run a first training job on your data
- Compare it against your baseline