# Detect

**Canonical:** https://apis.io/providers/detect/  
**Website:** https://detecttechnologies.com/  
**APIs profiled:** 0

Detect Technologies is an IIT Madras-incubated industrial AI company founded in 2016 and headquartered in Chennai, India. Its 360-degree Workplace Intelligence Platform, T-Pulse, combines proprietary IoT sensors, drones, robotics and machine vision with industrial AI to deliver automated inspections, predictive maintenance, gas and oil leak detection, structural health monitoring, and real-time workplace safety, operations and security monitoring for enterprises such as Shell, Vedanta, Adani Group and Hindustan Zinc. Backed by Accel and Elevation Capital. The company does not publish a public API or developer program; this profile captures its public identity and domain security posture.

## Kin Score — 10.6 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 10.6).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 7.1 |
| Commercial Clarity | 21.1 |
| Access Clarity | 21.1 |

## 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)

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

## Tags

Company, Cloud Saas, Industrial AI, IoT, Computer-Vision, Workplace Safety, Predictive Maintenance, Drones, Robotics

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