# Aiden.ai

**Canonical:** https://apis.io/providers/aidenai/  
**Website:** https://www.aiden.ai/  
**APIs profiled:** 0

Aiden.ai was a London-based marketing-analytics startup, founded in 2016 by Marie Outtier and Pierre-Jean Camillieri, that built what it billed as the first AI-powered marketing analyst: a machine-reading assistant that ingested large volumes of paid-advertising data to surface actionable insights, detect anomalies, predict campaign performance, and proactively act to improve ROI. It raised a $1.6M seed round led by Partech in 2018 and was acquired by Twitter (now X) on 2019-11-21. The company no longer operates as an independent business, ships no public product or developer API, and the aiden.ai domain is now parked and listed for sale (Afternic). This profile is retained as a Partech portfolio lead but is defunct: there is no live developer surface to enrich.

## 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, Applicative Saas, Marketing, Analytics, Artificial Intelligence, Advertising, Marketing Analytics, Acquired, Defunct

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