# TechieMatter

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

TechieMatter is a tech recruitment platform that provides an all-in-one solution to source, filter, assess, interview, and onboard technical talent from around the globe. The platform spans candidate sourcing and resume screening, technical skills assessment and coding evaluations, expert-led technical interviews, and remote-worker onboarding (payroll, compliance, and legal). It serves 900+ companies across 13+ countries, sourcing candidates from 9+ countries across 60+ technical roles including software engineers, data scientists, DevOps, security, product, and hardware roles. No public API or developer platform is documented at this time; this profile is maintained as an API Evangelist network company record.

## Kin Score — 5.5 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 5.5).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 2.4 |
| 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)

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

## Tags

Company, Recruitment, Hiring, Talent, Technical Assessment, Onboarding, HR Tech

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