# Zypl

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

Zypl (zypl.ai) is an AI company building synthetic-data and machine-learning technology for financial services and government. Its core is proprietary zGAN generative synthetic-data technology, delivered through Lucid, a no-code platform for developing and deploying robust scoring models. Products include zScore for credit decisioning, plus fraud detection, collection scoring, and IFRS-9/GAAP-compliant provisioning for credit-loss estimation. Zypl serves banks, financial institutions, and public-sector entities, and operates an R&D lab (zehnlab.ai). It is backed by Prosus Ventures. Zypl publishes no public developer/API surface at this time; this profile carries verified identity and a live domain-security probe.

## Kin Score — 6.3 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 6.3).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 2.6 |
| Developer Ergonomics | 4.8 |
| 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)

- **Zypl Domain Security** — TLSv1.3 · HSTS

## Tags

Company, Artificial Intelligence, Fintech, Machine-Learning, Synthetic Data, Credit Scoring, Financial-Services, Risk

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