# Datapad

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

Datapad is an autonomous AI data analyst platform that lets business teams analyze their data by asking questions in natural language. It connects to 50+ data sources — SQL databases, BigQuery, Snowflake, Google Ads, Facebook Ads, Shopify, HubSpot and more — and uses Text2SQL and Python code generation (powered by models such as Claude and ChatGPT) to turn questions into queries, dashboards, and insights, with a Slack bot for conversational analytics. Datapad is a San Francisco company backed by a16z. It is an end-user SaaS product and does not currently publish a public developer REST/GraphQL API, SDKs, or webhook surface; this profile captures its identity, security, and compliance posture.

## Kin Score — 25.1 / 100 (emerging)

Scored 2026-08-19 under rubric 0.12.0. Trend: flat (+0.7 from 24.4).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 18.2 |
| Contract Governance | 18.2 |
| Operational Transparency | 13.2 |
| Developer Ergonomics | 16.7 |
| Commercial Clarity | 60.5 |
| Access Clarity | 60.5 |

## 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 (3)

- **Datapad Domain Security** — TLSv1.3 · HSTS · DMARC
- **Datapad Vulnerability Disclosure** — disclosure policy published
- **Datapad Trust Center** — SOC 2, GDPR

## Tags

Company, Analytics, Artificial Intelligence, Data, Business Intelligence, Natural Language, Dashboards, SaaS

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