Foundation platform

Orchestration for models, tools, data, and people.

A configurable graph executor for private, end-to-end ML, AI, and GenAI workflows—from data ingestion and local inference to human review and operational output.

Model agnostic by designCombine language models, traditional ML, optimization, transformations, and human scoring within one governed execution model.

A graph, not a fixed pipeline.

Foundation represents work as nodes and dataflow as edges. Nodes can run data input and transformation, local LLM inference and training, predictive models, optimization solvers, human scoring, chat, or voice streaming. Edges can carry domain-specific structures shaped by medical, legal, financial, government, or enterprise ontologies.

01Ingest and transform
02Route to the right models
03Verify with tools and people
04Return grounded output

Built for non-linear execution.

Real AI workflows branch, stream, wait, retry, and converge. Foundation supports asynchronous execution, spray/gather fan-out, partial results, streaming, and choice routing so workflows can remain responsive and resilient.

Traditional ML and agents, together.

Agentic AI is effective at interpretation and coordination. Traditional ML is often better for forecasts, anomaly detection, ranking, and classification. Optimization is required when recommendations must satisfy constraints. Foundation allows each to be used as a governed tool inside the same workflow.

Lifecycle management inside your environment.

The platform is designed for secure training, model registries, controlled updates, low-latency inference, evaluation, access controls, and audit trails. It can operate in a private cloud, on-premises, or in fully air-gapped environments.