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The Archetype platform is organized around a set of core concepts that work together to power Physical AI applications. These components define how data enters the system, how Newton interprets it, and how insights flow back to your applications — synchronously through Direct Query, or at scale through Batch Processing.

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Core Components

Models are the interpretive engine of the platform. Newton is a family of foundation models trained on physical sensor data:
  • Newton Fusion reasons over text, images, and video, and answers in natural language — describe a scene, evaluate an assembly step, or extract structured JSON from a dashboard.
  • Newton Omega encodes windows of time-series sensor readings into fixed-size embedding vectors, the building blocks for classification and anomaly detection on vibration, power, temperature, and other signals.
See Model Performance for how the models are evaluated and how to measure them against your own workloads. Files offer a centralized way to upload and manage data on the Archetype platform. The Files API supports multiple formats — including images (JPEG, PNG), videos (MP4), CSVs, and JSON files. Standard uploads handle files up to 255MB, and the direct-to-cloud workflow supports files up to 250GB. Uploaded files are securely stored within your organization, making them accessible to your team while remaining private to others. Each file is assigned a unique file ID that you can reference throughout the platform — attaching a video to a query, feeding sensor logs to a batch job, or supplying training data for fine-tuning. Batch Processing runs processing jobs against many files at once. You submit a job against a named, versioned pipeline — via the Batch Processing API or the Console’s Batch Manager — and the platform manages the rest: parallel execution across workers, progress tracking at the task level, retries, and lifecycle events. Batch Processing is the counterpart to Direct Query for fleet-scale workloads: analyzing an archive of inspection videos, embedding months of sensor history, or running a fine-tuned model across your whole dataset. Direct Query ties these together for interactive use. A single synchronous POST /query call selects a model, attaches your data — uploaded files or inline data events — and returns the result in the same response. No sessions to manage, no infrastructure to stand up. It’s the fastest way to go from a sensor reading or video clip to an answer, and the path the Developer Quickstart walks through. Customization adapts Newton to your domain. Steer model behavior with n-shot examples — labeled reference data supplied at query time — or train dedicated checkpoints on your own data with the Fine-Tuning Service.

Direct Query API

Run a model against your data in one synchronous call.

Files

Upload and manage data referenced across the platform.

Batch Processing

Run managed jobs against many files in parallel.

Model Performance

How Newton models are evaluated and benchmarked.

Fine-Tuning

Train Newton checkpoints on your own data.

Agent Skills

Teach your coding agent to build on Newton.
As a developer, the fastest way in is the Developer Quickstart: create an API key, run your first Direct Query calls against the Fusion and Omega models, then scale up with Batch Processing and the Files API.
Looking for Lenses and data streams? Those docs now live in the Archive section at the bottom of the navigation.