# Parallel Domain

**Canonical:** https://apis.io/providers/parallel-domain/  
**Website:** https://paralleldomain.com  
**APIs profiled:** 0

Parallel Domain is a synthetic-data and sensor-simulation platform for physical-AI and autonomy developers. It turns real-world drive and flight logs into photorealistic, simulation-ready digital replicas (PD Replica) and generates deterministic camera, lidar, and radar sensor data at scale (PD Sim), plus self-serve synthetic-data generation through its Data Lab service. The platform is driven through a first-party Python SDK (paralleldomain / pd-sdk) and an app console, integrating with existing autonomy stacks and CI/CD for training, testing, and regulatory validation of perception systems across automotive, trucking, drone, robotics, agriculture, and defense.

## Kin Score — 25.0 / 100 (emerging)

Scored 2026-08-17 under rubric 0.11.0. Trend: flat (+0.0 from 25.0).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Operational Transparency | 5.3 |
| Developer Ergonomics | 58.7 |
| Commercial Clarity | 34.2 |

## Agent readiness — 9.0 (agent-aware)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| MCP Server | no |
| Auth Clarity | yes |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Self-serve signup — onboarding: self-serve, pricing: unknown, trial: no (confidence: medium).

## Security (2)

- **Parallel Domain Authentication** — apiKey · 3 schemes
- **Parallel Domain Domain Security** — TLSv1.3 · HSTS · DMARC

## Tags

Company, Ai, Synthetic Data, Simulation, Autonomous Vehicles, Computer Vision, Machine Learning, Robotics, SDK

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/parallel-domain/). Scores are computed from the provider's own public artifacts under a published rubric.