# Markov

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

Markov (Markov Studios) is a San Francisco-based Y Combinator (Summer 2026) startup building data and environments for training computer-use AI. The company captures how people actually use computers - screen recordings with synchronized mouse and keyboard inputs, annotations, and open datasets across real workflows in tools like Salesforce, Blender, and Photoshop, plus gaming data from Valorant, Minecraft, and GTA. Markov sources high-quality tasks and data to train the next generation of computer-use and reinforcement-learning models, releasing much of its work in public on Hugging Face (150k+ dataset downloads). Founded in 2026 by Dev Mandal (CEO) and Harish Ashok, Markov distributes its datasets through Hugging Face rather than a first-party API; this profile currently tracks no public API surface.

## Kin Score — 5.0 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 5.0).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 0.0 |
| 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).

## Security (1)

- **Markov Domain Security** — TLSv1.3 · HSTS · DMARC

## Tags

Company, Artificial Intelligence, Machine-Learning, Data, Computer Use, Reinforcement Learning, Datasets, Training Data

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