# Eigentech

**Canonical:** https://apis.io/providers/eigentech/  
**Website:** https://eigentech.ai  
**APIs profiled:** 0

Eigentech (eigentech.ai) is an artificial intelligence technology company, reported to have been founded around 2016 and headquartered in Hangzhou, China, that applies AI and machine learning to business and industrial domains. Its platform is positioned to help enterprises improve operational efficiency, optimize workflows, and strengthen data-driven decision-making by embedding AI into existing business processes. Eigentech is a portfolio company of Qiming Venture Partners and was added to the API Evangelist network as a lead for the enrichment pipeline. NOTE - as of the latest enrichment pass the company website (https://eigentech.ai) does not resolve and no public developer, documentation, or API surface could be reached, so no API artifacts could be harvested without fabrication.

## 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).

## Tags

Company, Artificial Intelligence, Machine-Learning, Enterprise AI, Automation, China, Qiming Portfolio

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