foundation
Your AI Research Partner

An AI agent that does machine learning engineering for you

Foundation is an autonomous AI MLE that helps you research papers, train models, run experiments, manage GPU infrastructure, and ship production AI systems faster.

Foundation Dashboard
Foundation Dashboard showing agent activity, security scans, and vulnerability detection

Your end-to-end ML engineering team

From research to production, Foundation handles the entire ML lifecycle.

AI Research Assistant

Stay on top of the latest research without drowning in papers. Foundation reads, summarizes, and extracts key insights from arXiv, conferences, and publications. Ask questions about methodologies, compare approaches, and get implementation guidance—all from natural language queries.

AI Research Assistant

Autonomous Model Training

Describe what you want to build and let Foundation handle the rest. From data preprocessing and augmentation to architecture selection and hyperparameter tuning, our agent runs training jobs autonomously. Watch experiments unfold in real-time with detailed metrics and automatic checkpointing.

Autonomous Model Training

Intelligent Model Deployment

Go from trained model to production endpoint in minutes. Foundation handles model optimization, quantization, containerization, and deployment. It selects the right serving infrastructure based on your latency and throughput requirements, and monitors performance post-deployment.

1from foundation import Agent, ModelConfig
2from foundation.training import Trainer, Experiment
3from foundation.deploy import Deployer, ServingConfig
4
5# Initialize the Foundation agent
6agent = Agent(project='llm-fine-tuning')
7
8# Configure the model architecture
9config = ModelConfig(
10 base_model='llama-3.2-8b',
11 quantization='int8',
12 lora_rank=64,
13 target_modules=['q_proj', 'v_proj']
14)
15
16# Launch distributed training
17experiment = Experiment(
18 name='customer-support-ft',
19 dataset='s3://data/support-tickets',
20 config=config,
21 gpus=8,
22 provider='aws'
23)
24
25trainer = Trainer(agent)
26model = trainer.train(experiment)
27
28# Automatic hyperparameter optimization
29best_model = trainer.optimize(
30 model,
31 metric='eval_loss',
32 trials=50
33)
34
35# Deploy to production
36deployer = Deployer(agent)
37endpoint = deployer.deploy(
38 best_model,
39 serving=ServingConfig(
40 replicas=3,
41 max_batch_size=32,
42 autoscale=True
43 )
44)
45
46print(f'Model deployed: {endpoint.url}')
1from foundation import Agent, ModelConfig
2from foundation.training import Trainer, Experiment
3from foundation.deploy import Deployer, ServingConfig
4
5# Initialize the Foundation agent
6agent = Agent(project='llm-fine-tuning')
7
8# Configure the model architecture
9config = ModelConfig(
10 base_model='llama-3.2-8b',
11 quantization='int8',
12 lora_rank=64,
13 target_modules=['q_proj', 'v_proj']
14)
15
16# Launch distributed training
17experiment = Experiment(
18 name='customer-support-ft',
19 dataset='s3://data/support-tickets',
20 config=config,
21 gpus=8,
22 provider='aws'
23)
24
25trainer = Trainer(agent)
26model = trainer.train(experiment)
27
28# Automatic hyperparameter optimization
29best_model = trainer.optimize(
30 model,
31 metric='eval_loss',
32 trials=50
33)
34
35# Deploy to production
36deployer = Deployer(agent)
37endpoint = deployer.deploy(
38 best_model,
39 serving=ServingConfig(
40 replicas=3,
41 max_batch_size=32,
42 autoscale=True
43 )
44)
45
46print(f'Model deployed: {endpoint.url}')

MLOps Infrastructure Management

Never worry about GPU availability or infrastructure scaling again. Foundation manages your compute resources across cloud providers, automatically spinning up and down clusters based on workload. Get cost optimization recommendations and unified billing across AWS, GCP, and Azure.

MLOps Infrastructure Management

Scale your ML capabilities

From research exploration to enterprise AI. All plans include a 14-day free trial.

Researcher

$49/month

For individual researchers and hobbyists exploring AI.

  • 100 research queries/month
  • 5 concurrent training jobs
  • Basic experiment tracking
  • Community model hub access
  • Email support
Most Popular

Team

$299/month

For ML teams building production AI systems.

  • Unlimited research queries
  • 25 concurrent training jobs
  • Advanced hyperparameter optimization
  • Model deployment & serving
  • Multi-cloud GPU orchestration
  • Priority support

Enterprise

Custom

For organizations with large-scale AI infrastructure needs.

  • Everything in Team
  • Unlimited training jobs
  • On-premise deployment
  • Custom model fine-tuning
  • SSO & audit logs
  • Dedicated ML engineer support