# Architect Labs

**Canonical:** https://apis.io/providers/architectlabs/  
**Website:** https://architectlabs.com/  
**APIs profiled:** 0

Architect Labs is an AI lab building an AI system that explores, designs, and provably verifies custom silicon for the world's most demanding AI and compute workloads. The company is developing a platform that automates the chip-design and verification workflow, aiming to let AI model developers, robotics startups, and neocloud operators generate and verify custom chips without deep hardware-engineering expertise. Architect Labs raised a $24M seed round led by Kindred Ventures, with participation from investors including Aravind Srinivas and Lukasz Kaiser, and is scheduled to appear at DAC 2026. As of this profile the company publishes a marketing site (about, blog, careers) but no public API, developer documentation, or SDKs.

## Kin Score — 5.5 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 5.5).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 2.4 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| 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

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Tags

Company, Chip Design, Semiconductors, Artificial Intelligence, Electronic Design Automation, Hardware Verification, Silicon, Machine-Learning

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